Early Opening of Concrete Pavements to Traffic

UNIVERSITY OF PITTSBURGH | SWANSON SCHOOL OF ENGINEERING | CIVIL & ENVIRONMENTAL

S U M M A RY R E P O R T

Early Opening of Concrete Pavements to Traffic I R I S E - 2 1- P 2 0 - 0 3 - 01 • D E C E M B E R 2 0 2 1

IRISE Consortium Impactful Resilient Infrastructure Science and Engineering

Technical Report Documentation Page 1. Report No.

2. Government Accession No.

3. Recipient’s Catalog No.

FHWA-PA-2021-014-IRISE WO 02 4. Title and Subtitle

5. Report Date December 2021

Early Opening to Concrete Pavements to Traffic 6. Performing Organization Code 7. Author(s)

8. Performing Organization Report No.

Katelyn Kosar, Lucio Salles, Lev Khazanovich 9. Performing Organization Name and Address Department of Civil and Environmental Engineering University of Pittsburgh Benedum Hall 3700 O'Hara Street | Pittsburgh, PA 15261

IRISE-21-P20-03-01 10. Work Unit No. (TRAIS) 11. Contract or Grant No. IRISEPITT2018

12. Sponsoring Agency Name and Address

13. Type of Report and Period Covered

The Pennsylvania Department of Transportation Bureau of Planning and Research Commonwealth Keystone Building th 400 North Street, 6 Floor Harrisburg, PA 17120-0064

14. Sponsoring Agency Code

15. Supplementary Notes PI contact info: 742 Benedum Hall, 3700 O’Hara Street, Pitsburgh, PA 15261 – lev.k@pitt.edu 16. Abstract Earlier opening of concrete pavement to traffic without detrimental to short- and long-term pavement performance can reduce construction time and improve driver satisfaction. Improving concrete strength estimations in the field and strength gain predictions as well as early age damage modeling are key to optimizing opening to traffic. To evaluate the estimation of concrete strength laboratory and field studies were conducted using maturity and ultrasonic tomography. It was found that both methods were efficient and resulted in reasonable concrete strength estimations for in-situ pavement, but ultrasonic tomography was found to have advantages over the maturity method, such as improved correlation and mobility. A strength gain prediction model was created using both nondestructive methods that combined the benefits of ultrasonic tomography for strength estimation with the predictive ability of the maturity method for future strength development prediction. A mechanistic-based early opening damage analysis procedure incorporating the combined nondestructive testing technique for strength evaluation was developed to predict the effect of early loading on transverse cracking and dowel bar performance for Pennsylvania climatic conditions. A web-based tool was created to facilitate the implementation of this procedure for determining the optimal time when paving projects can be opened to traffic without significant damage. User cost analysis showed that early opening a pavement to traffic would have significant benefits on urban arterial roadways and a minor effect on freeways. 17. Key Words Concrete pavement, concrete strength estimation, maturity method, ultrasonic tomography, nondestructive testing, concrete damage analysis, transverse cracking, dowel bar performance, early opening

18. Distribution Statement No restrictions. This document is available from the National Technical Information Service, Springfield, VA 22161

19. Security Classif. (of this report)

20. Security Classif. (of this page)

21. No. of Pages

Unclassified

109

Form DOT F 1700.7

(8-72)

22. Price

Reproduction of completed page authorized

IRISE The Impactful Resilient Infrastructure Science & Engineering consortium was established in the Department of Civil and Environmental Engineering in the Swanson School of Engineering at the University of Pittsburgh to address the challenges associated with aging transportation infrastructure. IRISE is addressing these challenges with a comprehensive approach that includes knowledge gathering, decision making, material durability and structural repair. It features a collaborative effort among the public agencies that own and operate the infrastructure, the private

Acknowledgements The authors gratefully acknowledge the support of all members of IRISE. We are especially indebted to the Pennsylvania Department of Transportation and the Federal Highway Administration for their sponsorship of the project, and to the advice and assistance provided by the Project Technical Advisory Panel: Mr. Neal Fannin, PennDOT; Ms. Jennifer Albert, Federal Highway Administration; Mr. Patrick Leach, Michael Baker International Mr. Jason Molinero, Allegheny County, and Mr. Charles Niederriter, Golden Triangle Construction.

companies that design and build it and the

Disclaimer

academic community to develop creative

The contents of this report reflect the views

solutions that can be implemented to meet

of the authors who are responsible for the

the needs of its members. To learn more,

facts and the accuracy of the data presented

visit: https://www.engineering.pitt.edu/irise/.

herein. The contents do not necessarily reflect the official views or policies of the US Department of Transportation, the Federal Highway Administration, the Commonwealth of Pennsylvania or any other IRISE member at the time of publication. This report does not constitute a standard, specification or regulation.

Table of Contents 1

INTRODUCTION ........................................................................................................................... 1 1.1 1.2

NONDESTRUCTIVE TESTING ................................................................................................... 4 2.1 2.2

POTENTIAL EARLY OPENING PERFORMANCE ISSUES .................................................................. 2 CRITERIA FOR OPENING TO TRAFFIC ......................................................................................... 2

MATURITY ................................................................................................................................ 5 ULTRASONIC TOMOGRAPHY...................................................................................................... 8

ESTIMATING CONCRETE STRENGTH: FIELD STUDY ...................................................... 10 3.1 DESCRIPTION OF FIELD STUDY PAVING PROJECT ..................................................................... 10 3.1.1 Concrete Mixtures ............................................................................................................. 13 3.1.2 Laboratory and Field Testing ............................................................................................ 13 3.1.2.1 3.1.2.2 3.1.2.3

Laboratory Strength Testing ................................................................................................................ 13 Maturity Testing ................................................................................................................................... 15 Ultrasonic Testing: shear wave velocity ............................................................................................. 16

3.2 RESULTS AND ANALYSIS FOR HIGH EARLY STRENGTH (HES) CONCRETE ................................ 19 3.2.1 HES Compressive Strength Development ........................................................................... 22 3.2.2 Effect of Variability on HES results ................................................................................... 23 3.2.3 Effects on Early-Opening Decision for HES....................................................................... 24 3.3 RESULTS AND ANALYSIS FOR LONG-LIFE CONCRETE PAVEMENT (LLCP) ................................ 26 3.3.1 Effect of Variability on LLCP results ................................................................................. 27 3.3.2 Effects on Early-Opening Decision for LLCP .................................................................... 29 4

EVALUATION OF CONCRETE STRENGTH BY COMBINED NONDESTRUCTIVE TESTS 34 4.1 4.2

DETERMINING MATURITY CURVE ........................................................................................... 34 COMBINING MATURITY METHOD AND SHEAR WAVE VELOCITY .............................................. 38

MECHANISTIC-BASED EARLY OPENING DAMAGE ANALYSIS ..................................... 41 5.1 CONCRETE PROPERTY ESTIMATION ......................................................................................... 42 5.1.1 Concrete Strength Prediction with Maturity Method .......................................................... 42 5.1.2 Concrete Strength Prediction with Combined Shear Wave Velocity and Maturity .............. 43 5.1.3 Concrete Modulus of Elasticity estimation ......................................................................... 43 5.1.4 Concrete Properties Variability Predictions ...................................................................... 43 5.2 TRANSVERSE CRACKING PERFORMANCE ................................................................................. 44 5.2.1 Thermal Load Characterization ......................................................................................... 44 5.2.2 Transverse Cracking Reliability......................................................................................... 48 5.3 DOWEL BAR PERFORMANCE ................................................................................................... 50

WEB-TOOL .................................................................................................................................. 52 6.1 EXAMPLE SIMULATIONS ......................................................................................................... 57 6.1.1 Case 1: Changing Location ............................................................................................... 57 6.1.2 Case 2: Changing Construction Month .............................................................................. 58 6.1.3 Case 3: Changing Traffic Pattern ...................................................................................... 58

6.1.4 7

Case 4: Changing PCC Thickness ..................................................................................... 59

ANALYSIS OF WORK-ZONE USER DELAY COSTS ............................................................. 60 7.1 WORK ZONE ANALYSIS TOOLS ............................................................................................... 61 7.2 EFFECT OF WORK ZONES ON ARTERIAL NETWORKS ................................................................ 63 7.2.1 Methodology...................................................................................................................... 63 7.2.2 User Cost Associated with Work Zones .............................................................................. 69

CONCLUSIONS............................................................................................................................ 71

REFERENCES ....................................................................................................................................... 73 APPENDIX A ........................................................................................................................................... 1 APPENDIX B ............................................................................................................................................ 1 APPENDIX C ........................................................................................................................................... 1 APPENDIX D ........................................................................................................................................... 1 APPENDIX E ............................................................................................................................................ 1

List of Figures Figure 1: Example of developed maturity curves (MnDOT Specifications 2461.3.G.6, 2020) ..... 7 Figure 2: Ultrasonic tomography device (MIRA, 2021) ................................................................ 9 Figure 3: Testing site design with construction and instrumentation details. ............................... 11 Figure 4: Construction of HES lane (a, b, and c) and of LLCP lanes (d, e, and f) ....................... 12 Figure 5: Conventional laboratory strength testing (a) compressive strength and (b) third-point loading for flexural strength.......................................................................................................... 14 Figure 6: Thermocouples instrumentation in the (a) field and (b) laboratory .............................. 16 Figure 7: (a) Ultrasonic tomography device, MIRA, and (b) illustration of linear array system for ultrasonic signal emission and reception ...................................................................................... 17 Figure 8: Ultrasonic testing in (a) the field and (b) the laboratory ............................................... 18 Figure 9: (a) Slab and laboratory maturity over time and (b) slab shear wave velocity and over time ............................................................................................................................................... 20 Figure 10: Development of concrete pavement subsurface and shear wave velocity over the first 27 hours after construction............................................................................................................ 21 Figure 11: Relationship between concrete compressive strength and (a) shear wave velocity, and (b) maturity ................................................................................................................................... 22 Figure 12: Shear wave velocity variability in (a) different positions within a single slab and (b) different slabs ................................................................................................................................ 23 Figure 13: Measured cylinder compressive strength (a and b) and beam flexural strength (c and d) versus maturity and shear wave velocity .................................................................................. 26 Figure 14: Shear wave velocity vs maturity for concrete slab and beams .................................... 27 Figure 15: Slab shear wave velocity variation in different positions of the instrumented slab .... 28 Figure 16: Shear wave velocity variation in different slabs ......................................................... 29 Figure 17: Concrete strength estimation for slab C1.1 two days after construction ..................... 30 Figure 18: Slab and modified beam SWV versus maturity .......................................................... 31

Figure 19: Concrete strength gain versus maturity and shear wave velocity ............................... 32 Figure 20: Concrete strength estimation for slab C1.1 two days after construction using the modified shear wave velocity ....................................................................................................... 33 Figure 21: Temperature data for the cylinders (Cyl) and beams. ................................................. 35 Figure 22: Example maturity - strength curves............................................................................. 38 Figure 23: Example shear wave velocity- strength curves. .......................................................... 39 Figure 24: Accounting for temperature loading in the proposed procedure ................................. 46 Figure 25: Traffic, location, and pavement structure settings for web-based damage analysis tool. ....................................................................................................................................................... 53 Figure 26: Settings for strength predictions using the maturity method. ...................................... 53 Figure 27: Settings for strength predictions using the shear wave velocity ................................. 54 Figure 28: Advanced settings for the damage analysis model. ..................................................... 54 Figure 29: Results provided by the web-tool. ............................................................................... 56 Figure 30: The grouping of PennDOT regional offices with similar climate conditions. (PennDOT, 2021) .......................................................................................................................... 58 Figure 31: The Sources of Congestion National Summary - FHWA (https://ops.fhwa.dot.gov/aboutus/opstory.htm) ........................................................................... 61 Figure 32: Downtown Pittsburgh VISSIM model for PM peak hour ........................................... 64 Figure 33: Street chosen for closure (7th Street from Fort Duquesne to Penn Ave) .................... 65 Figure 34: Hourly Volume Percentage Changes – PennDOT ...................................................... 66 Figure 35: Total User Cost of Delay for Downtown Pittsburgh ................................................... 70

List of Tables Table 1: Strength criteria from PennDOT 408 (2021) .................................................................... 3 Table 2: Mixture design details..................................................................................................... 13 Table 3: Average and standard deviation for compressive strength testing in psi........................ 15 Table 4: Average and standard deviation for flexural strength testing in psi ............................... 15 Table 5: Hourly predicted compressive strength based on maturity and shear wave velocity ..... 25 Table 6: Example set of strength data collected from Golden Triangle Construction Company. 36 Table 7: Frequency distribution probability of a given combination of B and C ......................... 48 Table 8: Example cases varying location, construction month, traffic pattern, and PCC thickness. ....................................................................................................................................................... 59 Table 9: Adjustment of Total Delay Caused by Work Zone ........................................................ 67 Table 10: Occupancy rates ............................................................................................................ 68 Table 11: Cost of Delay Caused by Work-Zone for Pittsburgh Downtown Network .................. 69

1 Introduction In the current paving industry, finding ways to save time and money in pavement construction is a popular topic. Often research uses alternative materials or additives to reduce time of construction. However, this changes concrete properties and is regularly more expensive. Researching methods to shorten construction times for both conventional and high early strength concrete can save time and money. Early opening to traffic requires minor modifications to project planning to save time which can be accumulate to a significant decrease in roadway closure time. One aspect of construction that is viable for shortening is the time a pavement cures before traffic is allowed on the new pavement. Opening a pavement to traffic at the earliest time allowable can reduce construction time and improve driver satisfaction. The Minnesota Department of Transportation (MnDOT) has recently conducted a study to evaluate the long-term effects of early loading (Khazanovich et al., 2021). This study loaded a concrete pavement at two different axle weights at stepped increments before strength criteria was reached. The pavement was then monitored regularly for four years while being exposed to 10,000 Equivalent Single Axle Load (ESALs) per year. Several aspects of pavement performance were monitored using destructive and nondestructive testing as well as embedded sensors. However, no identifiable damage could be attributed to early loading. This indicates that a pavement can be loaded significantly before the current strength criteria. To determine the earliest strength allowable to open to traffic without affecting long-term performance, knowledge gathered from a MnDOT field testing and modeling report was used to highlight variables that have a significant effect on stresses and strength gain within the concrete. This was developed into a mechanistic-based early opening damage analysis model that incorporates the effects of strength gain rate, traffic volume, climate, load characteristics, and pavement structure parameters. This predictive performance reliability model was used to help improve the decision risk analysis on the timing to open a pavement to traffic. The current project aims to adapt the developed damage analysis model to Pennsylvania conditions. The maturity – shear wave velocity relationship to be developed, alongside the damage analysis model, will be used to create a comprehensive web tool for Pennsylvania contractors to better determine opening times. The objectives of this study are to:  

Predict strength gain using popular nondestructive testing techniques. Recommend optimized early opening time for Pennsylvania pavement construction.

1.1

Potential Early Opening Performance Issues

Determining the opening time for a concrete pavement is a critical decision. There are repercussions from both opening too late or too early. Construction is an expensive task making time is a precious commodity on a construction site. Proper scheduling is necessary to shorten construction time as much as possible. Longer construction time not only affects the contractor and funding government agency. User cost is the time lost to drivers due to congestion or detours caused by construction (Uddin and George, 1993). Vehicle operating cost and predicted traffic disruption are both used to calculate the total traffic delay cost that affects drivers and is included in the life cycle cost of the project. Opening late is costly, however, opening early can be detrimental to short- and long-term pavement performance. If a pavement has not had the time to reach the strength needed to carry the expected traffic load, performance issues are possible, and chances of failure or damage increase the earlier a pavement is opened. Concrete exposed to loading prematurely is susceptible to internal distresses, lowering the durability of the pavement. This can affect shrinkage and compromise the microstructure of concrete. This damage may not be evident immediately but can cause serious issues long term and shorten the life of the pavement. Cracking and fatigue damage from early opening is a distress that can occur immediately after opening (Olek et al., 2002). Cracking is a common distress that occurs later in a pavement life and often maintenance costs are expected and accounted for when determining the life cycle cost to fix severe cracking and fatigue damage. However, the need to address this issue so early after construction is inconvenient and overall, avoidable. Joint damage is also possible from loading a pavement too early. Early wheel loads can cause excessive bearing stresses on the dowel bars that can damage the concrete around the reinforcement. This is another distress that may not be evident right away but will cause dowel looseness and compromise load transfer efficiency. The key to finding the optimum time to open to traffic is to determine the strength at which the chance of damage to the pavement structure occurring is small enough that the pavement is still deemed reliable to last the original design life.

1.2 Criteria for Opening to Traffic Opening times were originally arbitrary wait times which has since developed into strength criteria based on concrete compressive or flexural strength (FHWA, 1994; Olek et al., 2002). Ideally, this criterion is the minimum strength required to carry the expected traffic loads. Nevertheless, there are many factors that can affect the actual strength needed to carry the load and these factors can easily change between pavement and locations. The potential variability in the minimum opening strength caused a lack in consistent guidelines to traffic opening with criteria varying significantly between states. 2

Traffic opening criteria can be based on age, compressive strength, flexural strength, or some combination. Compressive strength is the most common test for strength in the field, however, the measurement of cracking resistance is indirect. Flexural testing is more representative of the stresses a pavement experiences and is therefore a better indicator of cracking resistance. However, flexural strength testing is more complex when compared to the simplicity of compressive strength testing. Regardless of the strength test chosen, testing needs to be done after 1, 3, 7, and 28 days to properly establish the strength gain for conventional concrete (ASTM C39). Most states have at least an age and compressive strength requirement, although, the age requirement is often secondary so the pavement can be opened whenever the strength criteria is met even if it is before the age criterion. For conventional concrete, the age, compressive strength, and flexural strength criteria ranges between 7 to 14 days, 2600 to 3500 psi, and 400 to 750 psi, respectively. PennDOT 408 requires various compressive or flexural strengths depending on the slab thickness and length (Table 1). However, often with the desire to open a pavement to traffic early, high early strength concrete is used instead of conventional concrete. High early strength concrete has its own set of criteria differing between states as well. For high early strength concrete, the age, compressive strength, and flexural strength criteria ranges between 4 to 24 hours, 1200 to 3500 psi, and 290 to 420 psi, respectively (Zayed, 2018). PennDOT 408 requires a minimum of 1,200 psi compressive strength to open to traffic for high early strength concrete. PennDOT also allows compressive strength to be measured using the standard destructive method or using the non-destructive maturity method. Table 1: Strength criteria from PennDOT 408 (2021)

Slab Thickness, in 6.0 7.0 8.0 9.0 10.0 +

Strength for Opening to Traffic, psi Slab Length < 10 ft Slab Length ≥ 10 ft MR (3rd point MR (3rd point f’c f’c loading) loading) 3000 490 3600 540 2400 370 2700 410 2150 340 2150 340 2000 275 2000 300 2000 250 2000 300

The existing criteria, regardless of the state, do not account for time of construction, design features, early age traffic load conditions, climate conditions, edge support conditions, and other factors that affect early age pavement performance (ACPA, 1994). The current methods also ignore the effect early opening has on the long-term performance. This limits the accuracy of cost-benefit decisions for the life of the pavement when considering when to open to traffic. Another restriction of the current criteria is that there is no separation between traffic levels. The stresses applied by lightweight, or passenger vehicles will be lower than heavy load vehicles. Therefore, smaller vehicles could be allowed on the pavement before the strength

criteria is reached for full traffic allowance without compromising long-term performance. This would limit the short-term traffic disruption caused by the construction.

2 Nondestructive Testing Destructive testing is the most common method of strength testing. Compressive or flexural strength is used as the opening criteria for every state. However, there are significant drawbacks to these methods. Large number of samples are necessary for testing because to establish strength gain, multiple rounds of testing must be performed at specific times along with the curing process. This introduces variability into the test because each sample is created by hand versus the consistent machinery used to pave the in-situ pavement. Another issue is that the separate samples have different environmental and boundary conditions than the pavement. Environmental chambers are used to replicate the average outdoor climate condition for the location and date, however, common fluctuations of climate conditions, such as temperature and moisture, cannot be replicated in a laboratory. There is also variability within the same slab that can be due to small environmental, mixture, or placement differences. The limited number and size of the samples makes it more challenging to consider such effects. The limitations have led to other testing methods being used alongside destructive methods to improve concrete pavement strength gain monitoring. Although destructive testing is still wildly used by Departments of Transportation, nondestructive testing is gaining popularity. Many departments, including Pennsylvania, have begun accepting nondestructive methods as acceptable measurements of compressive strength to be used for strength opening criteria. Nondestructive testing allows the user to inspect, monitor, or test systems without removing materials from the structure (Helal, 2015). The key aspect of this testing method is that it leaves minimal to no impact on the structure while providing material property estimations that account for the environmental factors in-situ pavements experience. Specifically, within concrete pavements, nondestructive testing has been performed for years to measure various concrete properties. There are various test methods that can measure or estimate a variety of concrete properties including moisture content, density, thickness, and elastic modulus (Crawford, 1997). Each nondestructive method involves different technologies with some being imbedded within the pavement, attached to the surface, or a portable machine. Nondestructive testing, despite the method used, can be performed directly on the concrete that will be in service. This allows the test to account for the effect of environmental, boundary, or curing conditions that may not be repeatable in a laboratory setting. In addition, separate samples are unnecessary in the field in comparison to destructive tests like compressive or flexural strength testing which require many samples. Many methods are also able to gather data continuously or are quick enough to be done often so that more data can be obtained. Another advantage for many methods of nondestructive 4

testing is the portability. Multiple locations on a slab can be monitored to account for variability within a single slab, monitor the strength of critical stress locations, or check areas flagged for potential errors. This project is specifically interested in those that can estimate the elastic modulus as it can be related to concrete strength. A few popular methods include maturity, ultrasonic tomography, ground penetrating radar, electromechanical impedance, and sounding methods (Freeseman, 2016). Maturity is a method for concrete strength estimation that involves temperature sensors previously embedded within the concrete and is accepted for use by PennDOT. The other listed involve emitting different types of waves into the concrete and analyzing the returning signal to estimate various concrete material properties or locate damage or internal boundaries. For this project, maturity and ultrasonic tomography were chosen to estimate early age concrete strength to determine improved opening times.

2.1 Maturity Maturity is a well-established, nondestructive method for concrete strength estimation and has been accepted by many Departments of Transportation. This method has been used for many years in research to reliably estimate concrete strength (Crovetti and Khazanovich, 2005; Helal, 2015; Soutsos, 2020; Soutsos, 2021; Freeseman, 2016; Hong, 2020; Olek, 2002; Hsu, 2002; Mancio, 2004; Mynarcik, 2013; Nazarian, 2003; Wilson, 2020) and has an ASTM standard for consistent use (ASTM C1074). The maturity technique is a non-destructive method that is used to evaluate the strength development of cementitious materials using the combined effects of time and temperature (Carino and Lew, 2001). The hydration reactions between cement and water are exothermic. The amount of heat released by the mixture during the hydration process is an indicator of the amount of produced hydration products. Therefore, there is a correlation between the heat generated by the mixture and the developed strength. Maturity testing involves imbedding temperature sensors in the concrete, as it is poured, at specific depths. Temperature data is continuously gathered and is used in a strength-maturity relationship previously calculated in laboratory work. This allows immediate evaluation of concrete strength at any given time. Maturity is commonly used to determine when a concrete pavement reaches the required strength to open to traffic. Furthermore, the strength-maturity relationship allows for future strength gain to be estimated (Crovetti and Khazanovich, 2005; Van Dam et al., 2005). The relation between temperature and strength is unique for each concrete mixture, therefore, before use in the field, pre-construction laboratory work is required. ASTM C1074 is the standard for estimating concrete strength using the maturity method. This method has been proven to provide accurate strength estimations and maturity has been accepted by many Departments of Transportation as an acceptable strength estimation. Minnesota Department of Transportation (MnDOT) has been using maturity method in their pavement procedures with 5

success for some time and have a well-established procedure for creating the strength-maturity curve. The development of a maturity-strength relationship is performed in three broad steps: 1. Determining the maturity-strength curve in a lab or in the field. 2. Estimating the in-situ strength in the field. 3. Verifying the maturity-strength relationship in the field. To use the maturity method, a maturity meter (or temperature sensors) and a data logger are needed along with the apparatuses to test either flexural or compressive strength. Calculating maturity in Celsius is preferred over Fahrenheit for easier computation. In the laboratory, the concrete strength test method can be either flexural or compressive strength. If flexural is chosen, 15 beam specimens are needed for testing with at least two containing temperature sensors. If compressive is chosen, 17 cylinder specimens are needed, again with two containing temperature sensors. MnDOT suggests different testing ages depending on the type of concrete used. For example, normal strength concrete is tested at 1, 2, 3, 7, and 28 days. Three specimens are tested at each age with the specimens containing the temperature sensors being tested on the last day. The Nurse-Saul method, described in the ASTM C1074 and MnDOT procedures, is commonly used to develop the maturity-strength curve by computing the concrete maturity using laboratory temperature data at each testing age using Equation (1). This equation calculates the time-temperature factor (TTF) which will be related to the measured concrete strength at that particular TTF value. TTF is also known as the maturity index (M(t)). (1)

∑

where: TTF is the temperature-time factor at age t, degree-days or degree-hours is a time interval, days or hours is the mean concrete temperature during the time interval

, °C

is the datum temperature set to be equal to 0 °C in this study, An example of the developed maturity-strength curve can be seen in Figure 1. Note the points represent the measured strength and the respective maturities indices. The complete maturity curve is then developed using these points.

Figure 1: Example of developed maturity curves (MnDOT Specifications 2461.3.G.6, 2020) The primary benefit of maturity method is the continuous strength estimations which can be used to better time critical construction scheduling such as joint cutting, form removal, and opening to traffic. Another major advantage is the predictive ability of the strength-maturity relationship which can be used to improve construction scheduling and guide future activities. Maturity also removes the variability destructive testing has from different environmental and curing conditions in the field as compared to in a laboratory. However, there are also limitations to this method. If the concrete mixture in the field is changed, which is possible under the contractors’ discretion, the strength-maturity relationship is no longer valid, and the project must return to destructive testing for strength values. Maturity is also unable to evaluate the effect of curing on moisture retention or identify errors in placement consolidation or curing (Freeseman et al, 2016). Set temperature sensor locations are another limitation because the area chosen may not be representative of the entire pavement, or it may 7

miss discontinuities elsewhere in the slab. Despite the increased prerequisites and other potential limitations, maturity is a common choice to reliably monitor strength gain in concrete.

2.2 Ultrasonic Tomography Ultrasonic tomography is an emerging technology that directly evaluates the condition of concrete using sound waves. While it is not as well-known as the maturity method, ultrasonic tomography has proven to estimated concrete strength more reliably and is used for many more applications. Ultrasonic tomography is unaffected by moisture or constraint conditions and therefore has a lower variability compared to other nondestructive tests (Cho et al., 2007; An et al., 2009). Ultrasonic tomography involves a portable device that is used externally on the concrete surface. The ultrasonic tomography device uses dry point transducers that emit and receive shear waves as they travel through the pavement. This high frequency shear wave, greater than 20,000 Hz, travels through the concrete and then returns to the device where the shear wave velocity is recorded and a 3D reconstruction of the pavement directly below the scan is created (Hoegh 2011; Haza 2013). Each scan only takes a few seconds. Data is internally recorded automatically or can be manually recorded. The shear wave velocity can then be related to concrete properties, including elastic modulus, Poisson’s ratio, and density. (Cho et al., 2007). This method has been used for a wide variety of testing. Quality control tests are common tests performed using shear waves. The 3D reconstruction allows the user to check pavement thickness, dowel bar location, or locate damage or delamination. This variety of uses is an appealing aspect in pavement construction. Hoegh et al. (2011) used linear-array ultrasonic tomography to determine slab thickness, reinforcement location, and internal distresses. Hoegh et al (2013) used ultrasonic tomography to classify joint damage and found this method to be more accurate that traditional methods. Vancura et al. (2013) used ultrasonic tomography to check pavement thickness in multiple locations over a project with accuracy. Choi et al. (2016) used ultrasonic shear-wave tomography to identify cracks and delamination in pavements. Salles et al. (2019) used ultrasonic tomography to identify incipient cracks in concrete slabs. Additional testing includes determining the extent of damage, boundaries of damaged concrete, and delamination between layers for improved maintenance (Hoegh et al., 2011; Choi 2016). This study will focus on the ability of ultrasonic tomography to measure material parameters, specifically, elastic modulus (Cho et al., 2007).

Figure 2: Ultrasonic tomography device (MIRA, 2021) Shear wave velocity is closely related to the elastic modulus of concrete and can therefore be related to concrete strength (Cho et al., 2007). The aggregate source must be consistent through calibration and monitoring for accurate relations. To determine the shear wav velocity strength curve, there must be flexural testing alongside. In a laboratory, the beam specimens are measured on all four sides and then the average shear wave velocity is adopted as the representative shear wave velocity for that testing age. The relationship can then be developed between the average shear wave velocity and the average concrete strength. The use of the shear wave velocity method for estimating the in-place concrete strength has the following advantages over the maturity methods:  

It has been shown to have more accuracy than maturity for in-situ strength estimation. It does not require to pre-installed sensors into the pavement so testing can be conducted at multiple locations that do not have to be pre-defined.

However, the major drawback of this testing method is that it can only provide estimations at the time of scanning. There is no predictive relationship like that of maturity.

3 Estimating Concrete Strength: Field Study The main objective of this chapter was to present and discuss the laboratory and field evaluation of a concrete paving project with focus on the estimation of concrete strength. For this, two non-destructive tests (maturity and ultrasonic tomography) were used and correlated with conventional laboratory strength testing. Tests were performed in laboratory and field using two different concrete mixes: high-early strength concrete (7-hr accelerated mix design) and long-life concrete (conventional mixture). The field study was divided in three main parts:   

Description of the paving project, concrete mixes, and tests performed. Results and discussion for the High-Early Strength Concrete Pavement. Results and discussion for the Long-Life Concrete Pavement.

Strength criteria for opening to traffic lacks a federal guideline creating a wide variety of requirements at the state level. These criteria lack quantifiable justifications for the chosen values and are designed to be a conservative, generalized value that can be used across an entire state. While the simplicity is convenient, strength gain relies on many factors that can vary significantly between projects even in the same region. Including these factors in decision making can decrease the construction time by trimming unnecessary curing time and allowing traffic at the earliest ability. To measure concrete strength in the field and, in the end, predict strength gain, two popular nondestructive methods will be considered in this study: maturity and ultrasonic tomography. Nondestructive testing is an alternative to destructive testing used to reliably estimate concrete strength. Maturity and ultrasonic tomography are two common methods that both correlate to strength well, although each have their own limitations. A maturity – shear wave velocity relationship that relates these two methods will be developed in this project allowing the benefits of both methods to be combined to optimize concrete strength measurement.

3.1 Description of Field Study Paving Project The testing site (parking lot), shown in Figure 3, consists of multiple lanes of 12 × 12 ft dowelled Jointed Plain Concrete Pavement (JPCP). Construction began at 7:00 A.M. on July 13, 2020. Figure 4 shows photos from construction. The project was an 8-inch-thick concrete pavement using high-early strength (HES) mixture for the first lane and Long-Life Concrete Pavement (LLCP) mixture for the remaining six lanes. On the first day, four lanes were paved. Due to the shape of the first lane (bottom to top in Figure 3), the HES lane was constructed using fixed forms. A slipform paver was used for the three LLCP lanes. A sprayed compound was used on each lane as the curing method. Joints were sawed approximately six to seven hours after paving. 10

To perform maturity measurements, thermocouples were installed at two locations shown in Figure 3: one at the slab center (TC) and another near the lane edge (TE). In addition to maturity data, concrete shear wave velocity was measured at several locations on the slab surface as shown in Figure 3: slab edge (TE), center (TC), corner right north (CRN), corner right south (CRS), corner left north (CLN), and corner left south (CLS).

Long-Life Conventional PCC (Slipform)

C1.1 CLN

C3 13:40 p.m.

CRN

CLS

CRS

12:00 p.m.

C1.1

C1.2

CLN

CLS

10:20 a.m.

HES1 HES2 HES3

Facility

Thermocouples

C1.3

CRN

CRS

07:35 a.m.

HES1 High Early Strength PCC (HES) (Fixed-Form)

Figure 3: Testing site design with construction and instrumentation details.

(a) HES

(b) HES

(d) LLCP

(e) LLCP

(f) LLCP

Figure 4: Construction of HES lane (a, b, and c) and of LLCP lanes (d, e, and f)

3.1.1

Concrete Mixtures

A summary of the mixtures design is provided in Table 2. The HES mixture was designed to meet standards provided by Pennsylvania Department of Transportation (PennDOT) for a 7-hr accelerated mix design whereas the conventional concrete mix was designed to meet PennDOT requirements for LLCP. Both mixes used Type I/II Portland cement with Type F fly ash. The coarse aggregates included #57 limestone and #8 sandstone, and the fine aggregate was concrete sand. Table 2: Mixture design details

Cement (lbs) Pozzolan 1 (lbs) Total Cementitious (lbs) Coarse Aggregate 1 (lbs) Coarse aggregate 2 (lbs) Total Coarse Aggregate (lbs) Fine Aggregate (lbs) Total Water (lbs) W/C Ratio Unit Weight (lbs/cu.ft) 3.1.2

High Early Strength Mix 600 150 750 1309 396

Long-Life Conventional Paving Mix 477 134 611 1357 410

1705

1767

1158 236 0.315 142.93

1161 249 0.408 141.03

Laboratory and Field Testing

Three types of testing were conducted for this field study: conventional laboratory strength testing, maturity, and ultrasonic tomography. Laboratory testing was performed on cylinder and beam specimens to determine concrete strength in different ages. Maturity and ultrasonic data were collected on both the laboratory specimens and on the slabs in the field. 3.1.2.1 Laboratory Strength Testing Compressive and flexural strength testing were performed (Figure 5). Cylinder and beam specimens were cast from concrete collected during the construction of slabs HES1 (HES) and C1.1 (LLCP). Standards ASTM C39, for compressive strength using 4 x 8-inch cylinders, and ASTM C78, for flexural strength using third-point loading, were applied (ASTM C39, 2015; ASTM C78, 2008). For the HES mix, compressive strength was estimated at 3, 5, 7, and 24 hours post construction. HES beams were only tested at 7 hours to determine the flexural strength at the suggested opening time. For the LLCP mix, both flexural and compressive

strength were computed from day 1 to day 28 after construction. Strength results are presented in Table 3 for compressive (f'c) and in Table 4 for flexural (MOR) strength. PennDOT requires a conventional concrete mixture to reach a compressive strength of 3000 psi in 72 hours and 3750 psi in 28 days (Pub 408, 2021). LLCP mix was able to reach 3000 psi within the first 24 hours and far exceeded the 28-day requirement at 6219 psi. PennDOT also requires a HES mixture to reach 1200 psi in less than 7 hours (Zayed, 2018). The HES mix also met this requirement with a 7-hour strength of 2484 psi. Results show that both mixtures reach an average compressive strength of 3000 psi within the first 24 hours after construction indicating that both mixtures have a similar performance for this particular case study. PennDOT does not have a minimum flexural strength to open to traffic. Several other states have flexural strength requirements for conventional concrete with opening strengths ranging from 400 to 750 psi (Ohio DOT Standards, 2019; TxDOT Standards, 2014; MnDOT Standards, 2013; NJDOT Standards, 2015). Within 7 days, the LLCP mix flexural strength was well over any state requirements at 819.3 psi. Five states that have HES specifications with flexural strength (California, Michigan, New Jersey, Ohio, and Texas) recommend opening strength ranging from 290 to 490 psi (Zayed, 2018; Ohio DOT Standards, 2019; TxDOT Standards, 2014; MnDOT Standards, 2013; NJDOT Standards, 2015). Results showed that at 7 hours, the recommended opening time for the HES mixture, the flexural strength was 360.9 psi, which falls into the suggested range based on other states’ policies.

(a)

(b) HES

Figure 5: Conventional laboratory strength testing (a) compressive strength and (b) third-point loading for flexural strength

Table 3: Average and standard deviation for compressive strength testing in psi Test Time 3 hr 5 hr 7 hr 1 day 3 days 5 days 7 days 14 days 28 days

High Early Strength Mix Avg. f'c SD f'c 125 16 951 43 2484 73 3658 128 -

Long-Life Conventional Mix Avg. f'c SD f'c 3311 145 4329 115 4426 106 5040 217 5237 440 6219 111

Table 4: Average and standard deviation for flexural strength testing in psi Test Time 7 hr 1 day 3 days 5 days 7 days 14 days 28 days

High Early Strength Mix Avg. MOR SD MOR 360.9 12.8 -

Long-Life Conventional Mix Avg. MOR SD MOR 597.2 57.9 741.2 18.0 719.0 19.2 819.3 88.6 824.0 86.9 804.4 25.5

3.1.2.2 Maturity Testing Maturity calculations were conducted according to ASTM C1074. Type T thermocouples were used to measure the slab temperature (ASTM 1074, 2013). The thermocouples were installed in two locations on slabs HES1 and C1.1: one at slab center (TC) and another near the slab edge (TE) as illustrated in Figure 3. For each location, four thermocouples were installed: two at 1-inch below surface and the other two at mid-depth (Figure 6a). Temperature data collection began immediately after concrete placement using a CR 3000 datalogger manufactured by Campbell Scientific®. Slab temperature was recorded at 5-minute increments. Temperature sensors were also placed in the cast concrete cylinders (Figure 6b) and beams. This allows for comparison of performance between the slab and laboratory specimens. The temperature data collected was then used to calculate maturity of the concrete in the slabs and in the laboratory specimens using Equation (1).

(a)

(b)

Figure 6: Thermocouples instrumentation in the (a) field and (b) laboratory 3.1.2.3 Ultrasonic Testing: shear wave velocity Shear wave velocity was measured using an ultrasonic tomography device, MIRA. Since MIRA scans do not require previous instrumentation, scans were performed near the maturity sensor placements as well as several other locations on the slab (CLN, CRN, CLS, CRS, TE, and TC) shown in Figure 3. This version of MIRA, provided by the Federal Highway Administration, uses a high frequency shear wave of 50,000 Hz. Transducers located on the bottom of the device emit and receive signals from 48 dry point contact transducers arranged in 12 linear array channels. No surface preparation is necessary before scanning. When the device is placed on the pavement surface, the generated shear waves propagate through the pavement depth and returns to the transducers. Any pavement distresses, changes in material, or other discontinuities will reflect in the shear wave velocity. The reflected shear waves are recorded, stored, and analyzed for every scan in 66 signal time histories.

(a)

(b) Figure 7: (a) Ultrasonic tomography device, MIRA, and (b) illustration of linear array system for ultrasonic signal emission and reception This study performed all measurements in calibration mode, so the calculated shear wave velocity and subsurface reconstruction of the pavement cross section is included in the MIRA report. While other studies have used the subsurface reconstruction to determine slab thickness, dowel bar locations, or internal damage (Hoegh et al., 2011; Hoegh, 2013; Vancura et al., 2013; Choi et al., 2016; Salles et al., 2019; Hoegh et al., 2012), this analysis will only use the shear wave velocity. Ultrasonic testing began on the day of construction when the concrete had set enough to support walking, about 3 hours after placement (Figure 8) for slab HES1 and 5 hours for slab C1.1.

(a)

(b) Figure 8: Ultrasonic testing in (a) the field and (b) the laboratory After setting, measurements were taken every 15 mins for one hour. Then measurements were performed every 30 mins for 3 hours and, finally, every hour for the remainder of the workday. Scans were taken twice at each location (shown in Figure 3) for each time increment. Locations include slab edge (TE), center (TC), corner right north (CRN), corner right south (CRS), corner left north (CLN), and corner left south (CLS). Scans were taken out of dowel bars location to avoid interference. Ultrasound testing was also conducted 1, 2, 3, 5, 7, and 14 days after concrete placement. On these days, in addition to the instrumented slabs, several other slabs were scanned as shown in Figure 1. Shear wave velocity data for every measurement cycle in 18

slabs HES1 and C1.1 are presented in Appendix A of this document. For slab HES1 measurement cycles 1 and 2 were ignored in the analysis because of inconsistencies on the measured shear wave velocity due to initial stage of concrete hardening. In the laboratory, ultrasonic testing was performed in the beams before flexural testing (Figure 8b). Concrete shear wave velocity was recorded three times on each side of the beam samples. Appendix B presents the full data.

3.2 Results and Analysis for High Early Strength (HES) Concrete Figure 9 presents slab shear wave velocity and slab and laboratory maturity over time. For maturity (Figure 9a), both in the field and in laboratory conditions have a linear relationship with time. During the first 24 hours, maturity in laboratory and field are similar meaning that the concrete strength development in both conditions is equivalent. From the second day on, the slab showed higher levels of maturity. The construction was carried out in the Pennsylvania summer with daily average temperatures of 30 C° during and for several days after construction. Since maturity is the cumulative effect of temperature, the field values were larger. Shear wave velocity develops in two well-defined stages (Figure 9b). In the first 24 hours after concrete placement, velocity increases drastically, doubling within 5-hour window measurement. In the remaining days, it increases more gradually becoming almost stable at around 2400 m/s. Figure 10 shows MIRA measurements exemplifying shear wave velocity development over time. At around 3 hours (Figure 10a) after concrete placement, shear wave velocity is extremely low. Concrete is hardening faster at the top of the slab and the ultrasonic signal captures some reflections due to this heterogenous hardening process. Around 2 hours later (Figure 10b), shear wave velocity has increased considerably and the slab/base backwall is now visible but with a weak intensity. After 1 day (Figure 10c), the material completes the majority of its hardening process, shear wave velocity is established, and the slab/base backwall is clearly marked.

12000

Slab L ab

8000 M atur ity (℃-hr s )

M atur ity (℃-hr s)

10000

6000

4000

2000

2500 2000 1500 1000 500 0 3h

1 day 2 days 3 days

Days

0 0

100

150

200

250

Time (hour s)

300

350

(a)

2200 2500

1900

Shear Wave Velocity (m/s)

2500

1600

1300

2200 1900

y = 1268.3ln(x) - 326.55 R² = 0.89

1600 1300 1000 3

Time (h)

1000 0

100

150

200

250

300

350

Time (h)

(b) Figure 9: (a) Slab and laboratory maturity over time and (b) slab shear wave velocity and over time

No slab/base backwall

Weak, undefined backwall

Velocity = 1190 m/s

Velocity = 2010 m/s

(a) 3.5 hours

(b) 5.5 hours

Well-defined backwall

Velocity = 2310 m/s (c) 27 hours Figure 10: Development of concrete pavement subsurface and shear wave velocity over the first 27 hours after construction.

3.2.1

HES Compressive Strength Development

Figure 11 presents the relationship between HES compressive strength gain and nondestructive test results for the first 24 hours after concrete placement. Although shear wave velocity was not computed for the cylinders, slab shear wave velocity can be used in correlation with laboratory strength because maturity of both field and laboratory cylinders is the same during this period as seen in Figure 9a. Compressive strength correlates well with both maturity and shear wave velocity with the latter presenting an almost perfect correlation. However, for the crucial 7-hour time window, maturity has a tendency to underpredict compressive strength in this case. 4000 1 day

Compressive Strength (psi)

3500 3000 7h

2500 2000 1500 5h 1000 500 0 1000

y = 6.2377e0.002751x R² = 1.00

3h 1200

1400

1600

1800

2000

2200

2400

Slab Shear Wave Velocity (m/s)

(a) 4000

Compressive Strength (psi)

3500

1 day

3000 2500

2000

y = 1666.87ln(x) - 7544.51 R² = 0.96

1500 1000

500

0 0

200

400

600

800

1000

L abor ator y M atur ity (℃-hr s )

(b) Figure 11: Relationship between concrete compressive strength and (a) shear wave velocity, and (b) maturity

3.2.2 Effect of Variability on HES results Concrete strength gain is a non-uniform process causing different sections of the same structure to have varying strength levels. The same is true for concrete pavements. The variability especially impacts early opening decisions. Figure 12a shows shear wave velocity variability in the same slab (HES1) taken in six different positions. Results indicate that shear wave velocity variability is substantial in the first hours after construction showing that the slab gains strength at different rates depending on the location. Figure 12b, in a different scale, shows shear wave velocity variability in different slabs. 2500

Shear Wave Velocity (m/s)

2300

14 days

Construction day: 3 to 24 hours after pavement construction

24 h

1 day

2100 5h

1900 1700 Subsequent days

1500 1300 3.5h

1100 900

Measurement Cycles 1

(a) 2550

HES1

HES2

HES3

Shear Wave Velocity (m/s)

2500 2450 2400 2350 2300 2250 2200 3

Days

(b) Figure 12: Shear wave velocity variability in (a) different positions within a single slab and (b) different slabs Due to the limited ultrasonic device access and time schedule, only one HES slab was able to be consistently measured in the first 24 hours. For the remaining two HES slabs monitoring began after 24 hours and was regularly monitored over 14 days. A significant 23

variation of shear wave velocity is observed especially for slab HES3. These three slabs were paved using the same concrete and methodology five minutes apart confirming that concrete strength gain varies substantially in other slabs. For maturity testing to be able to catch both variations within a single slab and between other slabs it would be necessary to use a large number of instruments in several locations prior to construction. There would be also the need for recording and interpreting the temperature data in a short period of time. Both actions are impractical for most pavement construction projects due to their expensive and time-consuming characters. 3.2.3

Effects on Early-Opening Decision for HES

For the HES, the following relationship was found between compressive strength and the nondestructive test results (shear wave velocity and maturity). (

)

(2) (3)

where: is concrete compressive strength, psi is maturity, °C-hr is shear wave velocity, m/s. Maturity and shear wave velocity estimations of compressive strength were compared against laboratory specimen compressive strength 5 to 8 hours after construction. Table 5 provides the average measurements at each location of testing as shown in Figure 3. Values highlighted in red are far below the required 3000 psi compressive strength requirements for Pennsylvania. The color becomes greener as the strength get closer to the standard. At 5 hours after construction, maturity and shear wave velocity based compressive strength are similar to each other and to the measured compressive strength. From six hours on, maturity based compressive strength is conservative. Shear wave velocity based compressive strength shows a significant increase between 5 and 6 hours. For 7- and 8-hour testing, the shear wave velocity based compressive strength is similar to the laboratory specimen measured after 7.85 hours. Using the average of all measurement locations and linearly interpolating to 7.85 hours, maturity predicts 1865 psi and shear wave velocity predicts 2459 psi. When compared to 24

the laboratory specimen compressive strength of 2484 psi, shear wave velocity more accurately estimates compressive strength while maturity remains conservative. Table 5: Hourly predicted compressive strength based on maturity and shear wave velocity

Time (hours)

Slab Maturity Based Compressive Strength (psi) NA

Slab Shear Wave Velocity Based Compressive Strength (psi) 1227

922

966

1222

779

2157

1599

1804

2375

1936

958 2014

951

(5.2 hours)

1880

2014 2248

2343

2098

2960

1896

812

1986

1555

1333

971

1175

Lab Compressive Strengh (psi)

2441

2311 2311

2484

(7.85 hours)

2615

2217

2441

2375

Overall, shear wave velocity shows better predictions in early age HES concrete pavements than maturity. The drastic increase shown in the shear wave velocity based 25

compressive strengths between 5 and 6 hours also shows how sensitive measurements are within the first 24 hour after construction. Moreover, shear wave velocity better captures the variation of compressive strength within the slab. It can be seen that the different locations within the slab have different rates of strength gain. Due to the portability of ultrasonic tomography, more locations are able to be measured to capture the variability within the slab.

3.3 Results and Analysis for Long-Life Concrete Pavement (LLCP) Figure 13 presents LLCP compressive and flexural strength verses maturity and shear wave velocity. As expected, there is a strong correlation between strength and maturity. However, shear wave velocity was found to be better correlated with strength. Indeed, while there was a significant increase in concrete maturity between 3 and 5 days (about 1200 ℃-hrs), both compressive and flexural strength did not show this rise and even dropped slightly. Measured beam wave velocities were similar at days 3 and 5. 6000

5500

7 days

5000 3 days

4500

Compressive Strength (psi)

5500 14 days

5 days

y = 855.83ln(x) - 2,360.74 R² = 0.94

4000 3500

3 days 4000 y = 11.29x - 26,286.23 R² = 0.99

3500 1 day

3000 1800

2800

3800

4800

5800

6800

7800

8800

9800

3000 2600

2650

Cyllinder M atur ity (℃-hr s )

2700

2750

2800

2850

Beam Shear wave velocity (m/s)

(a)

(b)

900

850

850 7 days

7 days

14 days

800 750

3 days

y = 92.03ln(x) + 14.074 R² = 0.86

5 days

700 650 600 550 1000

800

2000

3000

4000

5000

6000

Beam M atur ity (℃-hr s )

(c)

7000

8000

9000

5 days

700

y = 1.37x - 2,983.46 R² = 0.97

650

550 2600

14 days

3 days

750

600

1 day

Flexural Strength (psi)

Flexur al Strength (psi)

14 days

5 days

4500

1 day

800

7 days

5000

1 day

2650

2700

2750

2800

2850

Beam Shear Wave Velocity (m/s)

(d)

Figure 13: Measured cylinder compressive strength (a and b) and beam flexural strength (c and d) versus maturity and shear wave velocity Figure 14 presents computed maturity versus average velocities measured on the top of the thermocouple sensors in the slab and on the beam specimens. As it could be expected, 26

maturity velocities for the same mix are strongly correlated. However, for the same maturity, velocities measured on the beam specimens are higher than velocities measured on the concrete slab. It should be noted that the velocity calculation procedure was developed the MIRA manufacture for larger specimens and the presence of vertical edges near MIRA sensors may affect the results. An alternative velocity calculation procedure was developed for another version of MIRA (Hoegh, 2013). That version of MIRA has a different number of sensors (40) and a larger spacing between the sensor grouped in channel (40 mm vs 30 mm). This alternative procedure was not tested for this version of MIRA and therefore was not considered in this study. Only the manufacture procedure was utilized in this study. 3000

2800

Shear Wave Velocity (m/s)

2600

2400

2200

2000

TE 1800

Beam

1600

1400 100

1000

10000

M atur ity (℃-hr s)

Figure 14: Shear wave velocity vs maturity for concrete slab and beams It should be noted that the velocity calculation procedure was developed by the MIRA manufacturer for larger specimens and the presence of the vertical edges of the beam near the MIRA sensors may affect the results. A previous study concluded that the dimensions of the beam affect the results due to the proximity of the sensors to vertical edges (Hoegh, 2013) Different laboratory and field conditions may also affect measurements and the overall comparison. Figure 14Error! Reference source not found. presents the average shear wave velocities measured on the beams versus maturity measured at two locations of a pavement slab collected during the construction of the Golden Triangle Construction Company parking lot. As would be expected, maturity and velocities for the same mixture are strongly correlated. However, for the same maturity, i.e. similar concrete, velocities measured on the beam specimens are higher than velocities measured on the concrete slab. 3.3.1 Effect of Variability on LLCP results As with the HES slab,

Figure 15 shows variability in the measured shear wave velocity from the LLCP slab (C1.1) taken at five measurement positions (Figure 3). Again, shear wave velocity variation within the slab is substantial in the first hours after constructions. This has implication not only for traffic opening decisions but also for critical post-construction operations like joint saw cutting. As concrete gets older, variability in the measured velocity gets significantly smaller indicating concrete uniformity. Figure 16 shows shear wave velocity variation for different lanes. LLCP Lanes 2 (slab C1.2) and 3 (slab C1.3) were paved approximatively, 2 and 3 hours after LLCP Lane 1 (slabs C1.1, C1.2, and C1.3) with the same paver and concrete mix. It could be expected that 2-3 days after paving concrete properties would be the same in all lanes. Nevertheless, significant variation was observed between lanes. Lane 3 exhibited a significantly greater variability in shear wave velocity two, three days, and seven after paving. After seven and fourteen days from the placement, Lane 3 exhibited significantly lower shear wave velocity than two other lanes. The variability of concrete properties in Lane 1 and 2 was much lower, but still significant. Again, it should be noted that a detailed evaluation of variability of in-place concrete properties using maturity data would require embedding a large number of sensors.

2900 14 days

Shear Wave Velocity (m/s)

2700 1 day

2500 2300

Construction day: 5 to 7 hours after concrete placement

Subsequent days

2100 7h

1900 1700 1500

1300

Measurement Cycles 1

Figure 15: Slab shear wave velocity variation in different positions of the instrumented slab

2750

Shear Wave Velocity (m/s)

2700

2750

Shear Wave Velocity (m/s)

2700

2650

2600

2550

2650

2600

2550

2500

Day 22450

2450

Day 3

Day 5 Measurement Cycles

Day 7

Day 14

Measurement Cycles

Lane 1 Lane 2 Lane Lane 3 1Lane 1 Lane Lane 33 Lane 1 Lane Lane 2 2 Lane Lane 1 2Lane 2 Lane 3 Lane 1 Lane 2 Lane 3 Lane 1 Lane 2 Lane 3

Lane 3 Lane 1 Lane 2 Lane 3 Lane 1 Lane 2 Lane 3 Lane 1 Lane 2 Lane 3 Figure 16: Shear wave velocity variation in different slabs

3.3.2

Effects on Early-Opening Decision for LLCP

The following relationships between maturity and concrete strength as well as between shear wave velocity and concrete strength were developed based on the laboratory data: (

)

(4) (5)

(

)

(6) (7)

where: is concrete compressive strength, psi is maturity, C-hr is shear wave velocity, m/s.

is concrete flexural strength (modulus of rupture), psi Figure 17 shows the concrete compressive and flexural strength computed for two locations close to thermocouples using the maturity-based method and for six locations using the shear wave velocity-based method two days after construction. Values highlighted in red are below the required 3000 psi compressive strength as determined by Pennsylvania or the required 700 psi flexural strength as based on other state policies. The color becomes lighter as the strength get closer to the standard. Once these requirements are surpassed, the value is highlighted green which grows darker as the strength continues to increase. It can be observed that the shear wave velocity-based analysis resulted in a lower strength level compared with the maturity-based method.

2842

4030

3967

3124

(a) Maturity-based compressive strength estimation(psi) NA

2842

694

2616

3124

(b) Shear wave velocity-based compressive strength estimation (psi) 551

701

2842

551

585

551

524

585

(d) Shear wave velocity-based flexural strength estimation (psi)

Figure 17: Concrete strength estimation for slab C1.1 two days after construction As discussed earlier, the procedure for determining shear wave velocity utilized by the on-board MIRA software may not be accurate for measurements made on the concrete beams 30

used for flexural testing. The reflection from the beam sides may affect the calculation and result in higher velocities. The research team is planning to address this limitation and to develop an alternative procedure for velocity calculation using the raw time history data reported by MIRA. Meanwhile, a simple procedure adjusting the velocities of concrete measured on the slab was developed. The relationship was developed on the assumption that the ―true‖ shear wave velocity is measured on the beam and is equal to shear wave velocity measured on the slab if the maturity of concrete in the beam and in the slab are the same. The following correction factor was proposed for the concrete mix used in this study: (8) Figure 18 shows the modified beam velocities and slab measured velocities versus maturity. Similar relationships for beam- and slab-measured velocities and maturity are observed. 2800

Shear Wave Velocity (m/s)

2600

2400

2200

2000

1800

TE M odified Beam SW V

1600

1400 100

1000

10000

M atur ity (℃-hr s)

Figure 18: Slab and modified beam SWV versus maturity The correction factor, Equation (8), was combined with the predictive Equations (5) and (7) resulting in the following model: (9) (10) Figure 19 shows the concrete strength gain predicted by maturity and shear wave velocity. Using the modified SWV results in a comparable strength gain trend to maturity. 31

6000

Compressive Strength (psi)

5000

4000

3000

2000

Maturity

SWV

Adjusted SWV

1000

0 0

Concrete Age (days)

(a) Compressive strength 1000 900 800

Flexural Strength (psi)

700 600 500 400

Maturity

300

SWV

Adjusted SWV

200 100 0 0

Concrete Age (days)

(b) flexural strength Figure 19: Concrete strength gain versus maturity and shear wave velocity Using Equations (9) and (10), the following concrete slab strength was predicted for slab C1.1 (Figure 20).

4194

716

4481

4194

3964

716

751

4481

(a) Modified shear wave velocity-based compressive strength estimation (psi)

716

688

751

(b) Modified shear wave velocity-based flexural strength estimation

Figure 20: Concrete strength estimation for slab C1.1 two days after construction using the modified shear wave velocity It should be noted that the maturity and shear wave velocity models developed for the long-life concrete mixture design are only valid for a pavement age of at least 1-day. Using these models to compute strength in the first few hours after paving might predict unrealistic, negative strength estimations.

4 Evaluation of Concrete Strength by Combined Nondestructive Tests In this study, the benefits of the maturity method and ultrasonic tomography are combined to create an efficient and accurate method of evaluating concrete strength. As discussed above, the shear wave velocity method permits better evaluation of the strength development of concrete, but it cannot be used to predict concrete strength development because the latter is affected by the ambient conditions. On the other hand, the maturity method is less accurate for the strength level determination but can be used for the prediction of strength development. A procedure was developed in this study to utilize the advantages of each method. The combined model also presents more realistic results when estimating strength right after placement.

4.1 Determining Maturity Curve As previously discussed, maturity is a widely accepted method. The process of developing maturity curves is illustrated below using the data gathered in the field study aspect of this project. It must be noted that the coefficients obtained in this study are not the default values. Separate maturity testing MUST be performed for every individual mixture design for accurate correlations. Step 1: Obtaining Temperature Data Maturity is based around the temperature variation as concrete cures; therefore, the initial step is setting up and obtaining temperature data over several days or weeks. The maturity measurements should be conducted according to ASTM C1074. Temperature sensors are imbedded in at least two cylinder and two beam specimens. In this example, three specimens of each were equipped. A data logger is used to gather the temperature data over the entirety of the laboratory testing period with the specimens containing the temperature sensors being testing for strength on the final day. Figure 21 is an example of the gathered temperature data collected at the Golden Triangle Construction Company lab. The data logger creates a chart displaying the temperature every 5 minutes. The figure shows the temperature gain for the first 24 hours for all six equipped specimens.

Figure 21: Temperature data for the cylinders (Cyl) and beams. Step 2: Calculating Maturity The maturity index or TTF is calculated using Equation (1). The best fit exponential curve can be found using Equation (11). In the example, the unique set of coefficients (a m, bm, cm, and dm) found for this mixture design are 1263, 0.61, 6.4E+20, and 0.04, respectively.

(

)

(11)

(12)

where: is the flexural strength (modulus of rupture), psi is the ultimate expected flexural strength, psi is the compressive strength, psi is the ultimate expected compressive strength, psi am, bm, cm, and dm are calibration coefficients.

Step 3: Obtaining Strength Data Strength data must be collected in a laboratory before construction begins. In the example, three cylinder and three beam specimens were tested for compressive strength and flexural strength, respectively, for 1, 3, 5, 7, and 14 days. The obtained average compressive and flexible strength for each age along with the corresponding computed maturity are shown in Table 6. In this study, the earliest strength measurement was taken according to ASTM C1074 after 24 hours from the beginning of concrete curing. However, the long-life concrete mix exhibited a very high 24-hour compressive and flexural strength. This suggests that the pavement might be opened to traffic at an even earlier age. If it is desirable to open to traffic earlier than the current lab strength testing ages, the first strength testing should be conducted at earlier time. Based on the results of this study as well as the results of the study sponsored by the MnDOT, testing after 6, 12, and 18 hours is recommended in addition to testing after 1, 3, 5, and 7 days. Table 6: Example set of strength data collected from Golden Triangle Construction Company.

Age 1 day 3 days 5 days 7 days 14 days

Cylinder Maturity (oC-hrs) 881 1998 3190 4401 8604

Compressive Strength (psi) 3311 4329 4426 5040 5237

Beam Maturity (oC-hrs) 699 1810 3010 4226 8411

Beam Shear Wave Velocity (m/s) 2621.67 2719.72 2720.56 2766.39 2797.78

Modulus of Rupture (psi) 597 741 719 819 824

Step 4: Creating the Maturity – Strength Curves To develop relationships between the flexural strength and maturity, the following steps may be taken: 1. 2.

Estimate ultimate (long-term) flexural strength of concrete, Transform Equation (11) as follows:

(1 3)

Compute

and

independent variable and 4.

Perform a linear regression with

as an

as a dependent variable to determine

coefficient and . Determine the coefficients of the maturity-flexural strength relationship using the following equations:

Applying this approach to the beam maturity and flexural strength data summarized in Table 6 and estimating ultimate flexural strength to be 850 psi leads to the following values for these coefficients: 273.44

The coefficients of the relationship found in Step (3) between the compressive strength and maturity can be obtained in a similar manner. Applying this approach to the data summarized in Table 6 and estimating ultimate compressive strength to be 5,500 psi leads to the following values for these coefficients: 508.09

Figure 22 shows the resulting maturity – flexural strength and maturity – compressive strength curves along with the corresponding lab-measured strengths. A good correspondence between the lab-measured strength is observed. The modified models also present more reasonable strength estimations right after concrete placement than the conventional models as seen in Figure 13.

Figure 22: Example maturity - strength curves.

4.2 Combining Maturity Method and Shear Wave Velocity When only considering shear wave velocity, the following functional form was selected to describe the shear wave velocity - flexural strength relationship for the modified model: (1 4) where: SWV is the shear wave velocity, m/s; and

are coefficients depending on the PCC mix properties. To obtain the coefficients of this relationship,

and

performed with the SWV as the independent variable and

, a linear regression should be as the dependent variable.

Applying this approach to the beam-measured shear wave velocity and flexural strength data summarized in Table 6 and estimating ultimate flexural strength to be 850 psi leads to the following values for these coefficients: 0.00103

Figure 23 shows the relationships obtained between the concrete flexural strength and shear wave velocity measured in the lab in a comparison between the modified and the conventional model which was established in the previous section. It can be observed that both 38

models agree better with the lab-measured flexural strength than the maturity-based model. However, the modified model is more appropriate for estimation of early hour strength as the conventional model would predict unreliable negative strength for concrete with 2000 m/s shear wave velocity. 900 7 days 3 days 700

14 days

5 days

Flexural Strength (psi)

1 day 500

300

100

2000 -100

-300

2100

2200

2300

2400

2500

2600

2700

2800

2900

Beam Shear Wave Velocity (m/s) Modified Model

Beam

Conventional Model

Figure 23: Example shear wave velocity- strength curves. Substituting Equation (8) into Equation (14) leads to the following relationship between the field-measured shear wave velocity and concrete flexural strength for the long-life concrete mix tested: (15) To combine the benefits of maturity and ultrasonic tomography, a procedure was created using maturity predictive abilities to relate shear wave velocity to strength gain. This proposed procedure is outlined below. Step 1: Conduct laboratory testing of concrete compressive and flexural strengths at various ages. Concrete cylinders and beams should be prepared with the same concrete mix design as expected to be used for the pavement project. Compressive strength and flexural strength should be tested using cylinders and beams, respectively, at various ages, say 6 hours, 12 hours, 18 hours, 1 day, 3 days, 7 days, and 14 days. Concrete maturity should be measured for cylinders

and beams. The shear wave velocity should be measured on the beams prior to each flexural strength testing. Step 2: Establish maturity-strength and shear wave velocity-flexural strength relationships. Using the data collected at Step 1, obtain a. coefficients am and bm of the flexural strength – maturity relationship Equation (11), b. coefficients cm and dm of the compressive strength – maturity relationship Equation (12), and c. coefficients as and bs of the flexural strength – shear wave velocity relationship Equation (14) Step 3: Obtain the relationship between the compressive strength and shear wave velocity using the relationships obtain in Step 2:

( (

)

(1 6)

)

Step 4: Select PCC compressive or flexural strength, considered for traffic opening.

, respectively to be

Step 5: Determine the minimum shear laboratory (beam) shear wave velocity corresponding to the concrete flexural or compressive strength considered for traffic opening. This shear wave velocity is obtained by solving Equations (17) and (18).

(17)

((

)

(

)

(18)

Step 6: Determine the field shear wave velocity corresponding to the concrete strength specified for traffic opening using Equation (15). Step 7: Determine the maturity level corresponding to the specified opening to traffic concrete strength by inverting Equations (11) and (12). 40

(

)

(

)

(19)

(20)

Step 8: Predict concrete strength development after traffic opening based on the anticipated change in maturity:

(

)

(21)

(22)

where: is the time from traffic opening in hours; is the mid-depth mean PCC slab temperature depending on the pavement location, concrete slab thickness, and construction month.

5 Mechanistic-Based Early Opening Damage Analysis The National Road Research Alliance (NRRA) recently performed a study to determine the long-term effects of early loading (Khazanovich, 2021). This project began in 2017 as part of MnROAD, a pavement test track used for different research projects in Minnesota. The purpose of that study was to explore the short- and long-term effects of early loading at different times and axle weights. Six testing cells were loaded at varying times: one early enough to leave rutting, one control was not loaded at all, and the final four had stepwise loading at different maturity levels. The cells were split into two lanes of which one was loaded using a 31,000-lb MnDOT snowplow and the other using a ¾-ton pickup truck. Each load application included one forward and one backward pass. The first loading began in Cell 124 at maturity 100°C-hr. There was no visible damage after early loading. 41

All sections were loaded with about 10,000 ESALs per year and were monitored regularly until 2021. Several aspects of the pavement were monitored including maturity, dynamic and static strains, curling, strength, durability, and international roughness index (IRI). The effect on joints was analyzed using an ultrasonic tomography device to locate damage near dowels and using falling weight deflectometer (FWD) testing to determine load transfer efficiency (LTE). Petrographic data was also collected to consider possible surface damage effecting friction. Despite the extensive analysis using pavement performance measures, nondestructive testing, and embedded sensors, no significant, identifiable damage could be associated to early loading. Using the knowledge gained from field testing, a mechanistic-based early opening damage analysis model was developed. The effect of strength gain rate, traffic volume, climate, load characteristics, and pavement structure properties were included in the predictive performance reliability model. A probabilistic approach was applied to evaluate critical stresses for each early applied vehicle pass. The maturity-strength relationship was then used to calculate strength at a random time chosen to open to traffic. If the critical strength is greater than the strength calculated at the same time, then the simulation is counted as a failure. This is done for each vehicle and the total number of failures are used to calculate the probability of failure for the chosen opening time and then the corresponding reliability. This procedure was used for two types of damage: cracking and dowel bar failure. In this study, the NRRA model was generalized to allow for the strength estimation using the shear wave velocity measurements. In addition, it was adapted for Pennsylvania climate conditions. The details of this procedure are provided below.

5.1 Concrete Property Estimation Accurate estimation of concrete flexural strength, compressive strength, and elastic modulus is needed to perform a reliable damage analysis. The user is expected to provide the strength-maturity models so that the concrete maturity at any time after loading can be used in the analysis. The model developed in this study adapts the NRRA approach but adds an option to estimate in-place concrete strength using the shear wave velocity method. Each of these options is described below. 5.1.1 Concrete Strength Prediction with Maturity Method The NRRA model required the user to specify the concrete maturity, , at the time of opening to traffic, i.e., it is expected that maturity of the concrete pavement would be monitored after concrete placement and the pavement would be opened to traffic after concrete maturity would reach the specified value. The PCC compressive and flexural strength between the time of traffic opening and the time concrete reaches the design strength is determined using the predicted maturity and relationships between strength and maturity. 42

In this study, the NRRA approach was adapted with the following modifications: 1. 2.

The functional forms of the maturity-strength relationships were modified as described in Section 0. Instead of asking the user to specify the required maturity, the model asks the user to specify the concrete strength for opening to traffic. The required concrete maturity level is determined using Equation (13). The climate database required to predict the concrete maturity increase after opening to traffic is updated using the PittRigid climate stations.

5.1.2 Concrete Strength Prediction with Combined Shear Wave Velocity and Maturity In this method, the time of opening to traffic is determined when a certain level of shear wave velocity has been reached, but the strength development predictions are made using the maturity method. It is expected the shear wave velocity would be monitored after concrete placement and the pavement would be opened to traffic after the concrete shear wave velocity reaches the specified value, . The user is asked to specify the compressive strength at the time of traffic opening and the required shear wave velocity is determined using Equations (17) and (18). The effective maturity corresponding to this shear wave velocity level is determined using Equations (19) and (20) and the flexural strength and compressive strength are determined using Equations (21) and (22). 5.1.3

Concrete Modulus of Elasticity estimation The concrete modulus of elasticity can be estimated using the ACI equation: (23)

5.1.4

Concrete Properties Variability Predictions

The methods described above will only predict the mean concrete properties at any given time. The spatial variability of the concrete strength at an early age is much higher than for mature concrete and therefore must be accounted for in this analysis (Freeseman et al, 2016). A young concrete will have a high variability (typically about 25%), however as concrete matures, the variability becomes smaller (around 6%). The following model was adapted to evaluate the strength coefficient of variation, (24) 43

where: C, D, E are calibration coefficients with default values of 0.25, 0.001, and 0.075, respectively. To account for the strength spatial variability, the following expression for the damage analysis strength is adopted: (25) where: is the maturity estimated flexural or compressive strength, psi, is the damage analysis flexural or compressive strength, psi,c is a coefficient with a default of 1.

5.2 Transverse Cracking Performance Transverse cracking is a major distress in concrete pavements that can lead to failure. Cracking initiates when the combined stresses from axle load and temperature curling are greater than the concrete strength. Only the bottom-up fatigue damage is considered in this study. The flexural strength and elastic modulus were determined using the procedure above. The longitudinal stresses at the bottom of the slab were computed using an adapted neural network developed under the NCHRP 1-37A project (Khazanovich et al., 2001). The temperature curling stresses were computed using Westergaard’s Solutions (Westergaard, 1926). It is assumed that an early age pavement does not significantly separate from the subgrade and therefore the axle load stress can be computed independently from temperature curling stresses. This also permitted using the dynamic coefficient of subgrade reaction for the moving axle load stress calculation and the static coefficient of subgrade reaction in the curling analysis. The static coefficient of subgrade reaction was assumed to be half the dynamic coefficient of subgrade reaction. The total stress is obtained by summation of the axle-induced and temperature curling stresses. 5.2.1 Thermal Load Characterization Thermal gradients throughout the rigid pavement greatly affect critical stresses in the concrete slab contributing to cracking. Distributions of thermal gradients are required over each month throughout the year (both day and night). The Enhanced Integrated Climatic Model 44

(EICM) module of AASHTO M-E Design software generates the thermal profiles throughout concrete slab thickness for every hour of pavement life. To improve computation efficiency, the AASHTO M-E procedure converts these hourly predictions into monthly distributions of probability of combinations of traffic and temperature (known as the thermal linearization process). The AASHTO M-E linearization process eliminates the need to compute the number of loads as a function of both linear and nonlinear temperature differences by equating stresses due to nonlinear temperature distribution with those due to linear gradients (NCHRP, 2004; Yu et al., 1998). The equivalent temperature distribution concept was introduced by Thomlinson in 1940 and further developed by others (Thomlinson, 1940; Choubane and Tia, 1992). The concept, later generalized for non-uniform slabs (Khazanovich, 1994; Ioannides and Khazanovich, 1998), states that if two slabs have the same plane-view geometry, flexural stiffness, self-weight, boundary conditions, and applied pressure, and rest on the same foundation, then these slabs have the same deflections and bending moment distribution if the throughout-the-thickness temperature distributions satisfy the following condition:

(

∫

)

∫

(

)

(26)

where: a, b are the subscripts denoting two slabs; z is the distance from the neutral axis; T0, is the temperatures at which theses slabs are assumed to be flat; is the coefficient of thermal expansion; E is the modulus of elasticity; h is the slab thickness. The temperature distribution throughout the slab thickness can be split into three components, namely: the part that causes constant strain throughout-the-slab-thickness, the part that causes linear throughout-the-slab-thickness strains, and finally the part that causes nonlinear strains. The first step in the AASHTO M-E linearization process is to compute the monthly PCC stress frequency distribution in the pavement at critical locations for linear temperature 45

difference (ΔTL), non-linear temperature (TNL), and standard axle load. This thermal linearization process significantly reduces the amount of computing required to estimate stresses. Nevertheless, the process is still computationally expensive and needs to be performed for each combination of concrete overlay properties. Moreover, AASHTO M-E linearization process assumes that the stress due to the interaction between nonlinear temperature and traffic is constant for all traffic loads. In this study, an alternative approach was developed as an adaptation of solutions proposed elsewhere (Hiller and Roesler, 2010; Khazanovich and Tompkins, 2017). Figure 24 shows the flowchart of the adapted approach. The standalone EICM software simulation is performed to predict hourly distributions of the temperature throughout the UBOL pavement system. Then, each hourly temperature profile is approximated by a quadratic temperature distribution: (27) where: z is the distance from the mid-depth (inches)

Figure 24: Accounting for temperature load in the proposed procedure The gradients B can be used to compute the temperature difference between the top and bottom overlay surfaces, that are used to compute overlay bending stresses. The procedure for calculation of the overlay critical bending stress due to combined action of linear 46

temperature gradients and axle load is discussed in more detail elsewhere (Khazanovich, 2020). The quadratic terms C are used to compute self-equilibrating top and bottom overlay surface stresses, , using the following equation:

(28)

where: is the overlay coefficient of thermal expansion. is the overlay thickness. is the overlay modulus of elasticity. is the overlay Poisson’s ratio.

The total stress for each combination of the nonlinear temperature distribution throughout the slab thickness and axle load is determined as follows: (29) where: is the bending stress due to linear component of the temperature distribution and axle load. To account for the effect of climate, a database of the mean monthly PCC temperatures and frequency of linear temperature gradients was developed for five Pennsylvania locations. After each hourly temperature profile is approximated using Equation (27), the frequency distribution of linear and quadratic coefficients is created. In this study, the increment of the linear term B was selected to ensure 2°F for the linear temperature difference between the top and bottom PCC surfaces. The frequency distribution for the quadratic term, C, is in increments of 0.1°F/in 2. Table 7 presents an example of the frequency distribution for a 9-in thick concrete pavement located in Pittsburgh, PA. For this example, the probability of the temperature profile with the coefficients B and C equal to 2 and 0.3, respectively, is equal to 0.00898. Therefore, this portion of traffic would be applied with the corresponding equivalent temperature difference and nonlinear temperature stress.

Table 7: Frequency distribution probability of a given combination of B and C

Coefficient B

Coefficient C -0.400 -2.667 -2.444 -2.222 -2.000 -1.778 -1.556 -1.333 -1.111 -0.889 -0.667 -0.444 -0.222 0.000 0.222 0.444 0.667 0.889 1.111 1.333 1.556 1.778 2.000 2.2222 2.4444 2.6667 2.8889 3.1111 3.3333 3.5556 3.7778 4 4.2222 4.4444

0.3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

-0.2 0 0 0 0.00006 0.00006 0.00012 0.00024 0.00037 0.00055 0.00067 0.00043 0.00055 0.00055 0.00018 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

-0.1 0 0.00037 0.00079 0.00202 0.0033 0.00593 0.01002 0.01332 0.01375 0.0102 0.008 0.00672 0.00544 0.00348 0.00098 0.00092 0.00043 0.00049 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0.00018 0.00061 0.00318 0.00733 0.0245 0.04484 0.05682 0.06397 0.05444 0.04099 0.02285 0.01558 0.01246 0.01191 0.00965 0.00703 0.00385 0.00299 0.00147 0.00073 0.00024 0.00006 0 0 0 0 0 0 0 0 0 0

0.1 0 0 0 0 0 0.00055 0.00116 0.0044 0.01167 0.01918 0.0176 0.00965 0.00379 0.00464 0.00916 0.01185 0.01533 0.01656 0.01411 0.00935 0.00672 0.00477 0.00196 0.00177 0.00043 0.00018 0 0 0 0 0 0 0

0.2 0 0 0 0 0 0 0.00018 0.00049 0.0025 0.00403 0.00556 0.00764 0.01191 0.01375 0.01234 0.01381 0.01527 0.01735 0.02004 0.01851 0.01741 0.01112 0.00806 0.00629 0.00452 0.00293 0.00104 0.00037 0.00018 0 0 0 0

0.3 0 0 0 0 0 0 0 0 0 0 0.00006 0.00098 0.00336 0.00446 0.00611 0.00495 0.00849 0.01033 0.01057 0.01136 0.01087 0.00898 0.00861 0.00672 0.00599 0.00544 0.00409 0.0025 0.00086 0.00037 0.00006 0 0

0.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00018 0.00098 0.00275 0.00379 0.00287 0.00373 0.0047 0.00513 0.0044 0.00434 0.00385 0.00354 0.00299 0.0011 0.00086 0.00073 0.00006 0

0.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00006 0.00031 0.00018 0.00055 0.00043 0.00055 0.00031 0.00031 0.00037 0 0 0

0.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

5.2.2 Transverse Cracking Reliability To encompass many possible simulations that may induced transverse cracking, axle weight, traffic wander, and temperature difference are randomly selected. If the stresses induced by axle weight and temperature curling are greater than the flexural strength, then the run counts as a failure. To perform the analysis, the user should select the location and the construction month.

An analysis was performed on a randomly selected time for each vehicle pass to determine the concrete modulus of elasticity and flexural strength using the following procedures: a) Estimate concrete maturity using Equation (1); b) Determine concrete flexural and compressive strength using corresponding strengthmaturity relationships; c) Adjust strengths to account for spatial variability using Equation (25); and d) Determine the concrete modulus of elasticity using Equation (23). The longitudinal stresses at the bottom of the concrete slab caused by axle load are computed using the neural networks adapted from the rapid solutions developed under the NCHRP 1-37A project (Khazanovich, 2001). The temperature curling stresses were computed using Westergaard’s solutions (Westergaard, 1926). Axle weight and traffic wander are randomly selected as well as the temperature difference which is based on the effective temperature frequency analysis. If the combination of axle-induced and temperature curling stresses exceeds the flexural strength, then that run is counted as a failure. The total number of failures is summed for each expected vehicle and is used to compute the probability of failure for each simulation. It is recommended to conduct multiple simulations (between 100-800 simulations) and then average the probability of cracking failure between all simulations. The reliability that cracking will not occur is then calculated using the following equation:

(

∑

)

(30)

where: is the cracking resistance reliability, is the total number of failures for simulation i, is the total number of vehicles in one simulation, is the total number of simulations.

5.3 Dowel Bar Performance The second major distress considered in this model is dowel bar damage. The heavy axle loads in combination with the lower strength from opening early can lead to excessive bearing stresses acting beneath the dowels leading to micro or macro cracking in the concrete around the dowels. This reduces the dowel effectiveness and compromises the long-term performance of the joint. However, dowel-concrete interaction is complex. Rapid solutions implemented by the Mechanical-Empirical Pavement Design Guide (MEPDG) to determine deflections which are needed to compute the bearing stresses. The full procedure to determine deflections for a twolayered pavement can be seen in the MnDOT project report. The final equation for the maximum bearing stress is calculated using the following equation: (31) where: is the maximum bearing stress for the dowels, is the modulus of dowel support is the relative stiffness of a dowel bar embedded in concrete is the joint opening is the modulus of elasticity of the dowel is the moment of inertia of the dowel bar cross section is the dowel-concrete slab connection shear stiffness is the difference between deflections of the loaded and unloaded sides of the joint at distance r from the longitudinal edge The critical dowel bearing stress is computed using the methodology proposed by Crovetti and Khazanovich (2005) relating the deflections at the loaded and unloaded side of the joints above the dowel with the concrete stresses under the dowel. To compute the joint deflections, Neural Networks were developed based on the deflections predicted using ISLAB2000 for concrete pavements with various properties subjected to a single or tandem axle load. The following equation is standard to determine the allowable concrete bearing stress based on concrete compressive strength and dowel bar size.

The dowel bearing stress due to axle load is determined using the standard legal truck having a 12-kip single axle load and two 34-kip tandem axle. The following equation is standard to determine the allowable concrete bearing stress based on concrete compressive strength and dowel bar size.

(32)

where: is the allowable concrete bearing stress d is the dowel diameter The dowel bar reliability is the computed using a similar equation to that used for cracking reliability. The maximum bearing stress (Equation (31)) is compared to the allowable bearing stress (Equation (32)). Again, if the maximum stress is greater than the allowable stress, the simulation is considered a failure and the total number of failures out of all the simulations is used to computed to probability of failure. The reliability that the dowel bars will not fail is calculated in the following equation:

(

∑

)

(33)

where: is the cracking resistance reliability, is the total number of failures for simulation i, is the total number of vehicles in one simulation, is the total number of simulations. Cracking and dowel bar performance reliability must be combined to determine the optimal opening strength for the pavement. The resulting reliabilities allow the user to make an educated decision on when to open to traffic while understanding the risk of damage.

6 Web-tool To implement the mechanistic-based early opening damage analysis and the maturity – shear wave velocity relationship, a web-tool was developed for use in Pennsylvania. The computation models were implemented into a Fortran code while the web-based interface written in PHP and JavaScript permits the user to provide the input information for the analysis and displaces the analysis results. The application can be found at Early Opening Analysis (pittearlyopenpcc.azurewebsites.net). A detailed user guide is presented in Appendix C. To define the traffic and pavement structure for a project, the user must choose location, construction month, traffic pattern, and shoulder type and input the number of trucks per day, PCC thickness, PCC coefficient of thermal expansion, joint spacing, base thickness, base modulus, and dowel diameter (Figure 25). The location options are the different regions in Pennsylvania grouped by climate. Choosing a traffic pattern changes the chart with axle spectrum used to calculate the traffic load or the user can input their own values when they select custom traffic. Another input is the design PCC flexural strength which is the standard opening strength to open to traffic. The nondestructive test used on the project must then be selected between maturity and shear wave velocity. The model will change depending on which is chosen (Figure 26 and Figure 27). Both models require the desired PCC maturity or shear wave velocity at the time of opening. The application then calculates the PCC flexural and compressive strength at the time of opening using the respective models. The coefficients can be changed based on the strength-maturity relationship developed in the laboratory that corresponded to the mixture used. The shear wave velocity model has similar equations to solve for flexural and compressive strength. There is also the additional equation to relate shear wave velocity to maturity so that strength gain can be predicted forward. Advanced settings can be accessed that allow the user to alter simulation number, built-in curl, and dynamic k-value as well as alter the variability characteristics (Figure 28). These settings’ default values are standard but can be altered if the project requires. The final settings are the minimum PCC flexural and compressive strength which are the lowest strength measured in the lab. The program will not recommend any opening strengths lower than this value. This is used to remove extrapolation from early age strength prediction and ensure that the recommended strength has been confirmed in a laboratory setting. It is encouraged to perform laboratory strength testing as early as possible to obtain the lowest minimum strength available.

Figure 25: Traffic, location, and pavement structure settings for web-based damage analysis tool.

Figure 26: Settings for strength predictions using the maturity method.

Figure 27: Settings for strength predictions using the shear wave velocity

Figure 28: Advanced settings for the damage analysis model. After the inputs are added, the user can submit the simulation and the tool will use the mechanistic-based early opening damage analysis to evaluate the project. The tool will output the cracking and dowel bar performance reliability and the ESALs repetitions to design strength. The ESALs repetitions represents the number of single axles that will travel the roadway until the design strength specified by the user is reached. Four graphs are available in the results (

Figure 29). The first two are the cracking and dowel bar performance reliability as the concrete gains flexural and compressive strength. This allows the user to rerun the simulation at a higher strength if the reliability is below the desired level. The third graph is the ESALs to Design Strength which shows the remaining ESALs left to travel the roadway until the design strength is met. The final graph shows the predicted strength gain over time. In the example shown in these figures, the maturity model was used to perform the analysis. The cracking and dowel bar performance were calculated to be 88.7% and 77.5%, respectively. The pavement is expected to receive 997.7 ESALs after it is opened to traffic at the compressive strength of 1000 psi or flexural strength of 346 psi until it reaches the design flexural strength of 650 psi. If the traffic opening is delayed by 6.3 hours, the PCC flexural strength at time of opening would reach 463 psi and the cracking and dowel bar performance reliability would increase to 97.1% and 100%, respectively. The exact same simulation can be performed using shear wave velocity. A compressive strength of 346 psi would correspond to a shear wave velocity of 2336 m/s and 463 psi correlates a velocity of 2486 m/s. This will provide identical simulations and results.

Figure 29: Results provided by the web-tool.

6.1 Example Simulations To demonstrate the effect of varying location, construction month, traffic pattern, and PCC thickness, the following examples were simulated using the web tool. The other settings remain at the values shown in Figure 25. The maturity model will be used for each case with PCC maturity at the time of opening of 304 °C-hr and the calculated PCC flexural strength at the time of opening of 346 psi. If the shear wave velocity were to be used as the evaluation method, the velocity at the time of opening would be 2336 m/s.    

Case 1: Region 4, paved in May, Minor Arterial, 6-inch PCC thickness Case 2: Region 2, paved in October, Minor Arterial, 6-inch PCC thickness Case 3: Region 2, paved in May, Residential, 6-inch PCC thickness Case 4: Region 2, paved in May, Minor Arterial, 9-inch PCC thickness

6.1.1 Case 1: Changing Location For this web tool, Pennsylvania was divided into regions that groups the regional offices of PennDOT with others with similar climates. The only exception is Erie County whose severe climate due to the lakes puts it into its own region. Case 1 investigates the change of location from Region 2 (Districts 1, 10, 11, and 12) to Region 4 (Districts 3 and 4). In this simulation, the cracking and dowel bar performance change only slightly to 89% and 74.6%, respectively. The result that changes the most is the ESAL repetitions to design strength which increases to 1058 ESALs due the differing climates causing a difference in rate of strength gain. A pavement in Region 4 will need more time to reach the design strength when compared to the same pavement in Region 2.

Figure 30: The grouping of PennDOT regional offices with similar climate conditions. (PennDOT, 2021) 6.1.2 Case 2: Changing Construction Month Case 2 shows the effect of changing the construction month, in this case from May to October. On average, October is a colder month than May which can significantly slow the heat of hydration in concrete, reducing the rate of strength gain. In this simulation, cracking and dowel bar performance reliability and ESAL repetitions are 95.7%, 59.8%, and 1419, respectively. There is a significant difference for each of these values when compared to the same pavement constructed in May. The cracking performance increases accounting for the increase in stiffness due to the colder weather while the dowel bar performance reliability decreases and ESAL repetitions increase to account for the slow rate of strength gain. 6.1.3 Case 3: Changing Traffic Pattern Case 3 shows the effect of changing the traffic pattern, in this case from Minor Arterial to Residential. This input controls the amount and size of vehicles traveling on the pavement. A decrease in traffic of 415 ESALs is caused by the user changing the traffic pattern from Minor Arterial to Residential. In this simulation, cracking and dowel bar performance reliability are 99.1% and 99.9%, respectively. The large increase from the original example shows that allowing lighter weight traffic early is beneficial to lower congestion while maintaining pavement performance reliability. Changing the allowable traffic to achieve this reliability is a viable method for reducing the user cost of any road type including interstates and minor arterials.

6.1.4 Case 4: Changing PCC Thickness Case 4 shows the effect of changing the PCC thickness. In this case PCC thickness was increased from 6 to 9 inches. Since the PCC is thicker and therefore stronger, the cracking performance reliability and dowel performance reliability are 100% and 99.7%, respectively. This case also sees a higher number of ESALs (1021) before design strength is reached. Table 8 shows a comparison of each case. Table 8: Example cases varying location, construction month, traffic pattern, and PCC thickness. Location Construction Month Traffic Pattern PCC Thickness Cracking Reliability Dowel Bar Reliability ESALs to design strength

Example Region 2

Case 1 Region 4

Case 2 Region 2

Case 3 Region 2

Case 4 Region 2

May

October

May

Minor Arterial 6 in

Residential 6 in

Minor Arterial 9 in

88.7%

89%

95.7%

99.1%

100%

77.5%

74.6%

59.8%

99.9%

99.7%

997.7

1058

1419

415

1021

7 Analysis of Work-Zone User Delay Costs In the recent years, considering that the National Highway System is essentially complete, the focus of road construction has switched from building new roads to reconstructing and renovating existing infrastructure. Still, most of the existing roadways are beyond their useful design life. Consequently, work zones resulting from the need to address the reconstruction projects bring significant impacts on mobility, safety, and economic productivity of the relevant region and stakeholders. From partial to full road closures, the severity of work zone impacts could vary significantly. Depending on the type of the work zones, these impacts consist of growth in traffic volumes, congestion, increased travel time, and a reduction in safety, reliability, mobility, and economy in the area. These factors heighten the need for finding innovative and efficient ways to assess and improve mobility and safety in the paving industry work zones. This is a potential area for the early opening procedure described in this project to benefit construction practices. Saving a relatively small amount of time on a long, new construction project is not useful because curing time is not a stationary time. The construction can continue to other areas on the construction project. However, small repair projects, especially in urban areas, can benefit greatly from any construction time shortage possible to reduce traffic and congestion. Arterial roadways farther benefit because work zones do not only affect the roadway being repaired, but congestion can spread to the surrounding network of roads. The bus network is also highly affected by arterial work zones and farther affect the transportation efficiency of the city. According to the Federal Highway Administration (FHWA) Office of Operations, congestion derives from several factors which can be split into six root causes, often interacting with one another (FHWA, 2020). Figure 31 depicts an estimation of how much each of these sources contributes to the total congestion. As a matter of fact, not only work zone by itself stands for 10% of the total congestion, but also, it is one of the main reasons for bottlenecks' occurrence (FHWA, 2020). Therefore, transportation agencies across the country are trying to make travel through and around work zones safer and more efficient, especially in terms of travel time and delay.

Sources of Congestion Poor Signal Timing 5%

Special Events/Other 5%

Bad Weather 15%

Bottlenecks 40%

Work Zones 10%

Traffic Incidents 25%

Figure 31: The Sources of Congestion National Summary - FHWA (https://ops.fhwa.dot.gov/aboutus/opstory.htm) Historically, the Pennsylvania Department of Transportation (PennDOT) has utilized the Work Zone Delay Analysis Workbook to analyze work zone impacts. Recently, the Safety and Traffic Operations in partnership with the Bureau of Maintenance and Operations (BOMO) of PennDOT have decided to use a freeway evaluation software FREEVAL (FREeway EVALuation) to determine highway efficiency under work zone conditions. The FREEVAL, which is based on analytical formulas of the 6th edition of the Highway Capability Manual, is a tool for freeway analysis that could be calibrated and used to assess the possible scenarios of work zone activities in those areas. However, not all work zones are located on the freeways or rural and suburban highways. Therefore, to assist in developing practical plans to evaluate and manage the anticipated type, severity, and extent of work zone impacts on travel time and delay, there is a need to identify appropriate analytical tools and methods for different types of work zones in other (e.g., urban) areas.

7.1 Work Zone Analysis Tools Federal agencies paid considerable attention to improving the safety and mobility of work zones, mostly after 1991, when the Intermodal Surface Transportation Efficiency Act (ISTEA) mandated a highway work zone safety program to enhance the safety at highway construction sites. Afterward, multiple legislation, regulations, and initiatives in the past two decades have been declared and adopted. These initiatives aimed at enhancing safety, facilitating the management of the mobility impacts of work zones, implementing a policy for the systematic 61

consideration of work zone impacts on Federal-aid highway projects, and addressing the safety of motorists, pedestrians, and highway construction workers in highway construction work zones (Ye and Al-kaisy, 2010). Many of these studies considered the impact of work zones in a freeway and urban area. Jiang and Adeli (2003) presented a new freeway work zone traffic delay estimate and total work zone cost optimization model that can be adopted for short- and long-term projects. Khanta (2008) evaluated several traffic simulation modeling packages that are designed specifically for work zone analysis and provided recommendations for traffic agencies to opt for the appropriate software for a specific work zone project. Elefteriadou et al. (2008) reported from Florida Department of Transportation criteria that may impact arterial work zone capacity and improve the used analytical model. The FHWA (Bauer et al., 2019), considered referenced two spreadsheet analysis tools from Michigan and Missouri DOTs for estimating queuing and traffic delays during work zones. A more in-depth summary of these projects can be referenced in Appendix D. Many of these projects used and recommended different work zone analysis tools (other tools are explained more in detail in Appendix E). Work zone traffic analysis tools are necessary to provide a better view of the condition in the work zone section prior to and during the work zone activities. Several tools have been utilized historically for the analysis of work zones and some of them are still in use today. However, according to the literature, most of the states in the United States are developing appropriate tools based on the situation and requirements of those states. The developed tools are mainly spreadsheet-based analytical tools. In the following, some of the most common tools are mentioned. 



FREEVAL: Freeway evaluation (FREEVAL) is a computerized method intended to diligently execute computations for undersaturated and oversaturated directional highway installations for organizational study. FREEVAL is a powerful tool based on the Highway Capability Manual for macroscopic freeway research (HCM). FREEVAL-WZ: FREEVAL-WZ is a version of FREEVAL-2015e which is specifically designed to have a user-friendly environment that applies to work zone study planning level approach. The program aims to have the ability to easily test the results of various work zone conditions and to calculate the effects over time and space of congested times (Trask et al., 2016). FREEVAL-NC: FREEVAL-NC is the unique variant of FREEVAL for North Carolina intended to have a user-friendly environment that applies an approach to the planning stage for work zones and reliability review of the freeway. Additionally, FREEVAL-NC has access to the segmentation index of the entire North Carolina highway system. The program aims to rapidly test the results of multiple work zone conditions and to measure the effects of congested cycles over time and space with the ability to expand the reliability study over the year (Trask and Aghdashi, 2019). 62



FREEVAL-PA: FREEVAL-PA is a tool developed by Pennsylvania DOT that facilitates all forms of freeway research, helping PennDOT workers and contractors to easily gain meaningful information on what traffic effects they will see on every limited-access highway facility in the state from various forms of construction and maintenance programs. The software tool, along with traffic volume and speed data, weather data, elevation data, and a computing engine operated by the HCM, is programmed with the roadway characteristics of each highway section in Pennsylvania to understand the effect of decision-making on capacity. The concept of this tool is from the FREEVAL which has been programmed in North Carolina in the early 1990s (FREEVAL-PA, 2020). VISSIM: VISSIM as one of the most popular microsimulation tools could be used for freeway analysis especially conducting research for work zone study. VISSIM requires calibration and validation processes before providing meaningful simulation results. With a systematic procedure for calibration of a VISSIM model, capacity, speed, and queue length can be obtained for work zone analysis. One study regarding the calibration of VISSIM for freeway work zones has concluded that if field speed data is input for desired speed distribution, VISSIM does not reproduce the data for single-lane freeway work zones (Kan et al., 2014).

7.2 Effect of Work Zones on Arterial Networks Construction zones can be broken into those in an urban, arterial setting or freeways. These are very different situations and require separate consideration. Since a greater impact of early opening will be experienced by urban roads, this category will be considered and reported in this report. An alternate analysis of freeways can be referenced in Task 4 of this project. 7.2.1 Methodology To analyze the work-zone impact on the user delay costs of an urban arterial network, a subarea of the Pittsburgh Downtown was used (Figure 32). The network was modeled in VISSIM microsimulation environment. The model consists of 28 signalized intersections and about 550 Origin-Destination (OD) routes. The model was developed for the City of Pittsburgh, for the PM peak hour, and was given to University of Pittsburgh researchers. Considering that the VISSIM model was developed only for the busiest hour of a day, it was necessary to develop a methodology for adjusting the resulting work-zone outputs for the remaining hours of the day.

Figure 32: Downtown Pittsburgh VISSIM model for PM peak hour To simulate a valid work-zone scenario, a segment of the 7th Street (from Fort Duquesne to Penn Ave) was closed and considered as a continuous work zone area. This segment was selected intentionally as it is one of the few segments in the center of the modeled network that give travelers a number of detours. The exact location of the work zone is shown in Figure 33 as the segment encircled with a red rectangle.

Figure 33: Street chosen for closure (7th Street from Fort Duquesne to Penn Ave) Based on the VISSIM model, there is a total of 66 OD routes that are going through the 7 Street from Fort Duquesne and Penn Ave. Therefore, to apply findings from modeling the work-zone during the PM peak conditions to the other hours during the day, a methodology was developed that utilizes field travel times (reported by Google) to understand differences in delays and travel times between the PM peak hour and other hours. Then, based on such differences, the delay adjustment factors were calculated that would be applied to PM peak hour delays to estimate delays for all other hours during the day. To ensure that the analysis of the various routes is done with enough detail and in given timeframe, the analysis was reduced to a total of 23 routes with the most significant volumes. th

After reviewing the selected routes, applicable detours were developed for each route and the extent of vehicles which was rerouted was based on the respective significance of the travel time which would be experienced by that route (avoiding the closed link on the 7 th street). The workaround was the best approach without considering a large-scale traffic assignment scenario which is outside the scope of this project. Consequently, the vehicles in the VISSIM simulation were prevented from entering the link that represents 7 th street. All 66 routes originally passing through the 7th Street were rerouted to the other local streets. Rerouting was done based on the minimum relative travel times for the relevant substitutes of the closed route. In the cases where Google travel times were too similar for two alternative routes, one with the shortest distance was taken into consideration. The Google travel times were collected for the entire 24-hour period. However, to avoid working with too many periods, which are unnecessary, periods were stratified into five distinctive time windows with similar traffic conditions, according to PennDOT data shown in

Figure 34. Google travel times that correspond to each diurnal time window were aggregated for all routes.

Figure 34: Hourly Volume Percentage Changes – PennDOT Explanation for derivation of the delay coefficients (based on Google data) is given in Table 9 below. Xs represent sums of averaged travel times for the relevant times of the day window when the work-zone segment of the 7th Street is closed. Coefficients for PM peak hour are considered to be equal to 1.00. The rest of the delay adjustment coefficients are calculated by applying relevant derived delay adjustment factors to the results from the VISSIM’s model. Time of day windows and volume adjustment factors are based on Figure 34 and similarity of traffic conditions for the observed hours. Volume adjustment factors serve to reduce delay for all vehicle categories in free flow hours and other off-peak hours (when traffic volumes are lower). It should be mentioned that since buses do not operate from midnight until 5 AM the use of a reduction factor for that time window (for buses) is necessary. An important part of the methodology is simulating expansion factors which are used to derive delay for proposed time windows based on the delay from simulation. Delay caused only by work zone impact is calculated as:

(34)

where:

is adjusted D delay, as a difference between delays for work-zone and the base-case scenarios. Thus, D represents delay caused by work-zone only. Table 9: Adjustment of Total Delay Caused by Work Zone

Time of Day AM Peak (7-10am) Mid-day (10-4pm) PM Peak (4-6pm) Evening (6-9pm) Free flow hours (9pm-7am) ∑

Sum of Google Travel Times

Delay Coefficients from Google travel times (fd)

K1= X1/X3

fv1 = 0.95

fs1 =1.5

K2 = X2/X3

fv2 = 0.90

fs2 =3.0

K3=X3/X3=1

fv3 = 1.0

fs3 =1.0

K4= X4/X3

fv4 = 0.85

fs4 =1.5

K5= X5/X3

fv5 = 0.65

fs5 = 5.0

Volume Simulation Adjustment Expansion factor (fv) (fs)

Total delay caused by the Work Zone D1adj= D* K1 * fv1 * fs1 D2adj =D* K2* fv2 * fs2 D3adj = D * K3 * fv3 *fs3 D4adj = D* K4* fv4 * fs4 D5adj = D * K5* fv5 *fs5 (35)

where: is total delay caused by work zone for 24 hours period and it is calculated as sum of adjusted delay differences between base case scenario (normal traffic conditions) and work-zone scenario delays, calculated for distinctive diurnal time windows. Finally, total delay was calculated for three different vehicular categories: passenger cars, heavy vehicles (HGVs), and buses for the entire network of the VISSIM model of downtown Pittsburgh. Afterwards, equations for user delay cost calculations, provided below, are applied.

∑

(36)

(37)

The formulas of user delay cost and vehicle operating costs are presented in Equation (38) and Equation (39). (38) (39) where: is vehicle-hour delay in unit of hours, and are the cost coefficients for the road users in PA, as given by FREEVAL-PA documentation. is 21 $/hr for cars/buses, and 26.2 $/hr for trucks. is 26.2 $/hr for cars, 127.8$/hr for buses and 159.69 $/hr for trucks. Information on average occupancy for passenger cars, HGV and buses that was used is based on the FHWA Average Vehicle Occupancy report (2018) and summarized in Table 10. Table 10: Occupancy rates Vehicular Category Passenger car HGV Bus

Occupancy Rate 1.7 1.0 10.8

The calculation of user costs of delay in this report was based on specific estimations by using certain coefficients (e.g., coefficients representing relationship between travel times and delays over various hours per day). These estimations were done under the assumptions that are listed below. Thus, such a way of calculating user delay costs of delay may have some limitations when compared with the resource-intensive approach of modeling 24-hour traffic operations in an urban network. To estimate user delay costs for arterial network following assumptions were made. Alternate values to these assumptions were briefly considered and will be assessed farther in the discussion. 1. 24-hour delay can be estimated based on information from a single PM peak hour by using delay coefficients. 2. The diurnal volumes are distributed as PennDOT suggests in their Traffic Data Report (2019). Coefficients for volume adjustments were developed based on the previously mentioned report, whereas these can vary on daily basis. 68

3. Travel time from Google maps can be used to calculate delay adjustment coefficients for different periods of the day. 7.2.2 User Cost Associated with Work Zones Analysis of two different scenarios (base case and work zone) examines how presence of the work zones impacts delay and related users' costs. Table 11 represents costs of delay caused by work-zone in Pittsburgh downtown network. One can note that the costs of delay are the highest for passenger cars because that is the most frequent vehicular category in the network. User delay costs for buses are higher than those for trucks because buses have higher occupancy rate. Figure 35 is a graphical representation of the most important information from Table 11 showing that 94% of total user costs is due to increase delay of private cars. The extra delays experience by drivers, because of work-zones, cost around $123,645. Table 11: Cost of Delay Caused by Work-Zone for Pittsburgh Downtown Network Time of day

AM peak 0.91 0.95 Mid-day 0.97 0.90 PM peak 1.00 1.00 Evening 0.84 0.85 Free Flow hours 0.76 0.65 Total Delay (h) User Delay Cost ($) Vehicle Operating Cost ($) Total User Cost of Delay ($)

fs 1.5 3 1 1.5 5

Passenger Car Delay (h) 287.6 581.7 222.8 239.3 547.2 1,878.5 67,064.0 49,217.9 116,281.9

HGV Bus Delay (h) Delay (h) 1.9 4.1 1.4 1.8 5.3 14.4 377.0 2,297.8 2,674.7

2.0 4.1 1.6 1.7 3.9 13.2 2,998.7 1,689.7 4,688.4

Total 291.5 589.8 225.8 242.7 556.4 1,906.2 70,439.7 53,205.4 123,645.1

[CATEGORY Total User Cost of Delay for Arterial Network [CATEGORY NAME], 2,674.7, [PERCENTAGE ]

NAME], 4,688.4, [PERCENTAGE ]

[CATEGORY NAME], 116,281.9, [PERCENTAGE ]

Figure 35: Total User Cost of Delay for Downtown Pittsburgh In the arterial analysis, a few scenarios with different volumes show that a work-zone street closure in Pittsburgh Downtown area can cause user delay costs in the range of $102,779 to $123,645. The difference in models' delays, with and without work-zone, shows that such a street-closure work zone can increase the network's total delay by around 11%. It is essential to mention that the user delay costs incurred during 1.5 days of closure would be much lower if the extra half of the day is time overnight when traffic volumes are their lowest point. This demonstrates the importance of short construction time in urban areas. A significant amount of user delay cost can be saved by cutting down the length of time a roadway must be closed for construction. Even a few hours saved can lead to tens of thousands of user cost saved for the project. Early opening is especially crucial to urban areas because of the effect on neighboring streets. The passenger cars that typically take the roadway under construction are forced onto other routes creating significant delays, as shown in the 11% network delay from the example. Departments of Transportations and drivers can see the benefits of reducing the road closure time to significantly reduce user costs and congestion. This allows concrete construction in an urban environment while limiting the closure time but retaining the long-term performance concrete pavements are known for. However, this study presents several limitations. First, traffic volumes were available only for PM peak hours. Thus, estimation of volumes for other hours was done based on adjustment factors. Second, traffic in the VISSIM model routed through a set of static routes, and the rerouting (due to the work-zone street closure) needed to be done manually. Such an approach reduced the stochastic nature of the traffic and could have a minor-to-moderate impact on the model’s outputs. In the future, the model should use dynamic traffic assignment (DTA). Finally, various elements of modeled network should be calibrated and validated. 70

8 Conclusions Early opening is a simple concept to reduce construction time with minimal cost to the project. The idea is to open a pavement to traffic as soon as the concrete strength can carry the expected traffic load without risking long term performance. It is critical that the strength of concrete be higher than the stress imposed by the expected traffic load. Traditionally, strength criteria are determined through destructive testing such as compressive or flexural strength. Departments of Transportation use either or both destructive tests to determine criteria for opening, however, the criteria differ between states with no clear guideline. Nondestructive methods are also being accepted in the industry to provide immediate results along with other benefits. Maturity and ultrasonic tomography are two nondestructive tests that are able to reliably estimate concrete strength and will be used in this project to improve early opening strength prediction. The field study in this report evaluated the in-situ estimation of concrete strength of newly constructed concrete pavements using traditional maturity and emerging linear array ultrasound testing. Both methods estimate concrete strength indirectly and require calibration with laboratory measured strength development over time. Both methods permitted establishing a good correlation between the laboratory-measured strength properties (compressive or flexural strength) and fundamental PCC concrete properties: the heat of hydration for the maturity method and shear wave velocity for the ultrasound method. Both methods predicted reasonable concrete strength gain trends for in-situ pavement using two different mixtures: High-Early Strength and Long-Life Concrete pavement. Nevertheless, the linear array method was found to have the following advantages:  

  

The same device can be used for taking measurements at the in-situ pavement and at concrete beams required to calibrate the method in the field; The shear wave velocity was found to correlate slightly better with concrete compressive and flexural strength for both mixes, maturity tended to provide a more conservative strength estimate; It does not require installation of sensors prior to pavement paving; The measurements can be taken practically at any point of the pavements system surface; It permits evaluation of variability of strength development in the in-situ pavement.

Shear wave velocities reported by the ultrasound tomography linear array system MIRA for beams and for slabs presented a systematic difference for the same maturity level. A possible explanation for this phenomenon is the influence of the concrete beam sides on the velocity calculation. A procedure for adjustment of beam velocity was proposed in this study, but a more rigorous procedure for computing shear wave velocity from the signals time histories for individual sensor pairs will improve accuracy and reliability of this technology. 71

Previous studies show that the current criteria for strength to open to traffic are conservative. Minnesota Department of Transportation were able to successfully load a concrete pavement after three hours with a flexural strength of 73 psi with no significant damage in the short- or long-term. The same study farther determined a mechanistic-based early opening damage analysis that is able to predict cracking and dowel bar performance reliability based on the maturity or strength at opening. This allows for proper risk assessment when determining when to open a pavement to traffic by accounting for rate of concrete strength gain, traffic volume, climate, load characteristics, and pavement structure parameters. This damage analysis model was expanded to apply to Pennsylvania conditions and to include the relation between maturity and shear wave velocity so that additional nondestructive testing can be used to predict strength gain. The addition of shear wave velocity allowed the pavement to be monitored at any location desired instead of being restricted to the temperature sensor locations for maturity. Critical or additional locations can be scanned quickly using ultrasonic tomography and still be used for strength predictions. Concerning the possibility of opening pavements to traffic at earlier times, results from the traffic simulations indicate that for arterial roads, faster construction does have a significant impact on user delay costs. Since simulation involved a 24-hour road closure due to an active work zone, any reduction in closure time will yield substantial reduction in costs especially if the closure time is reduced during daytime. However, cases should be studied and modelled with a higher level of detail regarding the particular circumstances of each closure area and work zone.

References ―A1040 MIRA.‖ ACS, https://acs-international.com/product/a1040-mira/. Date Accessed Nov. 17, 2021. An, J.-H., J. Nam, S.-H. Joh, and S.-A. Kwon, ―Estimation of the Compressive Strength of Concrete Using Shear Wave Velocity,‖ in New Technologies in Construction and Rehabilitation of Portland Cement Concrete Pavement and Bridge Deck Pavement, 2009, pp. 154–164. ASTM, ASTM C39-Compressive Strength of Cylindrical Concrete Specimens, ASTM Stand., 2015. pp. 1–7. ASTM, ASTM C78- Flexural Strength of Concrete (Using Simple Beam with Third-Point Loading), Annu. B. ASTM Stand., 2008. 4, no. Reapproved, pp. 1–7. ASTM, ASTM 1074-Standard Practice for Estimating Concrete Strength by the Maturity Method 1, ASTM Stand. 2013, pp. 1–10 Bauer, Jocelyn, et al. ―Integrating Travel Time Reliability into Transportation System Management.‖ U.S. Department of Transportation, FHWA, 2019. ―Best Practices In Work Zone Assessment, Data Collection, And Performance Evaluation.‖ Lawrenceville, NJ : National Cooperative Highway Research Program, 2010. NCHRP Project 20-68A. Carino, N. J. and H. S. Lew, “The Maturity Method: From Theory to Application,” Structures Congress & Exposition, 2001, p. 19. Cho, M., S. Joh, S. A. Kwon, and T. Kang, ―Nondestructive In-Place Strength Profiling of Concrete Pavements by Resonance Search Technique,‖ Proceedings of the 86th Annual Meeting of the Transportation Research Board, Washington D.C., 2007. Choi, P., D.H. Kim, B.H. Lee, and M.C. Won, ―Application of Ultrasonic Shear-Wave Tomography to Identify Horizontal Crack or Delamination in Concrete Pavement and Bridge,‖ Constr. Build. Mater., 121, 2016, 81–91. https://doi.org/10.1016/j.conbuildmat.2016.05.126 Choubane, B. and Tia, M., ―Nonlinear temperature gradient effect on maximum warping stresses in rigid pavements.‖ Transportation Research Record, 1370, 1992, 11–19. ―Commonwealth of Pennsylvania Department of Transportation-Publication 408,‖ 2021. Copeland, LuAnn. ―User's Manual for QUEWZ-98.‖ Texas Transportation Institute, 1998.

Crawford, G. I., ―Guide to Nondestructive Testing of Concrete,‖ FHWA-SA-97-105. 1997. Crovetti, J. and L. Khazanovich, ―Early Opening of Portland Cement Concrete (PCC) Pavements to Traffic,‖ WHRP 05-13. Wisconsin Department of Transportation, Milwaukee, Wisconsin, 2005. DeVries, Lyle, et al. ―Full Closure Strategic Analysis.‖ Colorado Department of Transportation, 2014. Edara, Praveen K. and Cottrell, Benjamin H., ―Estimation of Traffic Mobility Impacts at Work Zones: State of the Practice.‖ Transportation Research Board Annual Meeting, 2006. Edara, Praveen. ―Evaluation of Work Zone Enhancement Software Programs.‖ Missouri Department of Transportation, 2009. Edara, Praveen, et al. ―Understanding the Impacts of Work Zone Activities on Traffic Flow Characteristics.‖ Smart Work Zone Deployment Initiative, FHWA, 2018. Elefteriadou, Lily, Jain, Mayank and Heaslip, Kevin. ―Impact of Lane Closures on Roadway Capacity Part B: Arterial Work Zone Capacity.‖ Florida Department of Transportation, 2008. Freeseman, K., K. Hoegh, and L. Khazanovich, ―Concrete Strength Required to Open to Traffic,‖ MN/RC 2016-01. Saint Paul, MN, 2016. ―FREEVAL-PA: An Innovation in Transportation & Software Engineering.‖ Kittelson & Associates. [Online] 2020. https://www.kittelson.com/ideas/freeval-pa-an-innovation-intransportation-software-engineering/. Hardy, Matthew and Wunderlich, Karl. ―Traffic Analysis Tools Volume IX: Work Zone Modeling and Simulation—A Guide for Analysts.‖ Federal Highway Administration Office of Operations, 2009. Hardy, Matthew and Wunderlich, Karl. ―Work Zone Analysis—A Guide for Decision-Makers.‖ Federal Highway Administration Office of Operations, 2008. Heaslip, Kevin, et al. ―Estimation of Freeway Work Zone Capacity Through Simulation and Field Data.‖ Transportation Research Record, 2009. Helal, J., M. Sofi, and P. Mendis, ―Non-destructive testing of concrete: A review of methods,‖ Electron. J. Struct. Eng., vol. 14, no. 1, pp. 97–105, 2015. Hiller J. and J. Roesler, ―Simplified nonlinear temperature curling analysis for jointed concrete pavements.‖ ASCE Journal of Transportation Engineering, 136(10), 2010, 654-663

Hoegh, K., L. Khazanovich, and H. T. Yu, ―Ultrasonic tomography for evaluation of concrete pavements,‖ Transp. Res. Rec., 2232, 2011, 85–94. https://doi.org/10.3141/2232-09 Hoegh, K., L. Khazanovich, and H.T. Yu, “Concrete pavement joint diagnostics with ultrasonic tomography,” Trans. Res. Rec., 2305, 54-61, 2012. Hoegh, K., ―Ultrasonic Linear Array Evaluation of Concrete Pavements‖. Dissertation. University of Minnesota, 2013. Ioannides, A. and L. Khazanovich, ―Nonlinear Temperature Effects in Multi-Layered Concrete Pavements.‖ ASCE Journal of Transportation Engineering, 124(2), 1998, 128–136. Jacoby, Ken, et al. ―A Tool for Integrated Schedule-Traffic-Cost Analysis for Highway Projects.‖ United States Department of Transportation, FHWA. [Online] 2008. https://www.fhwa.dot.gov/construction/ca4prsbroc.cfm. Jeannotte, Krista and Chandra, Andre. ―Developing and Implementing Transportation Management Plans for Work Zones.‖ United States Department of Transportation, Federal Highway Administration, 2005. Jiang, Xiaomo and Adeli, Hojjat. ―Freeway Work Zone Traffic Delay and Cost Optimization Model,‖ Journal of Transportation Engineering, 2003. Jiang, Yi. ―Estimation of Traffic Delays and Vehicle Queues at Freeway Work Zones,‖ 80th Annual Meeting of the Transportation Research Board. 2001. Kan, Xingan David, Ramezani, Hani and Benekohal, Rahim F., ―Calibration of VISSIM for Freeway Work Zones with Time-Varying Capacity.‖ Transportation Research Board 93rd Annual Meeting, 2014. Khanta, Pothu Raju. ―Evaluation of Traffic Simulation Models for Work Zones in the New England Area.‖ University of Massachusetts Amherst, 2008. Khazanovich, L., ―Structural Analysis of Multi-Layered Concrete Pavement Systems.‖ Ph.D. Thesis, University of Illinois, Urbana, IL, 1994. Khazanovich, L., O. I. Selezneva, H. T. Yu, and M. I. Darter. ―Development of Rapid Solutions for Prediction of Critical Continuously Reinforced Concrete Pavement Stresses.‖ In Transportation Research Record: Journal of the Transportation Research Board, No. 1778, TRB, National Research Council, Washington, D.C., 2001, pp. 64–72. Khazanovich, L., K. Kosar, and H. Li, ―Evaluation of Long-Term Impacts of Early Opening of Concrete Pavements.‖ Minnesota Department of Transportation. NRRA202111. 2021

Khazanovich, L. and D. Tompkins, ―Incorporating Slab/Underlying Layer Interaction into the Concrete Pavement Analysis Procedures.‖ The National Academies Press, Washington, D.C., 2017. Khazanovich, L., J. Vandenbossche, J. DeSantis, S. Sachs. ―Development of an Improved Design Procedure for Unbonded Concrete Overlays.‖ Report No. MN 2020-08. Minnesota Department of Transportation, 2020. Khazanovich, L., K. Kosar, and H. Li, ―Evaluation of Long-Term Impacts of Early Opening of Concrete Pavements,‖ National Road Research Alliance, Minnesota Department of Transportation, 2021. Report Number: NRRA202111 Kite, Steve. Work Zone ITS- ―North Carolina’s Experience‖. North Carolina DOT, 2013. Mechanistic–Empirical Pavement Design Guide, Interim Edition: A Manual of Practice. AASHTO, Washington, D.C., 2015. Memmott, Jeffery L. and Dudek, Conrad L., ―Queue and User Cost Evaluation of Work Zones.‖ Transportation Research Board, 1984. Michigan Department of Transportation, ―Special Provision for Quality Control and Acceptance of Portland Cement Concrete,‖ Michigan Dep. Transp., vol. 604. August 2013. Minnesota Department of Transportation, ―Estimating concrete strength by the maturity method.‖ MnDOT Specifications 2461.3.G.6, 2020. Concrete Maturity (state.mn.us) Mudge, Richard, et al. ―Work Zone Performance Measurement Using Probe Data.‖ U.S. Department of Transportation, Federal Highway Administration, 2013. NCHRP. ―Guide for mechanistic-empirical design of new and rehabilitated pavement structures.‖ Final report. Project 1-37A. National Cooperative Highway Research Program, Transportation Research Board, Washington, D.C., 2004. NJDOT, New Jersey Department of Transportation Standard Specifications for Road and Bridge Construction, New Jersey Dep. Transp. 2015. pp. 1–37 ―Office of Operations‖. FHWA Office of Operations. U.S. Department of Transportation, FHWA. [Online] April 2020. https://ops.fhwa.dot.gov/aboutus/opstory.htm. ―Regional Offices.‖ Pennsylvania Department of Transportation, [Online] 2021. https://www.penndot.gov/RegionalOffices/Pages/default.aspx. Pennsylvania DOT. ―Publication 213- Temporary Traffic Control Guidelines.‖ Pennsylvania DOT, 2006-2007.

Pennsylvania Department of Transportation. ―Pennsylvania Work Zone Pocket Guide for Municipalities & Utilities.‖ Pennsylvania Department of Transportation, 2010. PennDOT, PTC, and PSP. ―Pennsylvania's Automated Work Zone Speed Enforcement.‖ [Online] September 2020. https://workzonecameras.penndot.gov/locations/. Salles, L., L. Khazanovich, L., and J. T. Balbo. Non-Destructive Evaluation of Crack Initiation and Propagation in Continuously Reinforced Concrete Pavements. Transportation Research Record, 2673 (2019), 375–385. https://doi.org/10.1177/0361198119833672 Sankar, Param, et al. ―Work Zone Impacts Assessment – An Approach to Assess and Manage Work Zone Safety and Mobility Impacts of Road Projects.‖ Washington, DC : Federal Highway Administration (FHWA), 2006. Schnell, Thomas, Mohror, Jeffrey S. and Aktan, Fuat. ―Evaluation of Traffic Flow Analysis Tools Applied to Work Zones Based on Flow Data Collected in the Field.‖ Transportation Research Record, 2002. Scriba, Tracy and Feast, Laura. ―Using Modeling and Simulation Tools for Work Zone Analysis. Work Zone and Traffic Analysis.‖ [Online] May 2009. https://ops.fhwa.dot.gov/wz/traffic_analysis/wza_leaflet/wza_leaflet.htm. Song, Ziqi and Yin, Yafeng. ―Impact of Lane Closures on Roadway Capacity Part C: Modeling Diversion Propensity at Work Zones.‖ Florida Department of Transportation, 2008 State of Ohio Department of Transportation. ―Ohio Construction and Material Specifications,‖ Harrisburg, PA, 2019. Tennessee Department of Transportation. ―Work Zone Safety and Mobility Manual.‖ Tennessee Department of Transportation, 2017. Thomlinson, J., ―Temperature Variations and Consequent Stresses Produced by Daily and Seasonal Temperature Cycles in Concrete Slabs.‖ Concrete and Construction Engineering, 36 (6), 1940, 298-307. ―Traffic Bottlenecks.‖ U.S. Department of Transportation, FHWA. [Online] March 2020. https://ops.fhwa.dot.gov/bn/bnchart.htm. Trask, Lake, et al. ―FREEVAL-WZ Users Guide.‖ NC State University, 2016. Trask, Lake and Aghdashi, Behzad. ―FREEVAL NC User Guide.‖ Kittelson & Associates, ITRE, 2019. TxDOT, ―Standard Specifications for Construction and Maintenance of Highways, Streets, and Bridges,‖ Texas Dep. Transp., November 2014.

Ullman, Gerald L., Porter, Richard J. and Karkee, Ganesh J. ―Monitoring Work Zone Safety and Mobility Impact in Texas.‖ Texas Department of Transportation, 2009. ―Urban Street Reliability Engine User Guide.‖ Kittelson & Associates, Inc. 2014. Vancura, M., L. Khazanovich, and R. Barnes, Concrete pavement thickness variation assessment with cores and nondestructive testing measurements, Transp. Res. Rec., 2347 (2013) 61– 68. https://doi.org/10.3141/2347-07 Van Dam, T. J., Peterson, K. R., Sutter, L. L., Panguluri, A., Sytsma, J., Buch, N., Kowli, R., & Desaraju, R., ―Guidelines for Early-Opening-to-Traffic Portland Cement Concrete for Pavement Rehabilitation (NCHRP Report 540).‖ Washington, DC: National Highway Cooperative Research Program, Transportation Research Board, National Academies. 2005 Washburn, Scott S., Hiles, Thomas and Heaslip, Kevin. ―Impact of Lane Closures on Roadway Capacity,‖ Florida Department of Transportation, 2008. Westergaard, H. M. ―Stresses in Concrete Pavements Computed by Theoretical Analysis. Public Roads,‖ Vol. 7, No. 2, 1926, pp. 25–35. Also appeared as Computation of Stresses in Concrete Roads. HRB Proc., 5th Annual Meeting, Part I, National Research Council, Washington, D.C., 1926, pp. 90–112. ―Work Zone and Traffic Analysis Tools.‖ U.S. Department of Transportation, FHWA. [Online] Work Zone Management Program, February 2019. https://ops.fhwa.dot.gov/wz/traffic_analysis/tools.htm. Ye , Zhirui and Al-kaisy, Ahmed. ―Explore ITS Technologies for Work Zones and Work Zone Impact Areas.‖ Western Federal Lands Highway Division, 2010. Yu, H.T., L. Khazanovich, M.I. Darter, and A. Ardani, ―Analysis of Concrete Pavement Responses to Temperature and Wheel Loads Measured from Instrumented Slabs.‖ Transportation Research Record, 1639, 1998, 94–101. Zayed, A., ―Performance Improvement of High Early Strength (HES) Concrete for Pavement Replacement,‖ 2018.

Appendix A Appendix A presents the average shear wave velocity (SWC) collected in slabs HES1 and C1.1 during the experiment. Table A. 1: Shear Wave Velocity of slab C1.1 (Day 1) Date

Time

7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20

15:27 15:28 15:30 15:31 15:31 15:32 15:45 15:46 15:47 15:48 15:49 15:50 15:59 16:00 16:01 16:02 16:03 16:04 16:15 16:15 16:17 16:18 16:19 16:19 16:44 16:44 16:45 16:45 16:47 16:48

Time from construction (h) 5.13 5.14 5.17 5.18 5.19 5.20 5.42 5.43 5.45 5.48 5.48 5.50 5.66 5.68 5.68 5.70 5.72 5.73 5.92 5.92 5.95 5.97 5.98 5.99 6.40 6.41 6.42 6.42 6.46 6.47

Section

Position

Avg. SWV (m/s)

Cycle

C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1

TE TC CRS CRN CLS CLN TE TC CLS CRS CLN CRN TE TC CLS CRS CLN CRN TE TC CLS CRS CLN CRN TE TC CLS CRS CLN CRN

1445 1475 1520 1505 1495 1520 1565 1590 1575 1610 1680 1735 1650 1685 1675 1680 1700 1740 1745 1790 1785 1780 1800 1830 1925 1940 1950 1965 1970 1975

1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5

A-1

Table A. 2: Shear Wave Velocity of slab C1.1 (Subsequent days) Date

Time

7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/15/20 7/15/20 7/15/20 7/15/20 7/15/20 7/15/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20

8:32 8:35 8:36 8:37 8:38 8:39 10:50 10:51 10:52 10:53 10:53 10:54 10:37 10:38 10:39 10:39 10:40 10:41 12:26 12:27 12:28 12:29 12:30 12:31 8:06 8:06 8:07 8:07 8:08 8:08

Time from construction (h) 22.21 22.25 22.28 22.29 22.31 22.33 24.50 24.52 24.53 24.55 24.56 24.57 48.29 48.30 48.32 48.33 48.33 48.35 74.11 74.13 74.14 74.15 74.17 74.18 117.77 117.77 117.78 117.79 117.80 117.81

Section

Position

C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1

TE TC CRS CLS CRN CLN TE TC CLS CLN CRN CRS TE TC CLS CLN CRN CRS TE TC CLS CLN CRN CRS TE TC CRN CRS CLS CNS

Avg. SWV Cycle (m/s) 2520 2500 2500 2530 2500 2530 2530 2530 2530 2530 2530 2530 2560 2605 2580 2580 2580 2605 2600 2530 2615 2610 2625 2610 2645 2660 2665 2650 2660 2660

6 6 6 6 6 6 7 7 7 7 7 7 8 8 8 8 8 8 9 9 9 9 9 9 10 10 10 10 10 10

Table A. 3: Shear Wave Velocity of slab C1.1 (Subsequent days) Part II Date

Time

Time from construction

Section

Position

Avg. SWV Cycle (m/s) A-2

7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20

10:43 10:44 10:44 10:45 10:46 10:47 11:46 11:47 11:47 11:48 11:49 11:49

(h) 168.38 168.40 168.41 168.43 168.44 168.45 337.43 337.45 337.46 337.48 337.48 337.49

C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1 C-1.1

TE TC CRS CLS CRN CLN TE TC CRN CRS CLS CLN

2650 2635 2655 2665 2685 2665 2695 2700 2715 2695 2710 2680

11 11 11 11 11 11 12 12 12 12 12 12

Table A. 4: Shear Wave Velocity of slab HES1 (Day 1) Date

Time

7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20

10:22 10:25 10:29 10:30 10:32 10:34 10:37 10:39 10:41 10:42 10:44 10:45 10:51 10:53 10:54 10:56 10:58 10:59

Time from construction (h) 2.78 2.84 2.91 2.92 2.95 2.98 3.03 3.07 3.11 3.13 3.16 3.18 3.28 3.31 3.33 3.35 3.38 3.41

Section

Position

Avg. SWV (m/s)

Cycle

HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1

TE TC CRS CLS CLN CRN TE TC CRS CLS CLN CRN TE TC CRS CLS CLN CRN

3537 3930 3387 1997 2453 2657 3230 1010 2980 1010 1010 1010 1010 1050 1010 1153 1190 1083

1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3

Table A. 5: Shear Wave Velocity of slab HES1 (Day 1) Part II Date

Time

Time from construction

Section

Position

Avg. SWV (m/s)

Cycle

A-3

11:05 11:07 11:08 11:10 11:11 11:12 11:20 11:21 11:23 11:24 11:25 11:27 12:00 12:00 12:01 12:02 12:03 12:03 12:34 12:36 12:37 12:38 12:39 12:40 13:00 13:01 13:02 13:03 13:04 13:05

(h) 3.51 3.53 3.56 3.58 3.61 3.62 3.76 3.78 3.80 3.82 3.84 3.87 4.42 4.43 4.43 4.45 4.47 4.48 4.99 5.03 5.04 5.06 5.08 5.09 5.42 5.43 5.45 5.47 5.49 5.51

HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1

TE TC CRS CLS CLN CRN TE TC CRS CLS CLN CRN TE TC CRS CLS CLN CRN TE TC CRS CLS CLN CRN TE TC CRS CLS CLN CRN

1073 1187 1047 1243 1267 1220 1210 1300 1203 1387 1410 1337 1555 1610 1530 1670 1675 1655 1770 1835 1755 1830 1950 1920 1935 2010 1925 2055 2015 2030

4 4 4 4 4 4 5 5 5 5 5 5 7 7 7 7 7 7 8 8 8 8 8 8 9 9 9 9 9 9

Table A. 6: Shear Wave Velocity of slab HES1 (Day 1) Part III Date

Time

7/13/20 7/13/20 7/13/20

13:26 13:27 13:28

Time from construction (h) 5.86 5.88 5.89

Section

Position

Avg. SWV (m/s)

Cycle

HES-1.1 HES-1.1 HES-1.1

TE TC CRS

2075 2095 2060

10 10 10 A-4

13:29 13:30 13:31 14:00 14:00 14:01 14:02 14:03 14:04 14:22 14:23 14:24 14:25 14:26 14:27 15:21 15:22 15:23 15:23 15:24 15:25 16:21 16:23 16:24 16:25 16:25 16:26

5.91 5.93 5.93 6.42 6.43 6.44 6.45 6.47 6.48 6.78 6.81 6.83 6.83 6.85 6.87 7.77 7.78 7.80 7.81 7.83 7.83 8.78 8.80 8.82 8.83 8.83 8.86

CLS CLN CRN TE TC CRS CLS CLN CRN TE TC CRS CLS CLN CRN TE TC CLS CRS CLN CRN TE TC CLS CRS CLN CRN

2100 2100 2125 2140 2135 2110 2135 2130 2160 2170 2155 2115 2150 2140 2160 2170 2195 2160 2135 2150 2240 2210 2175 2210 2170 2170 2250

10 10 10 11 11 11 11 11 11 12 12 12 12 12 12 13 13 13 13 13 13 14 14 14 14 14 14

Table A. 7: Shear Wave Velocity of slab HES1 (Subsequent Days) Date

Time

7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20

8:16 8:17 8:19 8:20 8:21 8:22 10:45

Time from construction (h) 24.69 24.71 24.73 24.75 24.77 24.79 27.17

Section

Position

Avg. SWV (m/s)

Cycle

HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1

TE TC CRS CRN CLN CLS TE

2310 2300 2310 2320 2310 2305 2305

15 15 15 15 15 15 16 A-5

7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/15/20 7/15/20 7/15/20 7/15/20 7/15/20 7/15/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20

10:45 10:46 10:47 10:48 10:49 10:32 10:33 10:34 10:35 10:36 10:36 13:04 13:05 13:06 13:07 13:08 13:09 7:49 7:50 7:51 7:52 7:53 7:54

27.18 27.19 27.20 27.22 27.23 50.96 50.98 50.98 51.00 51.02 51.03 77.49 77.50 77.53 77.54 77.56 77.58 120.23 120.25 120.27 120.28 120.30 120.32

HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1

TC CRS CLS CLN CRN TE TC CLS CRS CRN CLN TE TC CLN CRN CRS CLS TE TC CRN CRS CLS CLN

2310 2285 2300 2310 2310 2335 2310 2340 2310 2345 2330 2330 2300 2360 2310 2270 2310 2405 2310 2405 2395 2400 2400

16 16 16 16 16 17 17 17 17 17 17 18 18 18 18 18 18 19 19 19 19 19 19

Table A. 8: Shear Wave Velocity of slab HES1 (Subsequent Days) Part II Date

Time

7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20

10:36 10:37 10:39 10:40 10:41 10:42 11:34 11:35 11:36 11:37 11:37

Time from construction (h) 171.02 171.04 171.07 171.08 171.10 171.12 339.98 340.00 340.03 340.03 340.04

Section

Position

Avg. SWV (m/s)

Cycle

HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1 HES-1.1

TE TC CRS CLS CRN CLN TE TC CRN CRS CLS

2310 2305 2360 2365 2410 2350 2405 2365 2415 2400 2370

20 20 20 20 20 20 21 21 21 21 21 A-6

7/27/20

11:38

340.05

HES-1.1

CLN

2395

A-7

Appendix B Appendix B presents the average shear wave velocity (SWV) collected in beams at the laboratory. Table B. 1: Shear Wave Velocity for LLCP beams Date

Time

7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/14/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20 7/16/20

10:23 10:25 10:27 10:29 10:31 10:33 10:34 10:35 10:37 10:38 10:40 10:41 11:04 11:06 11:08 11:09 11:14 11:17 11:20 11:24 11:28 11:31 11:33 11:36

Time from construction (h) 23.89 23.92 23.96 23.99 24.02 24.05 24.07 24.09 24.12 24.14 24.17 24.19 72.57 72.61 72.63 72.65 72.74 72.79 72.84 72.90 72.97 73.02 73.06 73.10

Section

Position

Avg. SWV (m/s)

D1B7 D1B7 D1B7 D1B7 D1B5 D1B5 D1B5 D1B5 D1B9 D1B9 D1B9 D1B9 D1B12 D1B12 D1B12 D1B12 D1B11 D1B11 D1B11 D1B11 D1B13 D1B13 D1B13 D1B13

A B C D A B C D A B C D A B C D A B C D A B C D

2617 2590 2620 2617 2670 2587 2660 2553 2670 2617 2647 2613 2727 2730 2730 2730 2730 2670 2730 2730 2730 2670 2730 2730

Table B. 2: Shear Wave Velocity for LLCP beams Part II Date

Time

Time from construction

Section

Position

Avg. SWV (m/s) B-1

7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/18/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/20/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20 7/27/20

7:20 7:21 7:23 7:24 7:26 7:28 7:30 7:32 7:36 7:38 7:40 7:42 10:13 10:14 10:16 10:17 10:20 10:22 10:24 10:26 10:28 10:30 10:32 10:33 10:51 10:52 10:53 10:54 10:59 11:01 11:02 11:03 11:11 11:12 11:13 11:14

(h) 116.84 116.86 116.88 116.90 116.94 116.98 117.01 117.03 117.10 117.14 117.17 117.20 167.72 167.73 167.77 167.79 167.84 167.87 167.91 167.94 167.98 168.01 168.03 168.06 336.35 336.37 336.38 336.40 336.49 336.52 336.53 336.55 336.68 336.71 336.72 336.74

D1B15 D1B15 D1B15 D1B15 D1B4 D1B4 D1B4 D1B4 D1B8 D1B8 D1B8 D1B8 D1B18 D1B18 D1B18 D1B18 D1B14 D1B14 D1B14 D1B14 D1B10 D1B10 D1B10 D1B10 D1B6 D1B6 D1B6 D1B6 D1B17 D1B17 D1B17 D1B17 D1B16 D1B16 D1B16 D1B16

A B C D A B C D A B C D A B C D A B C D A B C D A B C D A B C D A B C D

2730 2720 2733 2660 2730 2730 2773 2730 2733 2673 2730 2703 2747 2737 2833 2747 2733 2720 2747 2730 2870 2737 2857 2740 2770 2730 2730 2693 2880 2740 2883 2760 2890 2737 2890 2870

Table B. 3: Shear Wave Velocity for HES beams Date

Time

Time from construction

Section

Position

Avg. SWV (m/s) B-2

7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20 7/13/20

14:43 14:45 14:47 14:48 14:50 14:53 14:54 14:56 14:59 15:00 15:02 15:04 15:08 15:09 15:11 15:12

(h) 7.14 7.17 7.20 7.23 7.26 7.30 7.32 7.35 7.41 7.43 7.46 7.49 7.56 7.57 7.60 7.62

D2B3 D2B3 D2B3 D2B3 D2B1 D2B1 D2B1 D2B1 D2B4 D2B4 D2B4 D2B4 D2B2 D2B2 D2B2 D2B2

D A B C A B C D A B C D A B C D

2253 2280 2253 2323 2200 2227 2250 2170 2267 2307 2307 2260 2243 2237 2270 2210

B-3

Appendix C Appendix C provides a user manual to use the Early Opening to Traffic Analysis Webbased Tool created in this study. Figure C. 1 shows the default screen that appears when the user first accesses the website and highlights the different areas to be discussed in this user manual.

Traffic Spectrum

User Inputs

Parameters at Time of Opening

Nondestructive Models

Advanced Settings

Figure C. 1: Web-based tool opening screen

D-1

User Inputs To begin, the user must choose the location of the pavement project from a drop-down menu. Location is broken into five districts based on similar climates. The options for pavement location are as follows:     

Region 1: Erie County Region 2: PennDOT Districts D1, D10, D11, and D12 (except for Erie) Region 3: PennDOT Districts D2 and D9 Region 4: PennDOT Districts D3 and D4 Region 5: PennDOT Districts D5, D6, and D8

Use Error! Reference source not found. for reference if district is unknown. Next, the time of construction by month must be chosen from the drop-down menu.

Figure C. 2: Construction and traffic parameters Then the number of trucks per day must be entered by the user and a traffic pattern must be chosen from the drop-down menu. Choosing a traffic pattern will fill the traffic spectrum shown in Figure C. 3. Options for traffic patterns that automatically create a traffic spectrum are Minor Arterial, Residential, and Interstate. There is also a custom option if the exact traffic spectrum is known. The user can then input the custom traffic directly into the table.

Figure C. 3: Provided traffic spectrum for minor arterial

D-2

Next, the user must input the PCC and base thickness in inches. Additional parameters include the PCC coefficient of thermal expansion and base modulus. The default options are typical values but for increased accuracy, it is recommended the user input the project values. Joint spacing must be entered in feet. The shoulder type must be chosen from the drop-down menu as a tied shoulder or asphalt/no shoulder. Dowel diameter in inches must be entered. The design PCC flexural strength is the final input required in the user input section of the website. This is the required flexural strength for the project.

Figure C. 4: Pavement structure inputs

Parameters at Traffic Opening This section of the website is about the pavement at the time of opening. The nondestructive test being used must be chosen from the drop-down menu between shear wave velocity and maturity. Depending on the nondestructive test chosen, the value below will change to represent the method. The user then can enter a value for compressive PCC strength at opening. This can be a guess at first and will be reiterated as improved estimations are found. The minimum value is dictated by an advanced setting to be discussed later. The web tool will automatically change the PCC shear wave velocity or maturity and PCC flexural strength at opening.

Figure C. 5: User inputs at time of opening for shear wave velocity or maturity.

D-3

Nondestructive Models In this section of the tool the user has the option of modifying the model that relate the nondestructive tests to strength. First the user must enter the ultimate flexural and compressive strength of the concrete mixture.

Figure C. 6: Ultimate strength inputs Here the user can change the model coefficients. If shear wave velocity is the nondestructive test chosen, there will be an additional model to relate shear wave velocity to flexural strength. The remaining models are the same to create the strength predictions. The user can change these coefficients from their typical default values to the values the user calculated based on their own project. These can be found using the procedure outlined in this project.

(a)

(b) Figure C. 7: Location to change the nondestructive testing model coefficients for (a) shear wave velocity and (b) maturity.

D-4

Advanced Settings The advanced settings can be opened be clicking the Settings button at the bottom of the screen. This opens the following options:

Figure C. 8: Advanced settings These settings are not required to use the web tool but will increase the accuracy of the results. Increasing the number of simulations can increase the accuracy of the model but will also take longer. The built in curling and dynamic k-value can be changed but a standard value is the default. The expected PCC thickness variation can also be changed depending on past work in the area, but the standard default value is 0.03. The strength coefficient can also be changed here from the standard value of 1. The coefficient of variation in the modulus of rupture coefficients can also be modified for increased accuracy. The key part of the advanced settings is the minimum strength values. It is highly recommended to enter these values for each project because the minimum opening strength suggestion will not go lower than this value. This is the lowest age that strength testing was done in the lab. The analysis will not go lower than this value so that it does not extrapolate a strength value that was not tested. Click OK to exit the advanced settings.

D-5

Reading the Results When the entered values best represent the project, click submit. The results will start with numerical values for the cracking performance reliability, dowel performance reliability, and ESALs repetitions to design strength at the opening strength chosen in the inputs (Figure C. 5).

Figure C. 9: Numerical results If the reliabilities are not sufficient for the user, the simulation can be rerun using the graphed results to determine the next opening strength guess. In this example, the initial value for cracking performance is only about 89% which can be improved. Using the reliability graph created in the results, the user can find a strength with a higher reliability. In this example, using a flexural strength of 460 psi will provide a 97% cracking performance reliability.

Figure C. 10: Reliability graphs The user can then change the compressive PCC strength at opening to the updated value and rerun the simulation. It is not recommended to use the values from the result graphs without rerunning the simulation for that opening strength. Once the reliability is acceptable for the user, that strength is the recommended value to open to traffic for that project.

D-6

Appendix D In this appendix, based on the reviewed documents, webinars, and other resources, a brief summary is provided of what methods and tools are other states around the nation utilizing for analyzing the impacts of work zones. This information could be helpful in terms of decision making for the appropriate work zone software with various configurations. Table E. 1: State DOT Survey Results States

California

Delaware

Tools and Methods FREQ, HCS, CORSIM, and PASSER Spreadsheets are used for capacity addition projects on all roads except urban arterials and urban freeways. PASSER program is used for urban arterial roadways with signalized intersections, diamond interchanges, etc. High impact urban freeway construction or rehabilitation projects with capacity reductions: FREQ, HCS, or CORSIM. QuickZone and CA4PRS QuickZone as the main tool to estimate traffic impacts of work zones. A simpler version of the tool called the 'Lane Closure Analysis Program (LCAP)' is also used for simple work zone. configuration Work zones on arterials or interstates that do not involve any signalized intersections are analyzed using QuickZone or LCAP.

For two-lane roadways with flagging operations, the CORSIM simulation program with sufficient parameter calibration is used. SYNCHRO Florida

Uses a customized spreadsheet program.

Illinois

Use of a customized excel spreadsheet with input capacity values determined by the QUEWZ program. The QUEWZ program has produced good estimates of queue lengths in the past. However, since the software is no longer maintained Ohio DOT is looking for alternatives for the future. D-1

Iowa

Maryland

A customized spreadsheet was occasionally used. Based on past experience and knowledge of the staff. One of the districts also mentioned using SYNCHRO software. QuickZone SYNCHRO A customized excel spreadsheet that performs HCM analysis.

Minnesota

Missouri

HCM Quickzone Quickzone AASHTO User Benefit Analysis CORSIM

New Hampshire

VISSIM SYNCHRO MoDOT Spreadsheet Quickzone

New York State Ohio

Quickzone when it is close to the work zone lane capacity threshold Sketch planning tools SYNCHRO WZTA Tool

Oregon

Highway Capacity Manual (HCM) SYNCHRO Microsimulation

Pennsylvania

Texas

A Spreadsheet developed by a consultant. FREEVAL SYNCHRO Quickzone HCS

Wisconsin

Work Zone Traffic Analysis Tool (WZTAT)

D-2

Appendix E In this appendix, different types of work zone tools are described briefly. QuickZone QuickZone program is an analytical model, developed by FHWA, to facilitate the estimation of work zone traffic impacts. QuickZone provides a platform to compare traffic impacts for work zone mitigation strategies including estimating the costs, time delays, and potential backups associated with these impacts in addition to evaluating traffic delays linked with work zone schedules in relation to peak and off-peak traffic periods and/or with the employment of diversion routes. It can also display the amount of delay in vehicle hours and the maximum length of the projected traffic queue associated with the work activity. QuickZone is capable of analyzing full lane closures as well. QuickZone requires detour routes to be defined as part of the analysis network. Although QuickZone grants a comprehensive and detailed analysis of delays occurring in work zones, the process of analysis requires more time and effort in comparison with spreadsheet models (Edara et al., 2018). QUEWZ QUEWZ is an analysis tool for Queue and User-Cost Evaluation of Work Zones (QUEWZ) developed by the Texas Transportation Institute that can be used for estimating the traffic impacts of work zone lane closures. This tool can calculate the work zone capacity based on various input data such as hourly traffic volumes, percentage of trucks, capacity values under normal conditions, lane closure hours, work zone configuration, etc. It can also provide a traffic adjustment for demand values based on the alternate routes. The equations are based on HCM, 2000. QUEWZ is capable of analyzing traffic conditions on freeway segments with and without lane closures and providing estimates of additional road user costs and queuing as a result of work zone lane closures (Copeland, 1998). CA4PRS Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) is an analysis software platform that facilitates the integrated study of project alternatives for multiple solutions for pavement planning, logistics of construction, and traffic operations. CA4PRS was developed by the University of California, Berkeley, Institute of Transportation Studies, with technological assistance from the Turner-Fairbank Highway Research Center of FHWA. For alternate rehabilitation methods, including pavement cross-sections and material styles, construction windows and lane-closing techniques, and contractor logistics and restrictions, CA4PRS users may analyze "what if" scenarios. Moreover, CA4PRS can estimate the maximum distance of E-1

highway that can be rehabilitated or reconstructed within various resource constraints and closure timeframes (Jacoby et al. 2008). HCM-Based Tools Highway Capacity Manual (HCM) could be utilized for assessing the capacity and quality of service for current conditions of highway facilities, as well as the effects of temporary conditions such as work zones and future conditions such as proposed improvement projects. HCM is able to address the correct application and implications of micro-simulation analysis; offers basic methods and generalized volume tables of service provides an interactive multimodal approach to urban street analysis; and explores how demand and capability can be influenced by successful traffic management. Highway Capacity Software (HCS) contains some modules for the implementation of animation and simulation of both freeways and intersections (FHWA, 2019). FREEVAL Freeway evaluation (FREEVAL) is a computerized method intended to diligently execute computations for undersaturated and oversaturated directional highway installations for organizational study. FREEVAL is a powerful tool based on the Highway Capability Manual for macroscopic freeway research (HCM). FREEVAL-WZ FREEVAL-WZ is a version of FREEVAL-2015e which is specifically designed to have a user-friendly environment that applies to work zone study planning level approach. The program aims to have the ability to easily test the results of various work zone conditions and to calculate the effects over time and space of congested times (Trask, 2016). FREEVAL-NC FREEVAL-NC is the unique variant of FREEVAL for North Carolina intended to have a user-friendly environment that applies an approach to the planning stage for the work zone and reliability review of the freeway. Additionally, FREEVAL-NC has access to the segmentation index of the entire North Carolina highways. The program aims to have the opportunity to rapidly test the results of multiple work zone conditions and to measure the effects of congested cycles over time and space with the ability to expand the reliability study over the year (Trask et al., 2019). FREEVAL-PA FREEVAL-PA is a tool developed by Pennsylvania DOT that facilitates all forms of freeway research, helping PennDOT workers and contractors to easily gain meaningful E-2

information on what traffic effects they will see on every limited-access highway facility in the state from various forms of construction and maintenance programs. The software tool, along with traffic volume and speed data, weather data, elevation data, and a computing engine operated by the HCM, is programmed with the roadway characteristics of each highway section in Pennsylvania to understand the effect of decision-making on capacity. The concept of this tool is from the FREEVAL which has been programmed in North Carolina, early 1990s (FREEVALPA, 2020). STREETVAL STREETVAL, a similar tech application, permits analysis such in FREEVAl for arterial corridors. In order to create a travel time distribution from which reliability metrics can be derived, STREETVAL utilizes a scenario generator that feeds the Urban Streets Highway Power Analysis methodology. The technique of reliability can be used to determine the following causes of unreliable travel time on urban road services, including traffic accidents, work zones, variations in demand, special events, inadequate base capacity, etc. The input data expected to be analyzed using the HCM technique for an urban street facility (―Urban Street Reliability Engine User Guide‖, 2014). Spreadsheets Based on the literature, most of the state DOTs nationwide are using spreadsheet-based tools to evaluate the impacts of work zones. Using the graphical procedure outlined in HCM, along with analytical equations, most of the tools estimate the output (delay and queue lengths). Inputs are usually containing vehicle demand for every time interval, number of open lanes, roadway capacity, percentage of trucks, etc. CORSIM CORSIM is a software package for microscopic traffic modeling for signal systems, highway systems, freeway systems, or the combination of all. It is originally developed by FHWA and has been used for around 30 years. CORSIM is capable of adapting to simulate traffic operations around a work zone on the arterials in urban areas, suburbs, and towns. It can also simulate the work zones through a prolonged incident blockage, the program assumes that drivers have no knowledge of the approaching blockage and there is no taper. CORSIM results are not sensitive to the work zone length or the differentiating between the effects of right or left lane closures. In addition, CORSIM is not able to consider some of the geometric elements such as lane width and shoulder width (Heaslip et al., 2009). VISSIM

E-3

VISSIM as one of the most popular microsimulation tools could be used for freeway analysis especially conducting research for work zone study. VISSIM as one of the most popular microsimulation tools could be used for freeway analysis especially conducting research for work zone study. VISSIM requires calibration and validation processes before providing meaningful simulation results. With a systematic procedure for calibration of a VISSIM model capacity, speed, and queue length could be obtained for work zone analysis. One study regarding the calibration of VISSIM for freeway work zones has concluded that if field speed data is input for desired speed distribution, VISSIM does not reproduce the data for single-lane freeway work zones (Kan et al., 2014).

E-4

Swanson School of Engineering Department of Civil and Environmental Engineering IRISE Consortium 742 Benedum Hall 3700 O’Hara Street Pittsburgh, PA 15261

The information printed in this document was accurate to the best of our knowledge at the time of printing and is subject to change at any time at the University’s sole discretion. The University of Pittsburgh is an affirmative action, equal opportunity institution.

Early Opening of Concrete Pavements to Traffic

Articles inside

Table 11: Cost of Delay Caused by Work-Zone for Pittsburgh Downtown Network

Figure 35: Total User Cost of Delay for Downtown Pittsburgh

Table 10: Occupancy rates

Figure 33: Street chosen for closure (7th Street from Fort Duquesne to Penn Ave

Figure 34: Hourly Volume Percentage Changes – PennDOT

Figure 32: Downtown Pittsburgh VISSIM model for PM peak hour

Figure 31: The Sources of Congestion National Summary - FHWA https://ops.fhwa.dot.gov/aboutus/opstory.htm

Figure 30: The grouping of PennDOT regional offices with similar climate conditions. PennDOT, 2021

Table 2: Mixture design details

Table 6: Example set of strength data collected from Golden Triangle Construction Company

Figure 23: Example shear wave velocity- strength curves

Table 1: Strength criteria from PennDOT 408 (2021

Figure 29: Results provided by the web-tool

Figure 20: Concrete strength estimation for slab C1.1 two days after construction using the modified shear wave velocity