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Volatile Organic Compounds Generated in Asphalt Pavement Construction and Their Health Effects on Workers Dan Chonga; Yuhong Wang, P.E.b; Hai Guoc; Yujie Lu, A.M.ASCEd a Ph.D.

Candidate, Dept. of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong Email: dan.chong@connect.polyu.hk b Assistant Professor, Dept. of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong c Associate Professor, Dept. of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong d Assistant Professor, Dept. of Building, National University of Singapore, Singapore

Introduction

Analysis Results

The unique characteristic of hot-mix asphalt (HMA) pavement construction is that its placement and compaction have to be conducted at elevated temperature, massive amount of volatile organic compounds (VOCs) are generated. The VOCs can affect human health in both acute and chronic ways through inhalation and skin contamination. Most existing studies focused on the condensates in the asphalt fumes but not on VOCs. The connection between the pavement workers’ exposure to VOCs and the pavement construction process has not been adequately addressed in the existing studies.

Dominant VOCs Species Chromatographic data were derived from GC/MSD in Fig. 2. 28 chemical compounds were identified from the total 37 air samples. Dominant VOCs species were summarized in Table 2.

Research Method Identification of Typical Projects for Sampling

Samples were collected from five HMA resurfacing projects in Hong Kong shown in Table 1.

Table 2 Top Five Chemical Components and concentrations

Fig.2 VOCs chromatogram from GC/MSD

Temporal-Spatial Variations of VOCs The change of VOCs concentration with time can be examined by the VOCs variation in ESPs samples, Fig. 3 indicates that the VOCs concentrations generally decline quickly as time elapses. While Figure 4 demonstrates that VOCs concentrations during filling paver hopper are generally higher than those during paving.

Sample Collection VOCs emissions are generated in the paving and compaction stages. Air samples were collected at various time points and locations, as illustrated in Fig. 1. The time points include the beginning of paving, 4 min after paving, beginning of compaction, 5 min after compaction, and 8 min after compaction. The collection locations include the emission source points (ESPs), workers breathing zones (WBZs), paver hopper (PH), and background (B).

Fig.3 and 4 Trend of VOCs concentrations at ESPs (left) and WBZs (right)

Assessment of Occupational Health Risks The health risk of pavement workers is assessed on the basis of the laboratory test results of VOCs and widely acknowledged toxicological standards. The concept of hazard quotient (HQ) is used. Table 3 Recommended or Regulated Exposure Limits of Dominant VOCs

Fig.1 Sampling locations during the paving and compaction stages

Laboratory Analysis The characterization of VOCs species were analyzed by following U.S. EPA method TO-14 with an HP 6890/5973 GC/MSD (Gas chromatography coupled with mass selective detector) equipped with a TDS (Thermal Desorption Spectroscopy) thermal desorption apparatus. The VOCs were identified from the mass spectra and quantified by multipoint calibration.

Mitigation Opportunities On the basis of the findings of this research, three approaches are recommended to reduce exposures to on-site workers, namely, emission source control, intervention in the VOCs propagation path, and receptor protection. Acknowledgments The authors are grateful to the Highways Department of Hong Kong for supporting this study and to the Hong Kong Polytechnic University for an internal funding support.


Develop a Price Escalation Method for Single Award Indefinite Delivery/ Indefinite Quantity Contracts: AxE Bidding Author: Jorge A. Rueda

Advisor: Douglas D. Gransberg PhD, PE

PhD Candidate – Graduate Assistant Iowa State University jrueda@iastate.edu

ANALYSIS OF EXISTING ESCALATION METHODS CONSTRUCTION COST INDEXES

ABSTRACT As a result of a comprehensive research conducted for the Minnesota Department of Transportation (MnDOT) on the current Indefinite Delivery/Indefinite Quantity (IDIQ) practices adopted by different transportation agencies across the US, it has been identified a major issue to be addressed before MnDOT can fully implement IDIQ contracting: cost escalation in multi-year single award IDIQ contracts. This study introduces a new escalation methodology and terms it: Cost times Escalation (AxE) bidding. It seeks to eliminate the need to depend on external construction cost indices or to develop a MnDOT construction cost index by shifting the escalation risk to the contractor during bidding and allowing it to propose its own escalation adjustment factor. The proposed process requires competing contractors to submit a fixed annual adjustment rate, which will be used to modify bid unit prices over time throughout the IDIQ contract’s life cycle. The adjustment rate is also factored into the selection of the low bid in a manner similar to A+B bidding. This study evaluates different alternatives to incorporate this rate into the selection of the successful contractor (formulas for E) and quantifies the risk related to each alternative for different case scenarios. Additionally, AxE bidding is expected to reduce construction costs and agency staffing requirements, as well as overcome some disadvantages associated with using traditional price escalation methods, such as the lack of flexibility to adapt to the nature of the contract and the inability to consider imminent future changes in the construction industry. This study also presents an analysis of traditional price escalation methods by applying twelve different cost escalation indexes, and one alternative method currently used by MnDOT on its IDIQ contracts, on four case study projects over a five-year period. Outcomes from each index were compared with observed bid prices along the same period of time. A complete analysis of these traditional price escalation methods and historical bid data were used by the authors as a reference to develop an escalation method that meets MnDOT needs.

Donald & Sharon Greenwood Endowed Professor of Construction Engineering Iowa State University

Common Assumptions:  Changes in the construction market from period to period have equal or similar impact on all kind of construction projects.  Weighted price changes between construction periods in few significant materials or construction components represent the overall construction cost change during the same period of time.  Steady quality and production rates over time in construction materials and activities.  Construction prices for the oncoming period follow a trend marked between the base period and the last period with known index Case Study Projects: $2.4 $2.2 $2.0

Asphalt Pavement Concrete Pavement Traffic Barriers Drainage

$1.8 $1.6 $1.4 $1.2

CONCLUSIONS

AxE Bidding

Actual Prices vs. Adjusted Prices: Average Variation (+/-)

Cost Indexes Building Construction RSMeans (National) ENR-BCI (National) Average Highway Construction ENR-CCI (National) BLS-PPI NHCCI Caltrans (Quarterly) Caltrans (12-M) SDDOT Average Minnesota & Minneapolis RSMeans (Minneapolis) ENR-BCI (Minneapolis) ENR-CCI (Minneapolis) MnDOT Average

Asphalt Concrete Traffic Drainage Average Pav. Pav. Barriers 18.82% 18.76% 18.79%

7.93% 8.07% 8.00%

6.44% 10.25% 8.34%

10.83% 10.28% 10.56%

11.00% 11.84% -

17.20% 26.98% 33.83% 30.12% 27.06% 16.96% 25.36%

7.72% 16.54% 25.16% 19.96% 17.59% 6.48% 15.58%

11.07% 10.62% 20.94% 26.47% 20.56% 12.38% 17.01%

9.30% 17.52% 26.41% 21.90% 18.94% 8.15% 17.04%

11.32% 17.91% 26.58% 24.61% 21.04% 10.99% -

18.33% 19.96% 20.34% 18.09% 19.18%

7.63% 9.40% 9.46% 5.50% 8.00%

11.02% 9.96% 10.26% 12.92% 11.04%

10.61% 10.76% 11.21% 10.19% 10.69%

11.90% 12.52% 12.82% 11.68% -

AxE BIDDING - RISK ANALYSIS Probability of Awarding to Firm 1 Bid Firm 1 > Bid Firm 2 - Adjust. Rate Firm 1(r1) < Adjust. Rate Firm 2(r2) Adjustment Weighted Sum BS 2 BS 3 Sum BS 1-3 Rates BS 1-3 A(1 + r) A(1 + r)^2 A(r^2 + 3r + 3) r1 r2 A(0.1r^2 + 0.4r + 1) 0% 8% 31% 59% 31% 12% 0% 6% 24% 47% 24% 9% 0% 4% 16% 31% 16% 5% Risk Ranges in which Firm 1 Wins the Contract 0% 8% 0%-8.0% 0%-16.6% 0%-8.0% 0%-3.1% 0% 6% 0%-5.9% 0%-12.4% 0%-5.9% 0%-2.2% 0% 4% 0%-4.0% 0%-8.0% 0%-4.0% 0%-1.4%

ď&#x201A;§ AxE bidding provides great flexibility to adjust unit prices in single award IDIQs in accordance with the specific characteristics of each contract and considering imminent changes in the construction industry. Flexibility that is missing in traditional price escalation methods. ď&#x201A;§ Besides maintaining low agency administrative requirements, AxE bidding increases contractorsâ&#x20AC;&#x2122; confidence in understandable, fair, and transparent escalation clauses. ď&#x201A;§ AxE bidding is expected to reduce bid unit prices by reducing contractorsâ&#x20AC;&#x2122; perceived uncertainty. ď&#x201A;§ A selection formula using a weighted sum of Bid Schedules is shown as the less risky alternative.

â&#x20AC;˘ Components A = Unit Price Extensions â&#x20AC;˘ E = Escalation Multiplier

Example: BID SCHEDULE (BS) Bidder 1

Pay Quantity Items

Bidder 2

Unit Unit Extension Extension Price Price

Item X

11,631

$0.63

$7,328

$0.90

$10,468

Item Y

1,479

$64.55

$95,469

$64.00

$94,656

Item Z

1,530

$2.72

$4,162

$2.82

$4,315

Total Bid

$106,959

A

Escalation Rate

$109,439

E

AxE

Bidder 1 $106,959

8%

1.03% $110,450

Bidder 2 $109,439

2%

1.01% $110,319*

* Bidder 2 wins â&#x20AC;˘ Escalation Multiplier (E) Preliminary Formula Proposed to MnDOT: đ?&#x2018;Ź = đ?&#x;&#x17D;. đ?&#x;?đ?&#x2019;&#x201C;đ?&#x;? + đ?&#x;&#x17D;. đ?&#x;&#x2019;đ?&#x2019;&#x201C; + đ?&#x;? r = Escalation rate bid by Contractors â&#x20AC;˘ AxE bidding quantifies uncertainty, which should enhance cost certainty. â&#x20AC;˘ Proposed equation is applicable to single award IDIQ contracts executed by MnDOT only.


In lieu of traditional DES validation and verification tools that require a-priori information of activities, Robots Simulators are employed

DES Model

Robot simulators allow for the encapsulation of performance and equipment characteristics with the 3D model

Virtual model of operation using DES is used for analyzing operation

Data Input Input to DES model traditionally include activity durations, equipment characteristics etc.

Model is traditionally verified and validated using trace-file driven or concurrent animation

This allows for DES to command virtual equipment to perform without need for duration data

Output of DES model Whereas traditional model results need to be interpreted by human operatives, DES issues commands to autonomous equipment depending on operational logic of model

Robots communicate back to model when assigned task is complete and model progresses further

Real World Operation Operational logic is studied and modeled using DES initially Subsequently performed by autonomous robots on worksite

Earthmoving performed in lab setting with trace file orchestrated model robots

Fig 2: Case study with DES trace file

Future step to integrate DES with operation with robot feedback

Real time contextualized communication from robots allows for automated real time progress monitoring Generic methodology for automation of any operation if robots can perform activities in the operation

Enabling remote construction in extra-terrestrial, underwater, hazardous environments Novel visualization method that relieves operation modeler from low level geometric concern of work site and equipment Figure 1: Modified DES Processing Algorithm

Comprehensive methodology for unprecedented use of DES for construction automation


Managing Water  and  Wastewater   I nfrastructure   i n   S hrinking   C i6es   1 2   Kasey  M.  Faust ,  Dulcy  M.  Abraham

Increasing per capita costs for infrastructure Fig. 1.  Infrastructure  cycle  in  shrinking  ci5es  

Pressure (psi)

0 Tools: ArcGIS,  EPANET   Inputs:     • Network  characterisJcs   (e.g.,diameter,locaJon)   • Demand   • Water  use  trends  for   different   socioeconomic  statuses   • Decommissioning   scenarios   Outputs:     • Ability  to  provide   adequate  pressure   • Ability  to  provide   emergency    fire  flows  

Disruptors: • Changes  in  water  demand   • Changes  in  wastewater   produced   • ConsolidaJon  of  demand   • Removal  of  pipelines   servicing  vacant  areas   Unique  Water  Disruptors:   • Removal  of  redundancies   Unique  Wastewater   Disruptors:   • TransiJoning  land  uses   • Decrease  in  impervious   surfaces  

Fig. 3.  Methodology  

Tools: ArcGIS,  L-­‐THIA,   SWMM   Inputs:       • Current  land  use   • Soil  and  land   characterisJcs   • Wastewater  produced   • PrecipitaJon   Outputs:     • Total change in runoff and infiltration • % change in runoff • Financial return on alternative • Comparison of tools

4

8

12 16 20 24 Hour Fig. 5(a)

0

4

8

12 16 20 24 Hour Fig. 5(b)

Fig. 5.  Water  infrastructure  scenario  4  pressure  results  (a)  Single-­‐ family  water  use  trend,  (b)  Low-­‐income,  single-­‐family  water  use  trend   0.50  

Runoff coefficient  =   Runoff/Precipita5on  

0.40 0.30   0.20   0.10   0.00  

100% 80%   60%   40%   20%   0%  

33% Impervious   28%    Impervious   23%  Impervious   18%  Impervious   13%  Impervious   8%  Impervious   3%  Impervious   Grass  and  pasture   Woods  

Utility companies operating under fiscal constraints

Infrastructure management opportunities to potentially: •  Break the cycle •  Reduce or stabilize costs •  Improve service •  Aid in meeting regulations

Analysis of  Wastewater   Infrastructure  

100 80 60 40 20 0

Decrease in  Runoff  From  Base   Model  (%)  

Urban Decline

Decreasing tax bases and customers

Analyses of  Water   Infrastructure  Network  

38% Impervious   33%  Impervious   28%    Impervious   23%  Impervious   18%  Impervious   13%  Impervious   8%  Impervious   3%  Impervious  

OBJECTIVES   (1)  Evaluate  the  impact  of  decommissioning  underuJlized  water   infrastructure   (2)  Evaluate  the  impact  of  decommissioning  impervious  surfaces  on   the  quanJty  of  runoff  produced    

Urban decline  resul6ng  in   underu6lized  infrastructure   services  

100 80 60 40 20 0

Runoff Coefficient  

MOTIVATION •  Shrinking  ciJes  naJonwide,  many  of  which  are  located  in  the   Midwest,  have  experienced  extreme  populaJon  loss   •  The  fixed  costs  of  infrastructure  operaJons  remain  (75-­‐80%  of   total  cost)  in  spite  of  declining  populaJons  and  tax  bases   •  As  populaJon  declines,  the  per  capita  cost  for  service  increases   •  Maintaining  criJcal  infrastructure  at  original  levels  of  operaJon   becomes  unsustainable    

Pressure (psi)

1 PhD  Candidate  (faustk@purdue.edu),    2  Major  Professor,    Lyles  School  of  Civil  Engineering,  Purdue  University  

Scenarios (1)  and  (2)    

               Fig.  6(a)                                Fig.  6(b)   Fig.  6.  Select  wastewater  infrastructure  decommissioning  scenarios   (solid  line:  SWMM  results,  dashed  line:  L-­‐THIA  results)  (a)  runoff   coefficients,  (b)  %  decrease  in  runoff  coefficient  between  scenarios  

Scenario (3)    

RESULTS

 Fig.  2(b)  

 Fig.  2(a)  

*Candidate areas   are  residen5al  land   use.  Green  parcels     are  vacant  as  of   2010.     Fig.  2.  Candidate  areas  (a)  Flint,  MI,  (b)  Saginaw,  MI   CASE  STUDIES  

Flint, MI  (Fig.  2a)  is  a  medium  city,  peaking  at  196,940  people.   Saginaw,  MI  (Fig.  2b)  is  a  small  city,  peaking  at  98,265  people.   Flint  and  Saginaw  have  declined    43.4%  and  47.5%,  respecJvely.    

Scenario (4)    

(1) Center 18” diameter pipe decommissioned (2) Top and center 18” diameter pipe decommissioned (indicated by large dashed lines) (3) Center 18” diameter and select 6” diameter pipes removed (indicated by large and small dashed lines, respectively) (4) Top and center 18” diameter and select 6” diameter pipes removed (indicated by large and small dashed lines, respectively) Fig. 4.  Select  water  infrastructure  decommissioning  scenarios   modeled  in  Flint,  MI  

ACKNOWLEDGEMENT This  material  is  based  upon  work  supported  by  the  NSF  Graduate  Research  Fellowship,  and  reflect  the  views  of  the  authors.  

Water Infrastructure   •  Adequate  pressures  and  fire  flows  are  maintained  for   decommissioning  pipelines  12”  and  less  in  diameter     •  The  socioeconomic  status  of  the  area  must  be  considered  for   pipelines  over  12”  diameter  due  to  the  peak  usage  Jme(s)   Wastewater  Infrastructure   •  Decommissioning  impervious  surfaces  and  transiJoning  land   uses  can  significantly  reduce  runoff   •  L-­‐THIA  understates  the  quanJty  of  runoff,  however  yields   comparable  percent  change  in  runoff  


A Bio-inspired Solution to Mitigate Urban Heat Island Effects Yilong Han (ylhan@vt.edu) Advisor: Prof. John E. Taylor Network Dynamics Lab, Virginia Tech MOTIVATION

KEY QUESTIONS

To identify a solution for reducing Inter-Building Effects (IBE) that negatively impact energy consumption and lead to Urban Heat Islands (UHI) effects.

Q1: Does the retro-reflective property play a role in retaining cooler intra-areas? Q2: If building envelopes are made of retro-reflective materials, would the IBE that negatively impacts energy consumption and leads to UHI effects be reduced? Global Environmental Deterioration [1]

METHOD & FINDINGS Energy Consumption [2-3]

Inter-Building Effects [7]

Urbanization [4]

Urban Heat Islands [5-6] 7am

BACKGROUND

9am

11am Control Building Energy Performance Differences by City/Region

Surface Temperature Differences Distribution

 Biomimicry in engineering and architecture • “Design looking to biology” • Biological analogies of building systems

[8]

9000

9000

9000

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7000

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6000

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-0.2

-0.8

-1

-1.2

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Average: -0.34 °C

 Flowers exhibit a similar heat island effect, namely “micro-greenhouse effect” [9-10].

 A cooling effect has been observed in a peculiar temperate flower, Galanthus nivalis (snowdrop) [11].  Special reflectance property of snowdrop’s shiny petal has been suggested as a possible contributor [11] .  Retro-reflection is a phenomenon that light rays strike a surface and are redirected back to the source.

6000

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-0.99 °C

Building surface temperatures dropped considerably when neighboring buildings were retrofitted with our retro-reflective façade surface.

Total energy consumption and cooling energy consumption were reduced by up to 8.2% and 9.8%, respectively, in different metropolitan areas.

CONCLUSIONS & IMPLICATIONS Application of retro-reflective property on building envelopes in spatially proximal buildings can:

 Reduce surface temperatures and help mitigate Urban Heat Island effects.  Reduce the energy required for cooling and, therefore, total energy consumption.

REFERENCES: [1] Perez-Lombard, L., Ortiz, J., and Pout, C. (2008). "A review on buildings energy consumption information." Energy and buildings, 40(3), 394-398. [2] International Energy Agency (2013). "Key World Energy Statistic." International Energy Agency. [3] U.S. Department of Energy (2011). "Building Energy Data Book." [4] UN DESA (2011). "World Urbanization Prospects, the 2011 Revision." United Nations. [5] Oke, T., Johnson, G., Steyn, D., and Watson, I. (1991). "Simulation of surface urban heat islands under ‘ideal’ conditions at night Part 2: Diagnosis of causation." Boundary-Layer Meteorology, 56(4), 339-358. [6] Rizwan, A. M., Dennis, L. Y., and Liu, C. (2008). "A review on the generation, determination and mitigation of Urban Heat Island." Journal of Environmental Sciences, 20(1), 120-128.

[7] Pisello, A. L., Taylor, J. E., Xu, X., and Cotana, F. (2012). "Inter-building effect: Simulating the impact of a network of buildings on the accuracy of building energy performance predictions." Building and Environment. [8] Zari, M. P. (2007). "Biomimetic approaches to architectural design for increased sustainability." School of architecture, Victoria University, NZ. [9] Rands, S. A., and Whitney, H. M. (2008). "Floral temperature and optimal foraging: is heat a feasible floral reward for pollinators?" PLoS One, 3(4), e2007. [10] Corbet, S. A. (1990). "Pollination and the weather." Israel Journal of Botany, 39(1-2), 13-30. [11] Rejšková, A., Brom, J., Pokorný, J., and Korečko, J. (2010). "Temperature distribution in light-coloured flowers and inflorescences of early spring temperate species measured by Infrared camera." Flora-Morphology, Distribution, Functional Ecology of Plants, 205(4), 282-289.


Vision-based Building Energy Diagnostics and Retrofit Analysis using 3D Thermography & BIM Youngjib Ham (yham4@illinois.edu), PhD Candidate, University of Illinois at Urbana-Champaign (Advisor: Mani Golparvar-Fard) 4. Cost-benefit Analysis of Building Energy Efficiency Retrofits

â&#x20AC;˘ Need for energy diagnostics and retrofit analysis of existing buildings

EPAR modeling

ď&#x201A;§ About 150 billion S.F. (roughly half of the U.S. building stock) over the next 30 years

Equipment

Computer vision-based 3D point cloud and mesh modeling

Quantifying as-is thermal resistances

Labor

Material

Estimating cost for building energy efficiency retrofits

Thermal imagery Actual R-values at 3D vertices in EPAR model

Expected 3D Thermal Mesh Model

Areas with potential problem

Numerical analysis

Analyzing performance deviation

Visual inspection based on surface temperatures such as hot or cold spots Lack of energy performance benchmark and associated energy cost analysis

ď&#x201A;§ ď&#x201A;§

ď&#x201A;§ ď&#x201A;§

Importing notional properties declared in the specifications or industry standard databases Lack of consideration about diminishing thermal resistance caused by deteriorations

Estimating energy saving by improving R-values Degree days statistics

BIM-based energy analysis đ?&#x2018;&#x2018;đ?&#x2018;&#x201E; 1 = Ă&#x2014; đ??´ Ă&#x2014; â&#x2C6;&#x2020;đ?&#x2018;&#x2021; đ?&#x2018;&#x2018;đ?&#x2018;Ą đ?&#x2018;&#x2026;

đ?&#x2018;&#x201E;đ?&#x2018;&#x2020;đ?&#x2018;&#x17D;đ?&#x2018;Łđ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018;&#x201D; =

Costbenefit analysis

The amount of unnecessary heat transfer

1 đ?&#x2018;&#x2026;đ??´đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;˘đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;

Calculating the corresponding energy cost

Recommended R-values

â&#x2C6;&#x2019;

R-value recommendation Cooling Degree Days

Outside Temperature

Digital imagery

Actual3D Thermal Mesh Model

Retail price of energy

Comfort Zone Heating Degree Days Time

1

Ă&#x2014; đ??´đ?&#x2018;&#x192; Ă&#x2014; đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019; â&#x2C6;&#x2019; đ?&#x2018;&#x2021;đ?&#x2018;&#x153;đ?&#x2018;˘đ?&#x2018;Ąđ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019; Ă&#x2014; đ?&#x2018;Ą

đ?&#x2018;&#x2026;đ?&#x2018;&#x2026;đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;&#x161;đ?&#x2018;&#x161;đ?&#x2018;&#x2019;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018;

2. Research Objective â&#x20AC;˘ Create and validate an easy-to-use tool and automated diagnostics and analysis methods based on 3D thermography and BIM ď&#x201A;§ Support reliable cost-benefit analysis of energy efficiency building envelope retrofits ď&#x201A;§ Improve the reliability of BIM-based building energy performance analysis

11°C

Actual Measurement

3. Mapping of Actual Thermal Properties to BIM Elements đ?&#x2018;&#x2DC; 1 đ?&#x2018;&#x2DC; đ?&#x2018;&#x203A;=1 đ?&#x2018;&#x2026; đ?&#x2018;&#x203A;

Outside Air Film

Digital & thermal images

3D spatio-thermal point cloud

Plaster Fiberglass Batt & Stud Work

Plywood Sheathing & Wood Siding

A single R-value for building elements

Inside Air Film

Updated gbXML-based BIM đ?&#x2018;&#x201E;đ??śđ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018;Łđ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;Łđ?&#x2018;&#x2019; = đ?&#x203A;źđ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;&#x203A;đ?&#x2018;Łđ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;Łđ?&#x2018;&#x2019; Ă&#x2014; đ??´ Ă&#x2014; đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;,đ?&#x2018;&#x17D;đ?&#x2018;&#x2013;đ?&#x2018;&#x; â&#x2C6;&#x2019; đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;,đ?&#x2018;¤đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;

đ?&#x2018;&#x2018;đ?&#x2018;&#x201E; 1 = Ă&#x2014; đ??´ Ă&#x2014; â&#x2C6;&#x2020;đ?&#x2018;&#x2021; đ?&#x2018;&#x2018;đ?&#x2018;Ą đ?&#x2018;&#x2026; R=

R-Values (m^2K/W)

1.3 1.2

â&#x2C6;&#x2020;đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019; đ?&#x2018;&#x17D;đ?&#x2018;&#x203A;đ?&#x2018;&#x2018; đ?&#x2018;&#x153;đ?&#x2018;˘đ?&#x2018;Ąđ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019; đ?&#x203A;źđ?&#x2018;?đ?&#x2018;&#x153;đ?&#x2018;&#x203A; Ă&#x2014; đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;,đ?&#x2018;&#x17D;đ?&#x2018;&#x2013;đ?&#x2018;&#x; â&#x2C6;&#x2019; đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;,đ?&#x2018;¤đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122; + đ?&#x153;&#x20AC; Ă&#x2014; đ?&#x153;&#x17D; Ă&#x2014;

â&#x2C6;&#x2019;

4 đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;,đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x201C;đ?&#x2018;&#x2122;đ?&#x2018;&#x2019;đ?&#x2018;?đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x2018;

1.1 0.9 0.7 0.5

1.1 1 0.9

0.7

gbXML Campus

Location

Construction

Building

Layer

Surface

Schedule

0.6 0.5

Association of the measurement to the corresponding building element

Material

0.8

Centroids of faces in the meshed BIM for exterior building components â&#x20AC;&#x201C; measurements conducted from the interior side

5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

Heating

Zone .. .

PlanarGeometry PolyLoop

U-value .. .

CartesianPoint Coordinate

Exporting gbXML file from BIM

â&#x20AC;˘ Input of BIM-based energy analysis

ď&#x201A;§ ď&#x201A;§

Cooling

40

Accumulation

35 30 25 20 15 10 5 0 1

4 4 đ?&#x2018;&#x201E;đ?&#x2018;&#x2026;đ?&#x2018;&#x17D;đ?&#x2018;&#x2018;đ?&#x2018;&#x2013;đ?&#x2018;&#x17D;đ?&#x2018;Ąđ?&#x2018;&#x2013;đ?&#x2018;&#x153;đ?&#x2018;&#x203A; = đ?&#x153;&#x20AC; Ă&#x2014; đ?&#x153;&#x17D; Ă&#x2014; đ??´ Ă&#x2014; đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;,đ?&#x2018;¤đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122; â&#x2C6;&#x2019; đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;,đ?&#x2018;&#x;đ?&#x2018;&#x2019;đ?&#x2018;&#x201C;đ?&#x2018;&#x2122;đ?&#x2018;&#x2019;đ?&#x2018;Ąđ?&#x2018;&#x2019;đ?&#x2018;&#x2018;

4 đ?&#x2018;&#x2021;đ?&#x2018;&#x2013;đ?&#x2018;&#x203A;đ?&#x2018; đ?&#x2018;&#x2013;đ?&#x2018;&#x2018;đ?&#x2018;&#x2019;,đ?&#x2018;¤đ?&#x2018;&#x17D;đ?&#x2018;&#x2122;đ?&#x2018;&#x2122;

Monthly and accumulated cost for expected energy saving (Target R-value: 2.64m2K/W)

An existing residential building built in 1970â&#x20AC;&#x2122;s

Accumulated saving cost ($)

1đ?&#x2018;&#x161;2 đ??ž/đ?&#x2018;&#x160;

Thermal resistances at 3D vertexes

Potential Performance Problem (Threshold: 2°C)

Expected Simulation

5. Experimental Results and Conclusions

đ?&#x2018;&#x2026;đ?&#x2018;Ąđ?&#x2018;&#x153;đ?&#x2018;Ąđ?&#x2018;&#x17D;đ?&#x2018;&#x2122; =

3D spatio-thermal mesh model

27°C

Monthly energy saving cost ($)

0.6 đ?&#x2018;&#x161;2 đ??ž/đ?&#x2018;&#x160;

10°C

22°C

2

3

4

5

6 7 Month

8

9

10

11

Type of Space Conditioning

Source of Energy

Retail Price of Energy+ (per kWh)

Degree Days++

Expected Saving Cost (per year)

Heating

Natural gas

$0.069

3734

$21.0

Cooling

Electricity

$0.116

1326

$12.34

12 Total Saving Cost (per year)

Scientific Contribution â&#x20AC;˘ Measuring actual thermal resistances of building assemblies in 3D â&#x20AC;˘ Automated association of actual thermal property measurements to building elements in BIM â&#x20AC;˘ Updating their corresponding thermal properties in gbXML schema of BIM

Practical Significance â&#x20AC;˘ Leveraging the findings of this research and the developed tools to create new workflow for ď&#x201A;§ building commissioning â&#x20AC;&#x201C;particularly for LEED certified buildings ď&#x201A;§ energy efficiency retrofit assessment processes during thermographic inspection

$33.34

Real-Time and Automated Monitoring and Control (RAAMAC) Lab

1. Motivation

Posterpresentationbinder abourizk  
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