Page 1

2014 Construction Research Congress PhD Student Poster Presentations Volume I. A to H Sponsored by CRC Executive Committee Edited By Mani Golparvar-Fard, Ph.D., University of Illinois at Urbana-Champaign Youngjib Ham, University of Illinois at Urbana-Champaign

This proceedings is invaluable to all practitioners and researchers in the field of construction engineering and management.


RFID and BIM-Enabled Worker Location Tracking to Support Real-time Building Protocol Control and Data Visualization on a Large Hospital Project Aaron Costin School of Civil and Environmental Engineering

1. Introduction

4. Experimental Engineering Design

As construction sites get larger and more complex, the need to increase building protocol control and security is becoming more necessary. • Difficult maintaining site safety and security • Issues with the sequencing of trades • Increase in risks and disruptive work • Higher chances of reduced quality

Hardware and Equipment • RFID badge (left) • M6 RFID reader (top) • RFID badge printer (bottom) • RFID antennas (right)

2. Background The University of California San Francisco’s (UCSF) Medical Center at Mission Bay • 3 major buildings • 900,000 square feet • 125 contractors • 1,200 workers

Site Layout and Access • Designated into 3 work zones. • Approximately 80 UHF-RFID tag readers were installed in the infrastructure • Each person who enters the site would register and receive a RFIDenabled identification badge with breakaway lanyard • All construction personnel are required to wear the badge plainly visible • RFID-enabled turnstiles were placed at site entrance

aaron.costin@gatech.edu

5.Results Real-Time Resource Tracking and Safety Monitoring

Data Analysis and Code Compliance Check 1000

Trying to stay on schedule

Radio Frequency Identification (RFID): • Communication via radio waves • Passive RFID tags: lifetime longevity, inexpensive, and most suitable for indoors

Non SF

900 800

Man-Hours

3. Approach Integration of Building Information Modeling (BIM) and Radio Frequency Identification (RFID) technology • Real time tracking of materials, equipment, and personnel in the BIM model • Real time estimation and scheduling to organize worker logistics Building information Modeling (BIM): • Integrates the geometric properties of the building’s 3D model with all the information and properties of the building • Allows a convergence and integration of systems to play a greater role in overall building performance

SF

700 600 500 400 300 200 100 0

Full-time equivalent workers per day per contractor

Number of worker hours per week

6. Observed Benefits

Building Protocol • A project build protocol was enacted for each work zone. • The protocol level was based on the safety, security, and cleanliness. • Different zones of the building may be under different levels of protocol during the same time period • Four levels of access protocol • Authorized workers need specific qualification and training for each level to enter.

Improved Safety and Security • Alerts when unauthorized person enters unauthorized zone • Locate employees in emergencies or evacuations • Real-time monitoring of work zones Verification of Ordinances and Regulations • Automated compliance checks • San Francisco OEWD trade regulations Improved Quality • Limit damage and rework of completed areas • Equipment protection More Effective Workforce • Improve worker logistics • Data analysis can provide worker demographics to achieve project goals • Automated check-in/check-out times Acknowledgements: • National Science Foundation (NSF) GRFP Fellow ID #2012136645 • DPR Construction • Trimble ThingMagic


DEVELOPING CONTEXT SPECIFIC & GENERALIZED CONSTRUCTION LABOUR PRODUCTIVITY MODELS BACKGROUND

Direct

Yi: Mediator or Moderator Variables

R1 : Input  Process xi : Independent Variable

Delay

R2 : Process  Output Z : Dependent Variable

Crew Size

65

Crew Composition

R3 : Input  Output

Labour Productivity

Work Scope Location R1 : Represents the impact of input variables on process variables

….etc. MANUFACTURING

Industrial context ranking

Building context ranking

Industrial context ranking

Negative influence

Positive influence

Negative influence

Positive influence

Negative influence

Positive influence

Negative influence

I. Labour and crew

9

7

12

6

3

7

1

6

II. Material and consumables

4

15

3

7

7

1

9

2

III. Equipment and tools

1

16

1

11

8

2

8

1

IV. Foreman

2

17

8

12

1

9

3

9

V. Task property

14

12

15

16

9

5

5

3

VI. Location property

11

9

7

4

5

4

4

5

VII. Project delivery and contract

15

6

16

17

*

*

*

*

VIII. Engineering and instructions

8

2

9

1

4

8

7

8

IX. Project complexity

17

8

17

15

*

*

*

*

6

10

2

14

2

3

2

7

XI. Project management practices

5

13

4

9

*

*

*

*

XII. Project best practices

7

11

5

10

*

*

*

*

XIII. Project owner nature

10

5

6

8

*

*

*

*

XIV. Management of project

*

*

*

*

6

6

6

4

XV. Organizational

3

14

10

13

*

*

*

*

XVI. Provincial

13

3

13

3

*

*

*

*

XVII. National

12

4

11

5

*

*

*

*

XVIII. Global

16

1

14

2

*

*

*

*

environment

OUTPUT VARIABLE

Trade survey

Positive influence

X. Health, safety, and

Weather

60

Building context ranking

Input Variables Category

PROCESS VARIABLES

INPUT VARIABLES

ALBERTA CONSTRUCTION LABOUR PRODUCTIVITY TRENDS

Support

RESULTS: KEY INPUT VARIABLES PARAMETER CATEGORY RANKINGS Project Management survey

Waiting, Travel, Personal

Preparatory, Tools and Equipment, Material Handling

Tool Time

Dr. Aminah Robinson Fayek Supervisor

DEVELOPING THE GENERALIZED CONSTRUCTION LABOUR PRODUCTIVITY (CLP) MODEL

MODEL VARIABLES & RELATION PATHS

Construction labour productivity (CLP) significantly impacts the success of projects. However, challenges in modeling it include: • Inherent requirement of dealing with numerous, complex, and continuous influencing input variables (productivity factors and practices) • Need to address objective and subjective nature of input variables in an integrated approach • Need for flexibility in adapting models to suit different project contexts • Reliance on large data sets for model development, testing, and training

Abraham Tsehayae PhD Candidate

[Based on Project Management and Trade surveys collected from three building (commercial and residential) and three industrial (power shutdown) projects. There are a total of 17 input variable categories in the Project Management survey and 9 categories in the Trade survey]

R2 : Represents the impact of process variables on output variable R3 : Represents the impact of input variables on output variable

RESULTS: WORK SAMPLING VARIABLES VS. PRODUCTIVITY

50

CONTEXT SPECIFIC LABOUR PRODUCTIVITY MODEL ARCHITECTURE

LABOUR PRODUCTIVITY MODEL INPUT VARIABLES

45

5.0

40

AGRICULTURE 35

P 30

3.5 3.0

Support W 5%

2.5

Support W 10%

2.0

Support W 15%

1.5

Support W 20%

1.0

Support W 25%

2002

2003

2004

2005

2006

2007

2008

25

2009

Direct = 75%

Support = 5%

Delay = 20%

0.0

CONSTRUCTION 20 2001

Optimal WS Values:

4.0

0.5

P

25

Concreting Activity

4.5 Productivity (m3/mhr)

Value Added ($) / Hours Worked

55

35

45 55 65 75 85 Direct Work Proportion (%)

95

2010 0.6 Productivitity (each/mhr)

Year

P [Adapted from “Labour productivity ($2002 per hour worked), Alberta, by two-digit NAICS industry, 1997-2010,” by Center for the Study of Living Standard (CSLS), 2012]

RESEARCH OBJECTIVES

Shield Installation Activity

0.5 0.4

Support W 30% Support W 35%

0.3

Direct = 45%

Support = 40%

Support W 40% 0.2

Support W 45% Delay = 15%

Support W 50%

0.1 0.0 25

• To develop a hierarchy of key labour productivity factors and practices for varying project context • To develop a reliable and efficient productivity data collection methodology using work study methods • To develop a context-specific activity level labour productivity model and a generalized prediction and analysis model addressing varying project contexts (based on project type, location, etc.) using granular fuzzy hybrid techniques

Optimal WS Values:

35

45 55 65 75 85 Direct Work Proportion (%)

95 [Note: Both relationships not statistically significant ]

OUTPUT VARIABLE

RESEARCH CONTRIBUTION PROCESS VARIABLES

INPUT VARIABLES

• Critical context-specific factors and practices leading to better labour productivity through improved construction planning and execution • Construction labour productivity model adaptation and generalization algorithms • Advanced prediction and analysis tool for construction estimating and control


A tomated Assessment of Tornado-Induced Automated Tornado Induced Ind ced Building B ilding Damage g Based on Laser Scanning g Alireza G. Ali G Kashani K h i ((alireza.geranmayeh@gmail.com) g y @g ) Ad i Advisor: Dr. D Andrew A d Graettinger G i g D p t Department t off Ci Civil, il, Construction, C t ti , and d Environmental E i t l Engineering, E gi i g, The Th University U i ity off Alabama Al b Introduction d

Research h Approach pp h

Results l

• Damage assessment after natural disasters is important for • Data processing algorithms forensic engineering and loss estimation. estimation

• Key is to preserve perishable data of damaged buildings. • 3D laser scanning is an effective technology that captures geometric information with high precision.

I Input GIS layers l

• Point cloud of damaged buildings

• Pre-tornado Pre tornado aerial image

• Dense 3D point cloud data produced by laser scanning virtually reconstructs the damage site and enables engineers to take measurements and retrieve geometric i f information. i LLoss off rooff covering Uplift of roof

Failure of large g section of roof

Estimating wind speed based on damage

D processing Data i algorithms l ih

• Filter noise points (ground, trees, etc.) • Identify roof and wall surfaces • Detect Damage • Quantify the extent of damage • Extract geometric information: • Area of roof covering defects • Percentage off sheathing h h lloss • Roof pitch • Spat Spatial a relationships e at o s ps bet between ee bu building d g

O Outputs

• Quantitative damage information

• GIS damage map • GIS G S wind i d speed d map

Correlation of wind speed & roof damage

Automatically generated wind speed map

Extracting i rooff planes l and d their h i spatial i l relationships l i hi to the h tornado and wind direction

surfaces and tornado/wind / direction

• Estimate wind speed based on damage

• Algorithm tests in controlled conditions Angle between the plane normal and tornado path

Failure of d garage door

3D data d t collection ll ti with ith llaser scanners

Failure of some exterior walls

Information extracted for the selected plane: Sheathing loss: 35% Roof pitch: 33 degree Distance: 132 meter Angle:18 Angle: 8 degree

Broken window

3D point i t cloud l d off damaged d d buildings b ildi

• Manually taking measurements and processing data is challenging and time consuming due to the large amount of data collected and the repetitive nature of manual operations and calculations calculations.

• This PhD research aims at developing a data processing

Simulated data

Damaged roof model

Laboratory scans

• Algorithm l i h tests iin reall conditions di i

Detectingg roof coveringg defects Red: Roof shingles Black: Plywood Blue: Roof felt

framework to automatically analyze tornado-induced tornado induced b ildi d building damage based b d on laser l scanning i data. d Source: Sou ce http://extremeplanet.me/ ttp //e t e ep a et e/

Source: Sou ce FEMA at http://fema.maps.arcgis.com/ ttp // e a aps a cg s co /

p – Tuscaloosa,, AL Residential complex

Practical Implications

• Performance data to improve structural analysis, analysis design, design and d construction i

Extracted roof planes and spatial information

g Residential neighborhood – Moore,, OK

Results

• Automatedd ddata processing i fframeworkk for: f Identifying roofs and walls and calculating percentage of loss

Detected roof defect regions

• Sensitivity S i i i analysis l i off framework f k parameter settings i

• Generation of damage maps for disaster response and recovery

• Loss database updating for management of insurance and disaster assistance programs

• Preliminary P li i l loss estimation ti ti ffor costt analysis l i off repair i and d reconstruction projects p j Automatically generated GIS damage maps

Influence of GIS raster cell size on accuracy of (a) roof detection and (b) wall detection


Vanessa Valentin, PhD Assistant Professor, Vv@unm.edu

Cost Evaluation Model for Housing Retrofit Decision-Making: A Case Study

Objectives Create a model for evaluating housing retrofit alternatives. Perform a Life Cycle Cost Assessment (LCCA) considering typical retrofitting activities. Apply method using a real retrofitting case study as a benchmark.

Retrofitting Activities $79

$55

LCC Model

Programmable Thermostat

$148

HVAC tune up

$223

Replace all lighting with CFLs

$191

$165

$726

Replace with Energy Star for Refrigerator

$45

$526

Replace with Energy Star for Clothes washer

$62

$385

Replace with Energy Star for Dishwasher

$14

$1,521

Insulate Ceilings

$163

$1,916

Insulate walls

$145

$1,298

Insulate Attic

$149

$2,681

Replace doors with insulated core

$5,241

Replace windows with energy efficient glass

$190

Install ground source heat exchanger

$929

$1,460

Evaporative Cooler

$243

$4,572

Solar Thermal

$223

$23,500

Solar Electric

$884

$20,000

Case Study Ranch style home constructed in 1964 Located in Albuquerque, New Mexico 1,600 square feet: 3 BR, 2 BA

$56

Initial Cost

Energy Saving Cost

($)

($/year)

Life Cycle Cost Approach Life Cycle of 50 Years 80,000

Schematic Opt. Point Initial Cost = $38,470

70,000

Applicable retrofitting activities are identified for the case study house. Estimating Costs

Initial costs and energy saving costs are estimated for each activity.

Performing LCCA

Cost ($)

Selection of Retrofitting Activities

50,000 40,000 30,000

0

An LCCA is conducted for each combination of retrofitting activities.

Developing Model

A model which relates investment cost and energy saving is developed.

20%

40%

60%

80%

120%

NZE

100%

$30.0

80%

$20.0

60%

$10.0

Cost Effective Zone

$0.0 0%

20%

40%

60%

40% NZE Zone

80%

20% 0% 100%

Poly. (Investment Cost per Square Feet) Poly. (Energy Cost Saving per Year)

LCC50 years = 76,191X 2 − 88,930 X + 72,112

Application of Model Project located in Concord, MA Total square feet: 3,600 Actual Costs: Pre-retrofit utility cost: $5,500/yr Initial retrofitting cost: $70,000 Post-retrofit utility savings: $4,005/yr = 72% Model Prediction: Cost-Effective Strategy: Investing ≈ $85,000 → Savings ≈ 85% energy Net-Zero Energy (NZE) Strategy: Investing ≈ $105000 → Savings ≈ 100% energy Predicted post-retrofit utility saving : Investing $70,000 → Savings ≈ 75% energy

Only energy consumption is considered. To be more reliable, consider more than one benchmark.

10,000

0%

Optimum LCC

Limitations & Future Work

Realistic Opt. Point Initial Cost = $38,230

20,000

$40.0

The actual energy saving of the retrofitting project is 3% less than what is predicted.

60,000

Methodology

NEW MEXICO Civil Engineering Department

Problem Statement Over 60% of the houses in the US are old and need refurbishment and retrofitting. It is a complex decision to identify investment for the best retrofitting strategy. Questions: How much money the owner needs to invest in a retrofitting project?, How we can evaluate the investments to ensure an optimum return?

THE UNIVERSITY of

Energy Saving Cost (%)

PhD Student, Jafari@unm.edu

Investment Cost ($/f^2)

Amirhosein Jafari

100%

Sustainable Development Level Initial Cost

Energy Cost

Total Cost

Poly. (Initial Cost)

Poly. (Energy Cost)

Poly. (Total Cost)

Testing and calibration of the model to consider different size/properties of homes. Expanding the model to be able to select the best combination of activities for a specific project.


SAVES II: A Multiple Signals Enhanced Augmented Virtuality Training System For Construction Hazard Recognition 1 2 3

Ao Chen (aochen@vt.edu), Brian Kleiner (bkleiner@vt.edu) and Mani Golparvar-Fard (mgolpar@illinois.edu)

1. Via Department of Civil and Environmental Engineering, Virginia Tech 2. Myers-Lawson School of Construction,Virginia Tech 3. Departments of Civil and Environmental Engineering and Computer Science, University of Illinois at Urbana-Champaign

 Current construction safety training does not reach its expectation  780 fatalities and 4000 recordable accidents (BLS 2013) â€œâ€Śdevelop, implement and enforce a comprehensive safety and health training program  Current socio-industry impacts in language and literacy level for workers, which rd include training in hazard recognition and the  3 most unsafe industry, higher compensation rate avoidance of unsafe conditionsâ€? (OSHA 2009 and NIOSH FACE program 2012).  Traditional training methods  Low efficiency, low retention rate, passive role to perceive information

2. Research Background

 Lectures,Videos, Knowledge-based training, Certification  eLearning  Online training  VR and AR  BIM

95%

89%

82%

 

Construct Training Scenarios

Recognition Potential hazard

Client

Personnel exposure

Energy release

Building Information Model

Modify Training Focus

Training Demands

Hazard Perception

Fail

Fail 52%

52% 26%

On-the-Job Training Classroom Training

Authorized Jobsite Expert

Success

Human factor

Dispersion prevention

Risk assessment

Training Scenarios

Share BIM and Safety Information with Members

Other Clients

Training Outcomes and Feedback

Training Effectiveness Analysis

Ignition prevention Fail

On-site Training Room

Safety Regulations and Guidelines

SAVES Training Database

Training Outcomes and Feedback

Off-site Remote Training Device Safety Expertise

Safety Experts

Hierarch control

Escalation prevention

Fail

 Hazard Signal Detection

đ?‘†đ??ˇđ?‘Ą = đ?‘? + đ?‘? / (đ?‘Ž + đ?‘? + đ?‘? + đ?‘‘

SAVES

Success

Fail

Training Points

Human factor

Success

Fail

Consider to be of great value

Human factor

Damage control No accident

Accident

Signal Present Signal Absent Signal Correct Type I error Detected identification (a) False Alarm (b) Signal NOT Type II error - Correct rejection Detected Miss (c) (d)





Training Scene Representation Summary of Qualitative Survey

Positive Neutral Negative Attitudes of SAVES

98%

2%

0%

Agreement of training content

94%

3%

3%

Degree of motivations

100%

0

0

Degree of confidence

97%

3%

Comparing with other trainings 90%

5%

Elec Gra Mech Mot

đ?‘Ž đ??ťđ?‘…1 = đ?‘Ž+đ?‘?

đ??ťđ?‘…vitual đ?‘Žâˆ’đ?‘? đ??ťđ?‘…2 = đ?‘Ž+đ?‘?

Introduce and apply the energy source theory to construction safety training A comprehensive BIM-based “Learning by doing� hazard recognition environment for safety program

 Practical Significance

Outcomes of SDt1 with HRvirtual Che

đ??ťđ?‘…field

 Contributions

 300 hazard scenarios database are established  68 scenarios with 3 severity levels are selected  Tested in 6 construction sites

Bio

đ?‘†đ??ˇđ?‘Ą1 = đ?‘Ž (đ?‘Ž + đ?‘?

Conclusion:

6. Results and Conclusions

5. Experimental Results

Scenarios and BIM forming in SAVES

Sources

Ruquire for Designing Their Own SAVES

SAVES Designers

Feedback and Comments from Workers for Safety Improvement

Feedback to Client for Improvement

 The Wheel of 10 Energy

Fail

Online/eLearing

Develop and empirically examine new transformative hazard recognition strategies for safety improvement. Study whether the more signals synthesized in training the better hazards perceiving performance in work How such rich information enhanced platform like AV could help to improve worker’s safety situational awareness.

Clients needs and trainee inputs

Training within SAVES and Results Evaluation

86% 76%

Use

Generate MS Training Program in SAVES

Build BIM Environment

 Framework of Hazard  Structure of SAVES

Release prevention

3. Research Objectives 

Client inputs

SAVES

Level of Use and Value of Modes of Training for Workers 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

4. Research Methodology

Pres

Rad

Sod

Temp

Lv1

0.019 0.019 0.019 0.077 0.135 0.038 0.038 N/A 0.019

0.019

Lv2

N/A

0.038 0.019 0.058 0.019 0.135 0.019 N/A N/A

0.019

Lv3

0.019 0.019 0.019 0.115 0.019 0.019 0.019 0.038 0.019

0.019

Outcomes of SDt with HRvirtual Lv1

Bio Che Elec Gra Mech Mot Pres Rad Sod 0.026 0.013 0.128 0.077 0.051 0.013 N/A N/A 0.077

Temp 0.013

0

Lv2

0.013 0.064 0.013 0.026 0.115 0.051 0.013 N/A 0.051

0.026

5%

Lv3

0.026 0.038 0.038 0.026 0.013 0.051 0.026 N/A 0.013

N/A



Proactively and efficiently improve the safety awareness and hazard recognition ability  Accurate situational awareness data collection and efficient commination platform  Easy and quick to upgrade and modify the existing virtual model components  Increasing the training motivations and welcomed by workers especially the young workers Acknowledgments Special thanks to CII and RT 293 members

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

1. Motivation


Quantifying Energy-Use Behavior in Commercial Buildings

Construction Research Congress CRC2014

Ardalan Khosrowpour1, John E. Taylor2 1, 2 Charles E. Via, Jr. Department of Civil & Environmental Engineering, Virginia Tech, Blacksburg, VA, 24061 emails: {ardalan, jet}@vt.edu. Academic advisor: Prof. John E. Taylor

MOTIVATION

RESULTS

RESEARCH QUESTIONS

Growth in Commercial Buildings Energy Use

How can we classify commercial building occupants based on their energy consumption patterns?

Normalized Energy Consumption 100 80 60 40

Can we design a new metric for building occupant energy-use entropy based on energy consumption pattern consistency?

20 1980

1985

1990

1995

2000

Residential

Industrial

Transportation

Commercial

0 2005

[U.S. DOE 2008]

Commercial building CO2 emissions projected to grow the fastest, at 1.8% each year through 2030 [1].

Pattern classification

BACKGROUND Commercial Eco-Feedback Systems

METHOD

IMPLEMENTATION

K-means Clustering & Entropy Calculation

Targeted Interventions & Personalized Feedback

Collecting energy consumption data

 Real-time, Appliance-level Eco-Feedback [2,3,4]  Improving User Comfort and Office Energy Efficiency with POEM (Personal Office Energy Monitor) [5]

Pattern consistency entropy

Pre-processing and filtering the data

Clustering based on consumption rate

Normalizing the data

Clustering based on consumption pattern

 Calculating the entropies to measure building occupants’ energy-use consistency

Provide individuals with personalized feedback based on their consumption patterns Targeted interventions for high and inconsistent energy consumers

Efficient and Inexpensive Energy Reduction

References: 1- U.S. Department of Energy (2008) “Energy Efficiency Trends in Residential and Commercial Buildings” 2- Gulbinas, R., Jain, R., Taylor, J., Peschiera, G., Siegel, J., and Golparvar-Fard, M. (in press; Jan.2014).“Network Eco-Informatics: Development of a Social Eco-Feedback System to Drive Energy Efficiency in Residential Buildings.” ASCE Journal of Computing in Civil Engineering. 3 - Gulbinas, R., Jain, R.K., and Taylor, J.E. (in progress: invited paper for Special Issue in Journal of Applied Energy). “BizWatts: A Novel Eco-Feedback System for Quantifying Social Network Effects on Energy Conservation at the Workplace.” Applied Energy. 4 - Gulbinas, R. and Taylor, J.E. (2013). “Effects of Organizational Network Dynamics on Energy Consumption in Commercial Buildings.” Proceedings of the 8th Conference on Sustainable Development of Energy, Water, and Environment Systems, Dubrovnik, Croatia, September 22-27, 2013. 5 - Milenkovic, M., Hanebutte, U., Huang, Y., Prendergast, D., & Pham, H. (2013, April). Improving user comfort and office energy efficiency with POEM (personal office energy monitor). In CHI'13 Extended Abstracts on Human Factors in Computing Systems (pp. 1455-1460). ACM.


Adoption Readiness of Prevention through Design (PtD) Controls in Concrete, Masonry, and Asphalt Roofing Ari Goldberg1 and Deborah Young-Corbett2 1Grado

Department of Industrial and Systems Engineering [arigold@vt.edu] 2Myers-Lawson School of Construction, Civil and Environmental Engineering

_________________________________________________________________________________________________________________________________________________

Overview Concrete, masonry, and asphalt roofing operations are associated with some of the most pressing occupational health hazard risks in construction. The Prevention through Design (PtD) approach to controlling these risks involves the design of tools, equipment, systems, work processes, and facilities in order to reduce, or eliminate, hazards associated with work. Though PtD controls exist, the extent of their use is yet to be documented. The objective of this research is to determine current usage trends and adoption-readiness of decision-makers regarding PtD controls in the concrete, masonry, and asphalt roofing trades

Methods A survey instrument to capture information about current PtD control usage trends and decision-maker opinions about PtD controls was developed and validated. Controls investigated for concrete/masonry were dust collection equipment, wet-method systems, isolation systems, and sweeping compound. Controls investigated for asphalt roofing were asphalt tanker delivery systems, hot luggers/mechanical spreaders/felt-laying machines, insulated kettles/insulated hot luggers, low-fuming asphalt, and kettle fume guards. A telephone survey was completed of 365 decision makers in member firms of the Mason Contractors Association of America (MCAA)(n=700), the Concrete Sawing and Drilling Association (CSDA)(n=541), the American Concrete Pavement Association (ACPA)(n=4000), and the National Roofing Contractors Association (NRCA)(n=4000). Data analysis is currently underway.

Controls Asphalt tanker delivery systems Hot luggers/mechanical spreaders/felt-laying machines Insulated kettles/insulated hot luggers Low-fuming asphalt Kettle fume guards Controls

Dust collection equipment Wet-method systems Isolation systems Sweeping Compound

Asphalt Roofing Percentage of those familiar with Those who are the control, who sometimes, familiar with this regularly, or always use it control 54.8% 23% 78.9%

52.2%

71.8%

57.4%

67.8% 52.2%

45.2% 41.3%

Concrete/Masonry Percentage of those familiar with Those who are the control, who sometimes, familiar with this regularly, or always use it control 90.4% 76.2% 84.4% 92.2% 42.5% 40.9% 41.3% 46.3%

Business factors affecting the way decision makers address fume or dust control on projects

Asphalt Roofing

Concrete/Masonry

Insurance Premiums

58.3%

61.1%

Government Regulations

71.4%

82.0%

Productivity

54.3%

65.9%

Worker Safety

76.4%

91.6%

Other

15.6%

4.8%

Results Preliminary results from open-ended questions indicate decision makers in concrete and masonry feel the best way to reduce overexposure to silica is by reducing/eliminating silica in concrete/masonry products or by using dustless products. For tools/equipment, they suggest improving wet methods and vacuum collection systems. For asphalt roofing, decision makers feel the best way to reduce asphalt fume overexposure is by eliminating the use of asphalt or creating a fanning/exhaust system. For concrete, masonry, and asphalt roofing, decision makers heavily favored material substitution as the ideal PtD control Results indicate only 50.8% of asphalt roofing decision makers view exposure to asphalt fumes to pose a potential health risk to workers while 79.0% of concrete and masonry decision makers view exposure to concrete or masonry dust to pose potential health risks. In asphalt roofing, 35.7% of respondents indicate they have seen an increase in projects specifying fume-control while 54.5% of respondents from concrete end masonry indicate they have seen an increase in projects specifying dust control methods.

Discussion and Conclusion The concrete and masonry trades seem to understand the risk associated with their operations more than asphalt roofing. Asphalt roofing decision makers are less familiar with controls available than concrete and masonry decision makers and those that they are familiar with are used at a lower rate. The concrete and masonry industries seem to be trending toward healthier work practices.

Acknowledgments We thank the Virginia Tech Center for Survey Research staff for their efforts. This publication was supported by Grant/Cooperative Agreement Number 5U60OH009761 from CDC - NIOSH. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH.


A Bio-inspired Virtual Pedagogical Environment to Stimulate Bio-inspired Thinking BIO-INSPIRATION & BIO-BUILD

BIO-BUILD : Current Research

Bio-inspired buildings like Eastgate center, Turning Torso and Gherkin show how Bioinspired thinking can result in a more sustainable built environment.

Image ref: www.inhabitat.com

Image ref: www.inhabitat.com

Image ref: www.docstoc.com

Aruna Muthumanickam maruna@vt.edu Advisor: Dr. John E. Taylor

The BioBuild program at Virginia Tech is a new interdisciplinary PhD program to promote bio-inspired thinking in future academics and industry professionals.

Image ref: cuveline.com stefanharris.co.uk

Bio-inspiration from random walks observed in animal movement studies are being used to understand, model and predict human movements during hurricanes (image illustrates human movements during Hurricane Sandy).

Bio-inspiration from snowdrop flower to employ retroreflective surfaces which reduce urban heat island effects (image of flower and modeled urban block in Energy Plus).

BIO-INSPIRED CyberGRID DESIGN Student discussion spaces

BIO-INSPIRED CyberGRID

The path of water & nutrient transport in trees is analogous to the path in which knowledge diffuses in the BioGRID. Bio-inspired ideas germinate in the roots of the BioGRID. Exemplary cases of bio-inspired buildings are the foliage that are used to cultivate the ideas. Knowledge is collected and diffused in the trunk.

Knowledge diffusion space

Case studies

A bio-inspired version of the CyberGRID that grows along with the research—in a manner that mimics the growth patterns of trees. This recursive virtual pedagogical space is inspired by and will learn from nature to inspire paradigmshifting changes in the built environment.

Cyber-enabled Global Research Infrastructure for Design( CyberGRID): A “virtual collaboration & research environment.” a

THE TEAM:

Dr. A. Brunner

Dr. A.L. Buikema Dr. M.J. Garvin

Dr. D. Hindman

Dr. B. Kennedy

Dr. I. Moore

Dr. R. Muller

Dr. A. Pearce

Dr. G. Reichard

Dr. J.E. Taylor

Dr. D. Young-Corbett


Optimizing the Sustainability of Single-Family Housing Units Aslihan Karatas (karatas2@illinois.edu) Advisor: Prof Khaled El-Rayes Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign

Overview The energy consumption of residential units in the United States (EIA and DOE 2011) Residential Buildings 72% 59%

Commercial Buildings

Single Family Homes

26%

Other Sectors

35% 19%

13%

23%

39%

34%

Other (Apartment Units, Mobile Homes)

16% 66%

Energy Energy Consumption

Consumption

CO2 Emission

CO2 Emission

Electricity Electricity Consumption

Consumption

DOE stated the impact of 10% of US homes reducing energy usage by 25% that includes: (1) Saving $5billion per yr. (2) Reducing GHG equivalent to 225 million cars taking off the road Developers and owners demand their housing units be: More sustainable

Insulation materials

Energy-efficient window systems

Efficient HVAC systems

Water-saving systems

Model Development

Environmental Performance Model

Sustainability Model

We developed each model in six main steps:

We developed a model which is capable of optimizing singlefamily housing design and construction decisions and generating optimal tradeoffs between maximizing the overall environmental performance of single-family housing units and minimizing their initial costs.

We developed a multi-objective optimization model that is capable of simultaneously maximizing the environmental performance of single-family housing units, maximizing the social quality of life for their residents, and minimizing their life-cycle cost.

1. Identify Model Criteria & Metrics

2. Identify Decision Variables

3. 4. Design Formulate Model Objective Functions Constraints

Social Impact Model We developed the social impact model for optimizing the design and construction decisions of single-family housing units to maximize social quality-of-life for their residents while minimizing their life cycle cost. Model Criteria

Decision Variables

Possible Options

1. Thermal Comfort

d1: Heating set point (F) d2: Cooling set point (F) d3: Relative humidity percentage

[67,68, 69, 70, 71, 72, 73, 74] [75, 76, 77, 78, 79, 80, 81] [45, 50, 55, 60, 65] [10, 12, 15, 18, 20] From 2.7 to 3.9 [Clear; High-Solar Gain Low-E; Medium-Solar-Gain Low-E; LowSolar Gain Low-E] [Installed or Not]

d4: Window-to-wall area ratio (%) d5: Window elevation (ft) d6: Window type

Energy-efficient appliances

On-site renewable energy sources

6. Test the Model

We implemented each model as multi-objective genetic algorithm using MATLAB2013b, and linked to an external building energy simulation engine EnergyPlus.

2. Lighting Quality Energy-efficient lighting systems

5. Implement the Model

3. Indoor Air Quality 4. Neighborhood Quality

d7 – d15 :Installation of EPA recommended AQ equipment d16: Mechanical ventilation rate d17: Neighborhood location

Model Metrics 1. Greenhouse Gas Emission 2. Water Consumption

This model includes all criteria and metrics that were identified for Social Impact Model, and Environmental Performance Model. This model incorporates 33 decision variables that represents housing design and construction decisions. Each of the decision variables represents a selection from a set of feasible alternatives that have an impact on the model metrics.

Decision Variables HVAC, Building Envelope, Water Heating, Lighting Fixtures, Appliances, Water Fixtures

This model incorporates 17 decision variables. Each of these decision variables represents a selection from a set of feasible alternatives that covers possible design and/or construction decisions that have an impact on the metrics of the model.

Sustainability Objectives Tradeoff Analysis ENV - IC Tradeoff Analysis

From 35cfm to 75cfm

Max SQOL Solution S74 ENV=0.40 SQOL=0.83 LCC=$180,442

[Arbor Hills, Marquette, Lake Edge]

Task 3: Environmental Performance Model

Problem Statement

Max ENV Solution S1 ENV=0.89 SQOL=0.63 LCC=$196,142

Research Needs Developing Novel Models for Optimizing the Sustainability of Single-Family Housing

Parallel Computing Framework Enabling a practical and computationally-efficient optimization of housing sustainability decisions

Environmental Performance Model Sustainability Model Optimizing Tradeoffs Among Sustainability Objectives of Social, Environmental & Economic Performances

Research Objectives The main goal of this study is to develop new multi-objective models for optimizing the sustainability of single-family housing units.

Parallel Computing Framework We developed a scalable and expandable parallel computing framework to design and implement a global parallel optimization algorithm which is capable of an efficient distribution of the optimization algorithm over a number of parallel processors.  Framework is performed using MATLAB 2013b Parallel Computing ToolboxTM  Framework is implemented on the University of Illinois’ Golub Linux cluster

Elapsed Time

Elapsed Time (hr.)

Social Impact Model

Maximizing the impact of design and construction decisions on the overall environmental performance of housing units

300 250 200 150 100 50 0

287.2 149.3 77.9 53.8 40.5 32.5 27.1 1

2

4

6

8

10

12

Acknowledgments

Number of Worker Processors n

INITIAL COST IC

Maximizing the collective impact of housing decisions on the social quality of life for the residents

Min LCC Solution S210 ENV=0.57 SQOL=0.61 LCC=$149,920

We acknowledge the technical support provided by the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign.

Quality of Solutions

$65,000 $62,500 $60,000 $57,500 $55,000 $52,500 $50,000 $47,500 $45,000 $42,500 $40,000

12 Processors 10 Processors 8 Processors 6 Processors 4 Processors 2 Processors 1 Processor

0.2

0.4

0.6

0.8

ENVIRONMENTAL PERFORMANCE ENV

1


Three-Tired Data & Information Integration Framework for Highway Project Decision-Makings Asregedew Woldesenbet (Ph.D. Candidate) and H. David Jeong (Ph.D.) Civil, Construction & Environmental Engineering, Iowa State University, Ames, IA 50011

Abstract State highway agencies invest a large amount of resources in collecting, storing and managing various types of data ranging from roadway inventory to pavement condition data during the life cycle of a highway infrastructure project. Despite this huge investment, the current level of data use is limited and is raising concerns whether the growing amount of data adds value to users and offers meaningful return on data collection efforts. This study presents a holistic approach that can systematically integrate and bridge data with information and decisions through incorporation of a unique and proactive performance assessment technique to improve utilization of growing amount of data in transportation agencies. With a focus on enhancing active utilization of data and measuring level of data use, this approach delivers i) Three-tiered Hierarchical Framework and ii) Highway Infrastructure Data Integration (HIDI) index, new data and information performance assessment tool. This HIDI index is developed to evaluate the status of data utilization that may serve as Highway Infrastructure Data Report Card and help justify the return on investment on the continuous and growing data collection efforts. It will allow agencies interlink data, information and decisions and develop active utilization plan of currently existing databases to placing the right information in the hands of decision-makers. This new framework may be used as a benchmarking example to SHA in the area of data and information integration to make effective and reliable decisions through data-driven insights.

Vision & Objectives ndnd

2 2 Generation Generation

11stst Generation Generation Various DATABASE

Various DATABASES

Active ActiveInformation Information && Knowledge Extraction Extraction Knowledge

Data Collection Data Collection Effort Effort

Data Collection - Manual/Paper-Based

Data Collection - Semi-Automated/Automated Data Collection

Data Collection

- Semi-Automated/Automated

Approach - Manual/Paper-Based -Approach Expert Judgment - Expert Judgment System System - File Cabinet e.g. Contract Documents -- File Cabinet e.g. Contract Documents Personal computer e.g. Cost Data

- Personal computer e.g. Cost Data

Approach Approach Statistical Tools -- Statistical Tools Excel Spreadsheet -- Excel Spreadsheet

Analysis

33rdrdGeneration Generation

Integrated Data & IntegratedFramework Data & Information Information Framework to to Support Decision Making Support Decision-Making

Data Collection - Automated Data Collection - Automated Standard Data Collection Procedure

Degree Centrality:  Describe the social power & influence of a node  Number of links that lead in or out of a node Use  Identify the DATA critical in generating information Assumption  A key DATA participating in the framework is critical in generating information if exhibits a higher degree centrality

HIDI Grading System Grade

A

Criteria

80% ≤ HIDIl and HIDIm, HIDIn ≤ 20%

60% ≤ HIDIl ≤ 80% B

and HIDIm, HIDIn ≤ 40%

A+

if HIDIm > HIDIn

A

if HIDIm = HIDIn

A-

if HIDIm < HIDIn

B+

if HIDIm > HIDIn

B

if HIDIm = HIDIn

B-

if HIDIm < HIDIn

C+

if HIDIm > HIDIn

C

if HIDIm = HIDIn

C-

if HIDIm < HIDIn

D+

if HIDIm > HIDIn

D

if HIDIm = HIDIn

D-

if HIDIm < HIDIn

System - Project Management System System - Database e.g. SiteManager, roadway inventory -- Project Management Data Warehouse (DW) System

- Database e.g. SiteManager, Inventory - Data Warehouse (DW)

40% ≤ HIDIl ≤ 60% C

- Ontology Based Knowledge Management System

and HIDIm, HIDIn ≤

- Non-relational database System - Knowledge Portal e.g. cloud-based system - Ontology Based Knowledge Management System - Non-relational database - Knowledge Portal e.g. cloud-based system

50%

20% ≤ HIDIl ≤ 40%

The objectives of this study:

D

and HIDIm, HIDIn ≤

 Determine an integrated approach to identify highway infrastructure decision-makers data satisfaction

60%

 Develop an innovative highway infrastructure data and information integration and assessment framework

HIDIl ≤ 20% and

 Validate the developed framework through application of a different set of highway infrastructure data.

Three-Tiered Hierarchical Framework

F

Betweeness Centrality:  Proportion of geodesic paths that pass through a node  Share of a node X needs a node Y in order to reach node Z via shortest path Use  Identify the INFORMATION critical in keeping framework intact Assumption  A key INFORMATION participating in the framework exhibits higher betweeness centrality links Data with Decisions

 Managers are drowning in data while thirsting for information  Frontier of decision-making shifting toward Data-Driven Insights

Productivity Growth (%)

Paths: 1. Active Path - Active use of data currently being employed by agencies to generate information & support decisions 2. Inactive Path - data are currently available but are not utilized in decision-making processes 3. Non-Existing Path - Either no data are available for decision-makers to generate information or information extraction method is not known to support decisions

Social Network Theory Big Data Usage (Manyinka et al. 2011) Research Question:  Does current Data provide right Information for DecisionMaking?

asre@iastate.edu and djeong@iastate.edu

and integration of data and information and decision Highway infrastructure management collects data well, but requires active utilization

HIDIm, HIDIn ≤ 80%

Co-Author Network (Janssen et al. 2006) Construction Network (Park et al. 2011)

Highway Infrastructure Data Integration, HIDI Density Measure  Cohesion measure  Ratio of no. of existing relations to maximum no. of relations Use  Identify the DECISION FRAMEWORK well supported by data and information Assumption  A key highway infrastructure management participating in the framework exhibits a higher density measure reaches at datadriven insights

Highway infrastructure management does not actively utilize data and needs major changes in terms of developing well defined method to generate information and support decisions Highway infrastructure management’s current data and information use are questionable if they meet the standards or decision-makers’ requirement Highway infrastructure management

F

if HIDIm = HIDIn

needs new data collection and

F

if HIDIm < HIDIn

Neural Network Modelinformation generation plan -

Conclusion & Expected Contributions Conclusion  Top-down and bottom-up approach of data, information and decision integration

Decision-Maker Decision-Maker Integrated DecisionMaker Requirement

 Innovative three-tiered data and information integration framework  New data performance measure (HIDI)

 Generate new information

www.PosterPresentations.com

well supported through active utilization

F+ if HIDIm > HIDIn

 Internal evaluators – determine data and information requirement to improve decision-making process  External evaluators – evaluate the status of data and information use (periodic highway infrastructure data report card)

 Data is Considered as Fundamental Asset, New Oil, Science

TEMPLATE DESIGN © 2008

Highway infrastructure management are

- Standard Data Collection Procedure Approach Approach - Pattern Recognition Pattern Recognition -- Knowledge Discovery in Database (KDD)/Data Mining Knowledge Discovery in Database/Data Mining -- Big Data Analytics Algorithm - Big Data Analytics Algorithm System

Problem Statement

 Do the collected data meet decision-makers requirement?  Which data are important and critical in decision-making?  What type of analytical methods should be used to extract information and knowledge?

Definition

Grade Breakdown

Data 1 Data 2 Data 3

Decision-Maker Decision-Maker

Data, Information & Decision Integration Framework

Data 1 Data 2 Data 3

Information Information

Expected Contribution  Benchmarking example to state highway agencies  Proactive system active utilization  Paradigm shift data collection  Change the culture of owners view  Help justify the return on investment

Information Information

Information Generation

Data 1 Data 2 Data 3

References Flintsch W., Gerardo and Bryant, W. J. (2006), “Asset Management Data Collection for Supporting Decision Processes”, U.S. Department of Transportation FHWA Park, H., M. A. Seung H. Han, Eddy M. Rojas, JeongWook Son and Wooyong Jung (2011). "Social Network Analysis of Collaborative Ventures for Overseas Construction Projects." Journal of Construction Engineering and Management, vol. 137, pp. 344–55. Kennerley, Mike and Mason Steve (2008), “The Use of Information in Decision Making: Literature Review for the Audit Commission”, Centre for Business Performance, Cranfield S. of Management. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. (2011), “Big Data: The Next frontier for Innovation, Competition and Productivity”, Mckinsey Global Institute Vesely, W.E., M. Stamatelatos, J.B. Dugan, J. Fragola, J. Minarick III and J. Railsback (2002), “Fault Tree Handbook with Aerospace Applications”, Washington D.C., USA, NASA Office of Safety and Mission Assurance


Minimizing  the  effects  of  overfi3ng  and  collinearity  in     construc9on  cost  es9ma9on:     A  new  hybrid  approach   PhD candidate: Bo Xiong

Supervisors: Prof. Martin Skitmore, Dr. Bo Xia Queensland University of Technology, Australia Email: peterxiongbo@gmail.com

Introduc9on   Research  problem:  Overfi(ng  and  collinearity  problems   commonly  exist  in  current  construc9on  cost  es9ma9on   applica9ons  and  obstruct  researchers  and  prac99oners  in   achieving  be;er  modeling  results.     Research  objec9ve  and  method:  A  hybrid  approach  of  Akaike   informa9on  criterion  (AIC)  stepwise  regression  and  principal   component  regression  (PCR)  is  proposed  to  help  solve   overfi(ng  and  collinearity  problems.  U9liza9on  of  this   approach  in  linear  regression  is  validated  by  comparing  it  with   other  commonly  used  approaches.  The  mean  square  error   obtained  by  leave-­‐one-­‐out  cross  valida9on  (MSELOOCV)  is  used   in  model  selec9on  in  deciding  predic9ve  variables.  

The  new  hybrid  approach   Step  1:  Carry  out  the  AIC  criterion  stepwise  regression.     Step  2:  Carry  out  collinearity  diagnos9cs.     Step  3:  Carry  out  principal  component  analysis  (PCA)  to   transform  k  correlated  variables  to  a  set  of  uncorrelated   principal  components.   Step  4:  Compute  the  standardized  dependent  variable,  the  p   standardized  independent  variables  and  the  values  of  the  p   principal  components  respec9vely.   Step5:  Carry  out  the  AIC  criterion  stepwise  regression  to   select  q  principal  components  in  the  equa9on.   Step6:  Perform  a  series  of  transforma9ons  to  obtain  the   standardized  linear  regression  equa9on.   Step  7:  Calculate  the  regression  coefficients  and  constant  and   transform  the  standardized  linear  regression  equa9on  into   the  general  linear  regression  equa9on.  

Valida9on  details   204  UK  school  building  contracts  during  2000-­‐2012  were   collected.  Differences  generated  by  districts,  construc9on   years,  infla9on  rates  are  compensated  by  rebasing  all  204   cases  to  1st  Quarter  2012  ,  Greater  London  district.  To  mimic   a  situa9on  with  overfi(ng  and  collinearity  issues  when   applying    tradi9onal  regression  modelling,  33  elemental  cost   items  were  used  to  es9mate  preliminaries,  a  highly  uncertain   but  important  component  of  construc9on  cost  in  forecas9ng.  

Mul9collinearity  diagnos9cs  (step  2)                             1st    Model  selec+on  to  avoid  overfi2ng-­‐  MSELOOCV       M1   M  2   M  3   M  4     MSE  (1010)   1.4103   1.3554   1.3907   1.4142   MSELOOCV  (1010)   2.1603   2.6434   2.1175   4.0898   Rate of increase  

53.18%  

95.03%  

52.26%   189.20%  

Model  selec9on  aKer  PCR  to  reduce  collinearity  (step  3-­‐7)           M3-PCR1(SSE)   M3-PCR 3(AIC)   MSE(1010)   1.335   1.301  

Conclusions   The  approach  not  only  solves  overfi(ng  and  collinearity   problems,  but  also  improves  predictability,  with  7.8%  less   MSE  than  the  default  stepwise  regression  model.   Academic  and  prac9cal  contribu9ons:  Aaer  proposing  and   valida9ng,  researchers  and  prac99oners  can  apply  this  new   approach  in  overcoming  the  widely  exis9ng  overfi(ng  and   collinearity  problems  in  other  similar  forecas9ng  situa9ons.   Further  applica9ons:  A  decision-­‐making  assistant  tool  for   clients  is  under  development.  Since  a  single  point  es9mate   of  preliminaries  is  probably  not  enough  for  clients  to  select   contractors  and  avoid  poten9al  risks,  confidence  and   predic9on  intervals  are  presented  to  assist  clients  decision-­‐ making.    

Model  development  (step  1)   Three  models  were  developed  by  applying  stepwise   regression  under  the  criteria  SSE(M1),  adjusted  R2  (M2)  and   AIC  (M3)and  a  regression  model  with  enter  all  variables  (M4)  

Acknowledgement:  The  candidate  is  grateful  for  the  financial   support  of  a    QUT  HDR  Sponsorship  from  the  research   project  “BER–CAM”  funded  by  the  Commonwealth  of   Australia  represented  by  the  Department  of  Educa9on.  


Automated Real-Time Tracking and 3D Visualization of Construction Equipment Operation Using Hybrid LiDAR System Chao Wang, Ph.D. Candidate; Advisor: Dr. Yong K. Cho, Associate Professor Robotics & Intelligent Construction Automation Lab (R.I.C.A.L), School of Civil and Environmental Engineering

1. Introduction

3. Field Tests and Results

PROBLEM STATEMENT: • Unstructured work areas like construction sites are difficult to graphically visualize because they involve highly unpredictable activities and change rapidly

• Low data collection speed and low object recognition rates. • Difficult to rapidly extract the target area from background scattered noises in a large and complex 3D point cloud.

RESEARCH OBJECTIVES: The main objective of this research is to design, develop, and validate a 3D visualization framework to collect and process dynamic spatial information rapidly at a cluttered construction job site for safe and effective construction equipment operations.

2. Methodology

Surface Modeling Process (a) Pre-scanned point cloud of static site environment; (b) (b) A crane model embedded with the site environment

Total processing time with different α value for the case study

4. Findings and Conclusions

Framework of the proposed method

This research proposes fundamental research on 3D workspace modeling to foster a breakthrough innovation in robotic manufacturing and automated construction site operations which will greatly benefit the future U.S. construction industry and society in general. The knowledge obtained will enrich the literature in the important area of construction engineering and management and provide solutions to industry-driven problems. This research also proposes vigorous goals for integrating research and education.

Data transmission network configuration


I nt e r de pe nde ntI nf r a s t r uc t ur eNe t wor k Sys t e mVul ne r a bi l i t yI de nt i f i c a t i on Ch r i s t o p h e rVa nAr s d a l e , c d v a n a r s @mt u . e d u Mi c h i g a nTe c h n o l o g i c a lUn i v e r s i t y Ad v i s e r :Dr . Aml a nMu k h e r j e e

Mot i va t i on

Me t hod

1.I nf r a s t r uc t ur ei nt e r de pe nde nc i e sc a nr e s ul ti ns i gni f i c a nta ndune xpe c t e di mpa c t sa na dj a c e nt i nf r a s t r uc t ur ene t wor ks ,l owe r i ngt hes ys t e ml e ve lofs e r vi c epr ovi de d. 2. The r ei snome a s ur e me ntf orr a t i ngi nf r a s t r uc t ur es us t a i na bi l i t y( be yonde mi s s i ons ) . The s e TM . a r ene e de df orr a t i ngorc l a s s i f i c a t i ons ys t e mss uc ha sEnvi s i on

Af r a me wo r kd e v e l o p e db yMo n t a n aSt a t eUni v e r s i t y , NEO, o rNe t wo r kEx c h a n g eOb j e c t s s i mu l a t e sc o mp l e xd e p e n d e n tn e t wo r k s . Bya n a l y z i n gt h e s en e t wo r kp e r f o r ma n c e , t h e r o b u s t n e s sa n dr e s i l i e n c ec a nb ed e t e r mi n e d .

Si mu l a t i o n

Me t r i c s

Ba s e l i n en e t wo r k

Obj e c t i ve s 1.De ve l opMe t r i c sf orr obus t ne s sa ndr e s i l i e nc eofa ni nt e gr a t e di nf r a s t r uc t ur ene t wor k. 2.De ve l opame t hodt oi de nt i f yc r i t i c a lpoi nt si na ni nt e gr a t e di nf r a s t r uc t ur ene t wor kc ons i s t i ng ofna t ur a la ndt r a ns por t a t i onne t wor ksa ndr a nkt he mi nor de rofvul ne r a bi l i t y . Li n k d i s a b l i n ge v e n tn e t wo r k

The or y

Ba s e du p o nt h ei d e n t i f i c a t i o no ft h ev u l n e r a b l er o a d , a na l t e r n a t i v ep a t h c a nb ea d d e dt op r e v e n tt h es t r e a mf r o mb e c o mi n gu n v i a b l e

As us t a i na bl es ys t e mi sde f i ne da sas ys t e m t ha tpr ovi de sal e ve lofs e r vi c et ome e tt he c ur r e ntne e dswhi l ebe i nga bl et ome e t c ha ngi ngde ma nds . Vi a bi l i t yi sde f i ne da sa s t a t ewhe r et hene t wor kors ys t e mme e t st he mi ni ma ll e ve lofs e r vi c ef ori t sus e r s .

Mo d i f i e dn e t wo r k


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 workersâ&#x20AC;&#x2122; 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.


Quantitative Performance Assessment of Single-Step and Two-Step Design-Build Procurement David Ramsey1, Dr. Mounir El Asmar2, and Dr. G. Edward Gibson Jr.3 1Ph.D. Student, 2Assisstant Professor, 3Director of SSEBE, Arizona State University, Tempe, AZ 85287-5306, USA David.W.Ramsey@asu.edu, asmar@asu.edu, GEdwardGibsonJr@asu.edu

Objectives & Research Methodology

Introduction Design-Build (DB) is an alternative project delivery system that is distinguished by a DB team acting as a single point of responsibility for a project where the design and construction phases overlap. There are two primary procurement methods used to procure DB services: singlestep and two-step procurement. There are considerable differences between these two methods. This research project focused on evaluating the resource expenditures of single-step and two-step DB projects through investigating several pre-award and post-award metrics.

Single-Step vs. Two-Step DB Procurement Single-Step DB Single-phase procurement. Also called One-Step or Turnkey DB. Design-Builders respond to an owner’s solicitation that requires both qualifications and cost components in one submittal.

The primary goal of this research was to examine the resource expenditure and efficiency impact of single-step DB as compared to two-step DB. This comparison was completed by focusing on several procurement and project performance metrics that compare the two methods with respect to cost, schedule and quality. (1) Quantify Resource Expenditure: Resource expenditure was quantified by detailing a list of procurement costs and project costs from the design-builder’s perspective. Procurement cost information included cost to develop the SOQ (in the case of two-step projects) and cost to develop the RFP (in the case of single-step and two-step projects. Project cost information included the contract award value and final project cost. (2) Quantify Efficiency Impact: Efficiency impact was quantified by detailing a list of procurement schedule information and project schedule information. Procurement schedule information included the RFQ issue date, SOQ due date, shortlisted firm’s notification date, RFP issue date, proposal due date, and contract award date. Project schedule information included construction and delivery schedule information in terms of intended and actual dates for award and substantial completion. (3) Address quality differences: Procurement quality information was addressed by detailing innovative design and construction solutions as well as the percentage of design completed during the RFP stage of procurement. Project quality was documented by collecting data about two quality metrics, which included as-build project quality and overall satisfaction with the project from the DB team’s perspective.

These proposals typically consist of a statement of relevant qualifications, experience, proposed management strategies, technical approaches and cost considerations.

Step 1: Literature Review

Two-Step DB

Step 2: Survey Development

Owner’s issue a public request for qualifications (RFQ). DB Firms then submit their statement of qualifications (SOQ), which contains only relevant qualifications and experience.

DB Offerors' Relative Proposal Developement Costs (%)

Problem Statement

$3.5

$2.5 $2.0 $1.5 $1.0

15% 10% 5% 0%

$0.0 Single-Step

However, vital information regarding single-step and two-step procurement performance is lacking.

This 1999 study is the most recent that compares single-step and two-step DB procurement methods. One drawback lies in the fact that DB practices have evolved at a rapid pace since Molenaar’s original study and nearly 15 years have passed since new information regarding single-step and two-step DB procurement has been published. Therefore this research study was needed to re-evaluate the performance differences between these two distinct procurement methods.

350

35%

300

30%

Relative Procurement Duration (%)

Moreover, Molenaar et al. (1999) showed that within DB projects, schedule and cost growth of two-step DB projects were reduced significantly over single-step projects.

Absolute Prcurement Ducration (Calendar Days)

Several authors have shown that design-build project cost and schedule performance to be superior to that of other project delivery systems (Songer and Molenaar 1997; Konchar and Sanvido 1998; El-Wardani et al. 2006.)

Single-Step

Two-Step

Two-Step

Procurement Schedule

The literature review conducted helped to identify some related studies.

250 200 150 100 50

25% 20% 15%

Two-Step

1.21

0.81

0.61

0.69

0.001*

5.43

6.03

1.12

0.72

4.31

0.026*

1.50

1.21

0.95

0.71

0.55

0.001*

5.43

6.03

1.17

0.66

4.26

0.041*

0.28

0.26

0.26

0.21

0.02

0.500

0.71

0.51

0.36

0.27

0.35

0.065

Single-Step (N=15) Average Std. Dev.

Two-Step (N=17) Average Std. Dev.

Analysis Difference p-value

129.73

67.35

156.35

53.53

26.62

0.031*

96.50

64.50

71.18

33.78

25.32

0.203

129.73

67.35

144.06

45.12

14.27

0.093

18.18

6.69

22.53

6.91

4.35

0.354

Innovation: There is no conclusive evidence that single-step projects offer less potential for innovation than two-step projects. One indicator for innovation is the percentage of design completed at the RFP stage. On average single-step projects had about 23 percent of the design complete at the RFP stage, while two-step projects had approximately 12 percent of the design complete, theoretically allowing for more innovation in the DB team’s solutions. However, these observed differences are not statistically significant.

10%

Literature

0% Single-Step

1.50

Project Performance: There were no statistically significant differences in overall project cost and schedule metrics between single-step and two-step projects. Similarly, there were no significant differences in total project changes; however, there were significant differences in owner driven changes for two-step projects. The project quality metrics of as-built project quality and overall satisfaction showed marked improvements in two-step projects; however, these differences were not proven to be statistically significant. Moreover, facilities procured with two-step DB are achieving a higher LEED rating than projects procured under a single-step framework.

5%

0

Procurement Schedule Metrics Absolute Procurement Duration (calendar days) Owner's Review Time (calendar days) Absolute Procurement Duration (calendar days) minus Waiting Time Relative Procurement Duration (%)

Analysis Difference p-value

Procurement Schedule: There is no conclusive evidence that single-step projects are faster to procure than two-step projects. Although absolute procurement schedule duration was proven to be on average 28 calendar days shorter for single-step projects, the statistical significance was instantly negated by a mere reduction in the owner’s waiting time between shortlisting firms and RFP issuance.

20%

$0.5

DB as a project delivery system has become increasingly common in the architecture, engineering and construction (AEC) industry.

Absolute Cost to Develop All Proposals ($ millions) Relative Cost to Develop All Proposals (%) Absolute Cost to Develop All SOQs & Proposals ($ millions) Relative Cost to Develop All SOQs & Proposals (%) Absolute Cost to Develop Winning Proposal ($ millions) Relative Cost to Develop Winning Proposal (%)

Two-Step (N=11) Average Std. Dev.

Procurement Cost: Proposal development costs from an industry perspective are about five times larger for single-step DB projects (5.43% of project cost) when compared to two-step DB projects (1.12% of project cost). Cost differences also were statistically validated for absolute proposal costs in U.S. Dollars regardless of project size. Moreover, the range of the relative proposal development costs is about ten times larger for single-step DB (up to about 20% of project cost) when compared to two-step DB (up to about 2%).

25%

$3.0

Single-Step (N=11) Average Std. Dev.

Procurement Cost Metrics

Final Results

$4.0 DB Offerors' Absolute Proposal Developement Costs ($ millions)

typically consist of technical, managerial and cost

Step 4: Data Analysis

Procurement Cost

Owner’s then review this statement of qualifications and shortlist the most qualified firms, typically 3 to 5 firms are shortlisted.

These full proposals considerations.

Step 3: Data Collection

Results

Two-phase procurement

These shortlisted firms are then issued a request for proposal (RFP) by the owner and are invited to submit full proposals for the project.

Statistical Analysis

Single-Step

Two-Step

El-Wardani, M., Messner, J., and Horman, M., (2006). “Comparing Procurement Methods for Design Build Projects.” Journal of Construction Engineering and Management, ASCE. Vol. 132, No. 3, pp. 230–238. Konchar, M., and Sanvido, V. (1998). “Comparison of U.S. Project Delivery Systems.” Journal of Construction Engineering and Management, ASCE. Vol. 124, No. 6, pp. 435–444. Songer, A. D., and Molenaar, K. R. (1997). “Selective design-build: Public and private sector owner’s attitudes.’’ Journal of Management in Engineering, ASCE. Vol. 12, No. 6, pp. 47–53. Molenaar, K. R., Songer, A., and Barash, M. (1999). “Public-Sector Design/Build Evolution and Performance.” Journal of Management in Engineering, ASCE. Vol. 15, No. 2, pp. 54–62


Author: 

Risk Allocation in PPPs: Analysis of Contractual Provisions  in 18 U.S. Highway Projects Problem Statement

Advisor: 

Duc Nguyen, Virginia Tech, duc@vt.edu Edwin Gonzalez, Virginia Tech, edwing@vt.edu Dr. Michael Garvin, garvin@vt.edu

Selected Preliminary Results

• Public‐Private Partnerships (PPPs): long‐term contracts between the public and  private sectors; where the private entity provides design, build, finance, operation,  and maintenance services; and puts private equity at risk [1] • Risks can be allocated to the most appropriate parties • Risks and risk allocation in PPPs has received much attention in academic research 

[2, 3, 4, 5, 6, 7] • How risk allocation occurs in actual PPP projects: lacks of necessary attention

U.S. PPP Highway Projects NW Parkway  (2007)

East End Crossing (2012)

Chicago Skyway (2005)

Dulles Greenway (1993)

In. Toll Road (2006)

Project

State Site acquisition Price adj.

Usage/demand

Presidio  Parkway

CA

Public

Private

Latent defects

Force Majeure Residual value

Option for Private Public

Public

Shared

Private

IN

Shared

Shared

Public

Public

Shared (Negotiation) Shared

Private

IN

Shared

Shared

Private

Public

Public

Shared

Private

I‐635 LBJ TX North Tarrant  Exp. (Ph. 1) TX Dulles  Greenway VA

Private

Shared

Private

Private

Private

Mostly Private Private

Private

Private

Private

Shared

Private

Mostly Private Private

Public

N/A

Private

N/A

Public

Public

N/A

I‐95 

VA

Private

Private

Private

Private

Private

Shared

Private

I‐495 Midtown  Tunnel

VA

Private

Mostly Private Private

Mostly Private Private

Shared

Private

VA

Shared

Public

Shared

Public

Private

East End  Crossing Indiana Toll  Road

Private

Network

Shared

Legend •

Project sizes by dot areas

Projects:

(2006)

Tolls Availability Payment

Midtown Tnl. (2012)

Lease/Brownfield Unsolicited Proposal Qualification Base Selection Competitive Proposal •

I‐495 (2007)

States: Broad PPP Legislation Limited PPP Legislation

I‐95 (2012)

Presidio Parkway  (2012)

No PPP Legislation

I‐595  (2009)

(2009) I‐635  (2010)

SH‐130  (2008)

Data  collection*

Literature  review

PPP risks

IDI

Risk  allocation  Rubric

Infrastructure Development Initiative

Analyze PPP  contracts (9  to date)

*18 projects: from 1993 to 2013 • Cumulative contract value = $28,270.8 million • Average contract value = $1,570.6 million • Range of contract value = $130 million – $4,095.8 million

Comparative  Analysis

• Variance in risk allocation across jurisdictions is prominent.  The nature of projects and   transactions drives this variability • Many mechanisms exist for the parties to share risks. Earlier contracts tended to use  negotiation for risk sharing, while more recent contracts are employing other schemes  such as: deductible, reserve account, categorical, and prorated • In general, risks are transferred to the private sector; however, the magnitude of these  risks is modest, confirming literature on the subject

P. of Miami Tunnel (2009) Puerto Rico  PR 22 & PR 5  (2011)

Methodology

Discussion

Risk  allocation in  the U.S. PPPs

Future Research • Explore avenues to make contractual provisions more dynamic or flexible • Compare PPP risk allocation in the U.S. with other jurisdictions where the PPP market is  more mature

References 1 Garvin, M. J., and Bosso, D. (2008). "Assessing the effectiveness of infrastructure public‐private partnership programs and projects." Public Works Management & Policy, 13(2), 162‐178. 2 Akintoye, A., Taylor, C., and Fitzgerald, E. (1998). "Risk analysis and management of private finance initiative projects." Engineering, Construction and Architectural Management, 5(1), 9‐21. 3 Bing, L., Akintoye, A., Edwards, P. J., and Hardcastle, C. (2005). "The allocation of risk in PPP/PFI construction projects in the UK." International Journal of Project Management, 23(1), 25‐35. 4 Cruz, C. O., and Marques, R. C. (2012). "Flexible contracts to cope with uncertainty in public–private partnerships." International Journal of Project Management. 5 Froud, J. (2003). "The Private Finance Initiative: risk, uncertainty, and the state." Accounting, Organizations and Society, 28(6), 567‐589. 6 Grimsey, D., and Lewis, M. K. (2002). "Evaluating the risks of public private partnerships for infrastructure projects." International Journal of Project Management, 20(2), 107‐118. 7 Ke, Y., Wang, S., and Chan, A. P. (2010). "Risk allocation in public‐private partnership infrastructure projects: comparative study." Journal of infrastructure systems, 16(4), 343‐351.


Quantifying the risks of wildfire to buildings in Wildland Urban Interface (WUI) THE UNIVERSITY of Elmira Kalhor , Vanessa Valentin

NEW MEXICO

Research Framework

Problem Statement

Expected Contributions • This study assumes future fire scenarios for probability analysis.

• Frequency and severity of wildfires is increasing.

Topography Vegetation

• Wildfires find their way to the Wildland-Urban Interface (WUI) where residential buildings get closer to the wildland.1

Fire Ignition initiation in the Wildland

• Housing development projects progress further towards wildland increasing the vulnerability of the community.2,3

• Question: How urban planning and safety management can account for the risk of wildfire to buildings, specifically those located in WUIs?

• Provides a novel wildfire risk assessment that can be use for urban planning decision-making.

Future Work

Flames in WUI Wildfire Behavior Simulator

Risk of structural Ignition

Heat Transfer

• Fire progression model will be selected and encoded.

Weather MonteCarlo Data Generator

• Calculate the probability and damage of wildfire to the constructed environment.

• Extent of damage due to loss will be calculated.

Characteristics of the constructed environment

Management Plan LandUse

Research Objectives

• Urban plan scenarios will be analyzed and optimized.

References

Econometrics Analysis

Ignitio Predictor

(1) Ellen Whitman, Eric Rapaport, Kate Sherren. 2013. “Modeling Fire Susceptibility to Delineate Wildland– Urban Interface for Municipal-Scale Fire Risk Management.” Environmental Management 52 (6): 1427–1439.

• Provide risk maps for urban planning risk minimization.

Demonstration Based on fire propagation simulation, the risk of fire to the existing land-use plan is calculated considering structure burning probability and expected loss.

T Wildland

Risk associated to optimal land-use model is simultaneously generated

Burnt the first hour 1

1

0.5

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47500

45000

42500

40000

37500

35000

32500

30000

27500

25000

22500

20000

17500

15000

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Loss ($)

Simulator

Burnt the first hour: Optimal land-use

0.5

0

Loss ($)

Optimizer

Simulation and optimization work hand in hand to find a land-use plan that minimizes the risk from wildfire

Optimal land-use model is generated

T U U U U U U U U U

U U U U U U U U U U

U T T T T U U U U U

U T T T T T T U U U

W W W W T T T T T T

T T W W W W W T T T

T T T T W W W W T T

T T T T T T T W U T

T T T T T T T W U U

(2) Keither, Robert. 2006. “The Law of Fire: Reshaping Public Land Policy in an Era of Ecology and Litigation.” Environmental Law 36: 301–384. (3) Mueller, Julie M, and John B Loomis. 2013. “Does the Estimated Impact of Wildfires Vary with the Housing Price Distribution ? A Quantile Regression Approach.”

-5000 -3750 -2500 -1250 0 1250 2500 3750 5000 6250 7500 8750 10000 11250 12500 13750 15000 16250 17500 18750 20000 21250 22500 23750 25000 26250 27500 28750 30000

Frequency

1.5

12500

T T T T T T T W T T

10000

T T T T T T T W U T

7500

T T T U U U U W U T

5000

T T U U W W W W U T

2500

T T W W W W W T T T

0

W W W W U U U U T T

-2500

T T T T T T U U T T T U T T T U T T T U T T U U T T U U T T T U T T T U UrbanT Areas T T T WWaterTbody

-5000

Existing land use model is coded

Frequency

Spatial data collected

• Ignition probability functions will be constructed using social and ecological data.

T T T T T T T W U U

Contact information • Student: Elmira Kalhor Email :Ekalhor@unm.edu • Advisor: Vanessa Valentin, PhD Email: vv@unm.edu


Investigating the Impact of Innovation on Collaboration in the AEC Industry 1

Erik A. Poirier PhD Candidate Dept. of Construction Eng.1

Supervised by:

Prof. Daniel Forgues

Dr. Sheryl Staub-French

PhD, Professor Dept. of Construction Eng.1

PhD, Associate Professor Dept. of Civil Eng. 2

1

École de Technologie Supérieure Montréal, Qc, Canada

2

2

University of British-Colombia Vancouver, B.C., Canada

erik.a.poirier@gmail.com

ACT

IO

OR

GA

N

IN

O

LO

IN

C

GY

ES

IV ENT

ABILITIES

Figure 3 - Research Approach: Iterative Mixed- Method

Agentic Layer Structural Layer Operational Layer

Definition

Keywords

Expectations Requirements

Need, Demand, Call “A thing that is needed or wanted”[09]. Require, For, Insist On, Ask For

Capabilities

An individual’s, organization’s or team’s Capable, Can, Competent, “power or ability to do something”[09]. Accomplish, Skill, Experience

Incentives

Incentives “motivate or encourage someone to do something”[09].

Motivate, Value, Reason, Benefit, Advantage, Spur, Encourage, Success

Intentions

An individual’s “determination to act in a certain way” [10].

Intend, Plan To, Mean To, Have In Mind, Aim To, Propose

Technology

“The application of scientific knowledge Software, Hardware, Computer, for practical purposes, especially in Digital, [...] industry” [09]

Organization

“An organized group of people with a particular purpose [...]” 09]

Group, Company, Team, System, Network [...]

Process

“A series of actions or steps taken in order to achieve a particular end” [09]

Flow, Mechanism, Operation, Procedure [...]

Context Action

Technology Organization

Capability

Interaction

Process

Incentive

Outcome

Transaction

Context

Intention

Agentic Layer informs action (why it occurs)

Action

Structural Layer configures action (how it occurs)

Operational Layer manifestation of action (what occurs)

Outcome consequence of action (what is the result)

Cycle 2 - Test Framework

Expect, Believe, Anticipate, “The strong belief that something will Hope, Suppose, Presume, happen or be the case in the future” [09] Imagine, Assume

“The circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood” [09] “Human action occurs as a durée, a continuous flow of conduct, as does cognition.” [11]

Establish coding schema (figure 6)

Develop and aggregate categories

Figure 6 - Coding Schema

CAP

(Mixed-Method)

TE

C H N

Test Framework

T

Case Study

* Through a mixed-method approach [05]; * Rooted in the pragmatist epistemological paradigm [06]; * Underlies an iterative process aimed at theoretical saturation (figure 3) [07]. * Cycle 1 - build framework : the aim is to characterize collaboration in the AECM industry; * Cycle 2 - operationalized and test framework: the aim is to operationalize the framework and assess the impact of these innovations on collaboration, its emergence and its evolution; * The research methodology is case study based: Five case studies have been targeted to represent the full project continuum and allow cross-case analysis [08]; * Qualitative and quantitative data collection and analysis.

EX

Literature

N

Build Framework

SACTIO

How? (Qualitative)

AN

To investigate the impact of innovation on collaboration in the AEC industry with a focus on integration of information, of practice and of process.

TR

Research Objective

EXPECT A T I

Figure 2 - Collaboration through Integration: Consequences of Innovation

Category

O N S

Process

Socio-Cognitive Barriers Emerging Reconfiguration of Work which introduces / Restructuring Dynamic exacerbates... of Information Strategic [04] Realignements Interoperability Evolving Concerns [...]

TS

that considers...

Consequence

Analyze and code data

Requirement

CO

Collaboration as

Information

Practice

OUTCOME

Collect data from case studies Expectation

T

Integration of

TE

L

N

Innovation Building Information Modeling (BIM) a radical Integrated shift Design Process towards... (IDP) [03] Integrated Project Delivery (IPD)

N

NA

C S

Figure 1 - A Teleological View of Collaboration: a Means to an End?

IN

IO

O

R

Disperse

AT

R

E

ES

* The radical shift towards integration led by these unbounded innovations requires further study to ensure proper implementation (figure 2).

PR

YE

M

Cycle 1- Build Framework

ON

Outcome

OPER

LA

RE

CTI

Learn

* The traditional teleological view of collaboration is ill-suited to investigate its emergence through innovation (figure 1);

N

IO T ZA

RAL

UI

YE

Deliver

I

STRUCT

LA

[01]

T

EN

O TI

RA

Execute

As a core tenet of the Architecture, Engineering and Construction (AEC) industry, considerable work has strived Assemble to develop products, practices and processes aimed at Innovate fostering collaboration. These innovative approaches to project delivery are pushing us to rethink how and why [02] we collaborate; they highlight many issues: Collaborate Perform * The concept of collaboration remains amorphous;

REQ

NS

Preliminary Findings

C LA YE R

N

Plan

AGENTI

History, Culture, Background, Situation, Environment, [...]

Observe and query behaviour through framework (figure 4)

Interaction

Exchange, Interact, Communicate, Join, Contact, Join, Reach out, [...]

Transaction

“To do (business) with another company.” [10]

Transact, Conclude, Negotiate, Settle, [...]

Figure 4 - A Multi-Layered Framework for the Characterization of Collaboration in the AECM Industry

Effective deployment of innovation

Mis-aligned

Misfire of innovation

Seek alignements per type (figure 7)

Degree

Scale

Scope

Duration

Within Category Between Category Within Layer Between Layers

Individual Project Team Organization Industry

Intra-disciplinary Inter-disciplinary Single-System Multi-system

Temporary Project Phase Project Lifecycle Permanent

Expected Contributions Scientific

Practical

* Adopts a pragamatic and mixed-method approach to study the impact of innovation on collaboration.

* Its is scalable and aggregatory, i.e. it can be applied across scales, scopes and stages.

* Develop a substantive theoretical framework aimed at characterizing the emergence and evolution of collaboration through innovative approaches to project delivery in the AEC industry.

Use, Act, Produce, Do, Look, Seek, Search, Model, [...]

“Act in such a way as to have an effect on each other.” [09]

Aligned

Figure 7 - Alignment Types

References

Why?

* The substantive theoretical framework is grounded in practice. It offers a common language to foster collaboration and can serve in orienting focus when implementing unbounded innovations;

[01]. Smyth, H. & Pryke, S. 2008. Collaborative Relationships In Construction: Developing Frameworks And Networks, Wiley. Com. [02].Taylor, J. E. 2005. Three Perspectives On Innovation In Interorganizational Networks: Systemic Innovation, Boundary Object Change, And The Alignment Of Innovations And Networks. Stanford University. [03]. Elvin, G. 2007. Integrated Practice In Architecture: Mastering Design-Build, Fast-Track, And Building Information Modeling, Hoboken, N.J., John Wiley & Sons,. [04]. Forgues, D. & Koskela, L. 2009. The Influence Of A Collaborative Procurement Approach Using Integrated Design In Construction On Project Team Performance. International Journal Of Managing Projects In Business, 2, 370-385. [05]. Abowitz, D. A. & Toole, T. M. 2010. Mixed Method Research: Fundamental Issues Of Design, Validity, And Reliability In Construction Research. Journal Of Construction Engineering And Management, 136, 108-116. [06]. Simpson, B. 2009. Pragmatism, Mead And The Practice Turn. Organization Studies, 30, 1329-1347. [07]. Bryant, A. & Charmaz, K. 2007. The Sage Handbook Of Grounded Theory, Sage. [08]. Stake, R. E. 2006. Multiple Case Study Analysis, Guilford Press. [09]. Oxford English Dictionary. Oxford English Dictionary. 2013 [cited 2013 02 December]; Available from: http://www.oxforddictionaries.com/us. [10]. Merriam-Webster Dictionary. Merriam-Webster Dictionary,. 2013 [cited 2013 02 December]; Available from: http://www.merriam-webster.com/. [11]. Giddens, A., The constitution of society: introduction of the theory of structuration. 1984: Univ of California Press.


VIRTUAL CONSTRUCTION SIMULATOR AN EDUCATIONAL SIMULATION GAME IN CONSTRUCTION ENGINEERING Fadi Castronovo1, John I. Messner, Ph.D.1

1

Department of Architectural Engineering, The Pennsylvania State University, University Park, PA 16802

PREVIOUS WORK: THE VCS LEGACY

ABSTRACT

RESEARCH GOALS

The construction of a facility is a dynamic process, governed by complicated problems and solutions. This complex nature poses instructors the difficult task of developing pedagogical strategies to teach engineering students how to tackle such processes. Traditional construction planning and management teaching methods have been criticized for presenting students with well-defined problems, which don’t reflect the challenges present in the industry. An innovative teaching method – educational simulation games – has shown potential in teaching students complex construction processes, in a simulated construction environment. In addressing these instructional challenges, we will illustrate the lessons learned in the development and design of complex serious games. The work presented is a result of the current research efforts in the development of the Virtual Construction Simulator (VCS) a project supported by the National Science Foundation.

To improve construction engineering education through the use of interactive construction project simulation game and engage students in scenarios to improve their construction method knowledge, planning and decision making skills.

VIRTUAL CONSTRUCTION SIMULATOR 4

The Virtual Construction Simulator (VCS) is a simulation game that teaches students the dynamic nature of the construction process and frequent changes to construction schedules.

RESEARCH PROCEDURE We are currently expanding the previous research efforts through an innovative gaming platform, additional content, levels of difficulty, an improved userinterface VCS4, and modularity to support custom learning scenarios. DESIGN OR REVISE USER INTERFACE AND SYSTEM DYNAMICS DEVELOP OR REVISE LEARNING OBJECTIVES

VCS1: Developed using the Deep Creator game engine

NO

DEVELOP ASSESSMENT METHODOLOGY

PERFORM ASSESSMENT

ARE THE OBJECTIVES MET?

YES

IMPROVE GAME

VCS3: Developed using the XNA Game Studio

OVERVIEW ASSESSMENT METHODOLOGIES

VCS2: Developed using the Irrlicht 3D rendering engine

NEXT STEP Evaluate the effectiveness of the VCS simulation game.

CHOOSE THE CONSTRUCTION SITE

STRATEGIZE THE CONSTRUCTION PLAN SIMULATE THE CONSTRUCTION PROCESS

STEEL MODULE

VCS4 VCS4 CONCRETE MODULE

SOUTH HALLS ADDITION STEEL ADDITION REPORT SOUTH HALLS CONCRETE REPORT

Select Project Select Project Select Project

Begin Game Begin Game Begin Game

View Application Information View Application View Information Application Information Input Username Or ID Input Username Or ID Input Username Or ID Exit Game

Load Buffering Scene Load Pavilion Buffering Project Scene Load Buffering Pavilion Load Project Scene Buffering Pavilion Scene Project Load Concrete Buffering Project Scene Load Buffering Concrete Project Load Scene Buffering Concrete Scene Project Load Steel Buffering Project Scene Load Buffering Steel Project Scene Load Steel Application Project Information Load Scene Application Load Information Application Scene Information Scene User Profile

User Profile User Profile

Exit Game Exit Game

Fadi Castronovo Fadi Castronovo 1

EXPERIENCE THE CONSTRUCTION SITE

TEST YOUR management SKILLS

Develop adoption and adaptation guidelines for dissemination.

REVIEW YOUR PERFORMANCE

VCS

ACKNOWLEDGMENTS

PENN STATE COMPUTER INTEGRATED

CONSTRUCTION

PLAY SIMULATE learn

CONTACT

We would like to acknowledge the University of Washington and the University of Reading for their collaborations and contributions.

Fadi Castronovo fadi@psu.edu

The authors thank the National Science Foundation for support of this project. Any opinions, findings, conclusions, or recommendations expressed here are those of the authors and do not necessarily reflect the views of the National Science Foundation. (Grant No. 0935040)

John I. Messner, PhD jmessner@engr.psu.edu

GET THE VCS FOR FREE AT: www.engr.psu.edu/ae/cic/vcs/


Predictive Emissions Models for Excavators Heni Fitriani; M. Phil Lewis, Ph.D., P.E. School of Civil & Environmental Engineering, Oklahoma State University ABSTRACT

Table 3. Summary of MLR Equations and Coefficient of Determination (R2)

Multiple Linear Regression (MLR)

The purpose of this poster is to demonstrate two different predictive modeling methodologies for estimating emission rates for Heavy-Duty Diesel (HDD) construction equipment. The model were

Equipment MLR is used to model the relationship between a dependent variable (pollutant emission rate)

developed using real-world data collected from in-use HDD equipment. The modeling methodologies

and two or more independent variables (engine parameters). The general form of the MLR

used here are Multiple Linear Regression (MLR) and Artificial Neural Network (ANN).These modeling

model is:

techniques were used to produce models to predict emission rates of NOx, HC, CO, CO2, and PM.

Y1 = β0 + β1X1 + β2X2+ β3X3

NOx HC CO CO2 PM

where:

INTRODUCTION

Y1

= Emission rates (Either NOx, HC, CO, CO2, or PM in grams per second)

HDD plays a significant role in emitting pollutions into the atmosphere which result in human

X1

= Manifold Absolute Pressure (MAP in Kilo Pascal)

health and environment problems. This poster presents a comparison study for estimating

X2

= Revolutions Per Minute (RPM)

emission rates of three excavators using Multiple Linear Regression (MLR) and Artificial Neural

X3

= Intake Air Temperature (IAT in Celsius degrees)

Network (ANN). Engine performance data that include manifold absolute pressure (MAP),

NOx HC CO CO2 PM NOx HC CO CO2 PM

β0, β1, β2, β3 = Coefficient of linear relationship

revolutions per minute (RPM), and intake air temperature (IAT) were used to predict the emissions of nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO), carbon dioxide (CO2), and particulate matter (PM).

Artificial Neural Network (ANN) ANN is a computational model that simulates brain functions based on how the human brain

 Input layer (X)

Second-by-second emissions data for NOx, HC, CO, CO2, and PM were collected, along with engine attribute data, for three excavators using a Portable Emissions Monitoring System (PEMS)

0.8838 0.4021 0.3395 0.9715 0.9124 0.8798 0.2459 0.0964 0.9338 0.9303

Table 4. Comparison of Model Validation for MLR and ANN

Equipment

 Hidden layer (H)  Output layer (Y) EX 1

known as the Montana. Table 1 presents a summary of engine data for these excavators.

Table 1. Summary of Engine Attribute Data EX 2 Horsepower

Displacement

Model

Engine

(HP)

(Liters)

Year

Tier

Excavator 1

285

8.3

2001

1

Excavator 2

138

6.4

2003

2

Excavator 3

93

3.9

1998

1

PEMS

EX 3

RESULTS

NOx HC CO CO2 PM NOx HC CO CO2 PM NOx HC CO CO2 PM

MLR m 0.9435 0.5732 0.7729 1.0127 0.8734 0.8873 0.4407 0.3217 0.9739 0.9174 0.8780 0.2426 0.1052 0.9333 0.3843

b 0.0036 0.0016 0.0028 3.6904 0.0997 0.0062 0.0029 0.0128 0.2058 0.0530 0.0072 0.0042 0.0070 0.3540 0.2517

ANN R2 0.9514 0.5748 0.7590 0.9828 0.8856 0.8791 0.4339 0.3270 0.9710 0.9094 0.8778 0.2387 0.1000 0.9340 0.3872

m 0.9749 0.7685 0.9121 0.9913 0.8887 0.9012 0.4589 0.5504 0.9689 0.9400 0.9128 0.6549 0.2707 0.9547 0.7695

b 0.0030 0.0008 0.0010 0.1119 0.1080 0.0049 0.0027 0.0086 0.1665 0.0204 0.0052 0.0019 0.0054 0.2370 0.0896

R2 0.9624 0.7402 0.8836 0.9852 0.8786 0.8990 0.4595 0.5699 0.9747 0.9530 0.9144 0.6535 0.2683 0.9593 0.7911

CONCLUSIONS & RECOMMENDATIONS •

MAP has a strong positive relationship with emission rates of NOx, CO2, and PM, but a moderate positive relationship with HC and CO. RPM is the second most influential variable that affects the emission rates. IAT has a low correlation to emission rates.

In most cases, the MLR approach produced highly precise models for NOx, CO2, and PM. The models for HC and CO were less precise with R2 values ranging from 0.09 – 0.80.

ANN models performed the best with regard to precision, accuracy, and bias.

The results should be used as a means to help evaluate pollution mitigation strategies for HDD equipment.

The study should be expanded to include other equipment types such as backhoes, bulldozers, motor graders, wheel loaders, track loaders, and off road trucks.

and model comparisons for both methodologies. Table 2 shows the summary of Pearson correlation coefficients for each excavator, which reflects the relationship between engine data and emission rates . Table 2. Summary of Pearson Correlation Coefficients Equipment EX 1

EX 2

Excavator

Pollutants

Figure 2. ANN Structure (Barry & Linoff, 2004)

This section presents the results of the MLR and ANN models, including variable correlations

Figure 1. Diagram for Installation of PEMS for HDD Equipment

0.9537 0.5821 0.8007 0.9847 0.8799

works. The ANN is comprised of three layers:

DATA COLLECTION METHODOLOGY

Equipment

R2

Equation Excavator 1 Y2 = -0.2093 + 0.00247 X1 - 0.00002 X2 + 0.00018 X3 Y3 = 0.0056 + 0.000034 X1 + 2.64E-6 X2 – 0.00021X3 Y4 = - 0.00003 + 0.000041X1 + 0.000011X2 - 0.00018X3 Y5 = -18.21 +0.2302X1+ 0.00093 X2 - 0.093X3 Y6 = -12.21+ 0.0293X1 –0.0136X3 Excavator 2 Y2 = - 0.089 + 0.00082 X1 +0.000024 X2 +0.00013X3 Y3 = - 0.0024+ 0.000048X1 +3.14E-6X2 - 0.00008X3 Y4 = 0.0039 + 0.000013 X1 + 0.00002 X2 - 0.00024 X3 Y5 = -16.05+ 0.166X1 + 0.0021 X2 – 0.026 X3 Y6 = -1.530+ 0.021X1-0.00026X2 – 0.0064X3 Excavator 3 Y2 = -0.079 + 0.00096X1 – 05.33E-6X2 + 0.000096X3 Y3 = -0.0071 +0.000034X1 +1.57E-6X2 + 0.000094X3 Y4 = 0.0094- 0.00005X1 +9.92E-6X2 - 0.00018X3 Y5 = -7.41 +0.0932X1 + 0.00017X2- 0.022X3 Y6 = -1.142 +0.00814X1 - 0.00013X2 + 0.0104X3

EX 3

Engine Data

NOx

HC

CO

CO2

PM

MAP

0.9737

0.5920

0.7367

0.9909

0.9386

RPM

0.7352

0.6324

0.8547

0.7971

0.7391

IAT

0.5893

0.0704

0.3720

0.5650

0.5137

MAP

0.9219

0.6245

0.4684

0.9815

0.9421

RPM

0.8511

0.6210

0.5682

0.8512

0.6894

IAT

0.5649

0.3294

0.2967

0.5457

0.4359

ACKNOWLEDGEMENT

MAP

0.9357

0.4400

0.1353

0.9640

0.5767

The Authors acknowledge the use of the real-world nonroad equipment and emissions database

RPM

0.7917

0.4182

0.2254

0.8397

0.4689

IAT

0.3998

0.3578

-0.1177

0.3218

0.4366

that was developed at North Carolina State University by Dr. Chris Frey and Dr. William Rasdorf.


Estimating Extreme Event Recovery with Construction Activity Change Points Henry D. Lester, Ph.D. Candidate Advisor: Gary P. Moynihan, Ph.D. The University of Alabama, Department of Civil, Construction, & Environmental Engineering ABSTRACT Repairing and rebuilding structures following an extreme event requires a substantial outlay of resources to achieve full disaster recovery. Demographic shifts toward high-risk communities are increasingly placing both populations and the built environment at substantial loss exposure to such extreme events. Instead of retaining extreme event risks associated with these communities, property owners expect to transfer the risk to government regardless of the exploding municipal debt. The shrinking municipal assets restrict availability of resources for extreme event recovery operations. The associated construction increases in disaster-prone areas dictate disaster planning to safeguard at risk populations during the recovery phase of the disaster life cycle and this planning necessitates a temporal recovery metric. This poster presents research employing a change point approach to estimating extreme event built environment system recovery. Specifically, the research considers Fisher type price adjusted spatial new single-family residential building permits as a time series signifying residential construction activity. The research compares changes in this residential construction activity with accompanying declared disasters to ascertain any relationships. The approach examines spatiotemporal residential construction variability to determine built environment rapidity by measuring the duration between sequential time series change points. The change point approach and resultant temporal recovery metric allows estimation of extreme event built environment system recovery for decisions makers to conduct operative disaster planning and municipal resource allocation.

METHODOLOGY

METHODOLOGY

OBJECTIVE To determine a spatial-temporal metric ( ! ) for coastal residential built environment system rapidity for future public policy decisions concerning disaster resource allocation.

â&#x20AC;˘ Monthly Single-Family Housing Units

SCOPE â&#x20AC;˘

Nationally Available Data â&#x20AC;˘ Spatial Component: Baldwin County, Alabama â&#x20AC;˘ Temporal Component: 1996-2012 â&#x20AC;˘ Extreme Event Component: Major Disasters

RESEARCH DESIGN â&#x20AC;˘ Process Control â&#x20AC;˘ Discount: Construction Price Index â&#x20AC;˘ Trend: Construction Cost per Unit â&#x20AC;˘ Seasonality: Moving Average â&#x20AC;˘ Change Point Analysis â&#x20AC;˘ Temporal Recovery Model

  construction cost per single-family unit for month    total singleâ&#x2C6;&#x2019;family construction price for month    total quantity of singleâ&#x2C6;&#x2019;family construction permits for month    Fisher price index for month  with 2005 base year

CHANGE POINT ANALYSIS Construction Process Assumptions â&#x20AC;˘ In-Control â&#x20AC;˘ Normal Distribution â&#x20AC;˘ Independent & Identically Distributed Random Observations

DATA COLLECTION â&#x20AC;˘ Built Environment Data: Construction Permits (U.S. Census) â&#x20AC;˘ Extreme Event Data: Federal Disaster Declarations (FEMA)

Where:

â&#x20AC;˘ â&#x20AC;˘

Shifting Demographics Toward High-Risk Areas Increased Population and Built Environment Risk Exposure

' '

â&#x2C6;&#x2018;1 )* + 0

Where:

)*  stratum proportionality factor +,  price factor for current period +-  price factor for base period .  quantity for base period

PROBLEM Homeowner reliance on governmental post-disaster reconstruction assistance may exceed the governmentâ&#x20AC;&#x2122;s ability to cover the cost of that assistance.

What duration should the government utilize in determining expected resource allocation levels of post-disaster reconstruction assistance programs?

'

  1  



Where:

 !  extreme event recovery period "  extreme event intial change point "#$  extreme event final change point

Adjusted Temporal Recovery Model

 6

â&#x20AC;˘ Equalizes Inflation

  construction cost per singleâ&#x2C6;&#x2019;family unit for month    ith location parameter   scale parameter   ith change point

'â&#x2C6;&#x2018;1 )*+0

Fisher Price Deflator 6

' â&#x2C6;&#x2018; 78 01 23 45 ' â&#x2C6;&#x2018; 78 01 23 43

'

Time-Weighted Control Charts analysis along with comparison of locale extreme events yields the following estimates for Recovery Time ; ! <.

Unadjusted Temporal Recovery Model

â&#x20AC;˘ Underestimates Inflation â&#x20AC;˘ Lower Bound

QUESTION

  2  1, 2  2, â&#x20AC;Ś , 3 â&#x2039;Ž   1  1, 1  2, â&#x20AC;Ś , 

, 2

â&#x2C6;&#x2018;1 )* + 

)/  stratum proportionality factor +,  price factor for current period +-  price factor for base period ,  quantity for current period

 

 ~   , 2 2

TEMPORAL RECOVERY MODEL

 

Increased Expected Risk Transfer to Government Multiple Assistance Programs Exploding Federal Debt Shrinking Resources Restrict Recovery Operations

  1, 2, â&#x20AC;Ś , 1   1  1, 1  2, â&#x20AC;Ś , 2

 1

Paasche Type Price Index

â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘ â&#x20AC;˘

 0 , 2  1 , 2

â&#x2C6;&#x2018;1 )* +0 0

â&#x20AC;˘ Overestimates Inflation â&#x20AC;˘ Upper Bound

Where:

1996-2012 Plots of Three Month Time-Weighted Control Charts (Moving Average Chart and Exponentially Weighted Moving Average Chart) of Construction Cost per Single-Family Residential Unit for Baldwin County Alabama

Change Point Model

Constant Quality (Laspeyres) Price Index

& 

1996 -2012 Fisher Adjusted Monthly Construction Cost per Single-Family Unit for Baldwin County Alabama

1 

Where

CONSTRUCTION PRICE INDEX

SITUATION

Baldwin County Alabama

CONSTRUCTION COST PER UNIT

    

RESULTS

â&#x2C6;&#x2018;6

78

01 25 45

'

â&#x2C6;&#x2018;6

78 01 25 43



9 :

Where:

!

 "#$  "  %

 !  extreme event recovery period "  extreme event intial change point "#$  extreme event final change point %  extreme event lag adjustment

CONCLUSIONS â&#x20AC;˘ Appears some consistency exist between construction activity and extreme events â&#x20AC;˘ Provides decision makers an additional tool for operative disaster planning and municipal resource allocation. FUTURE W ORK â&#x20AC;˘ Unexplained Signal â&#x20AC;˘ Disasters are not the only built environment system shocks â&#x20AC;˘ Fisher Price Deflator â&#x20AC;˘ Upper Gulf Coast Strata â&#x20AC;˘ Refine Approach â&#x20AC;˘ Additional Construction Data


BIM-based Integrated Approach for Optimized Construction Scheduling under Resource Constraints Ph.D. Student: Hexu Liu

E-mail: hexu@ualberta.ca

Problem Statement:

Supervisor: Mohamed Al-Hussein and Ming Lu

System Architecture:

• Currently, BIM provides limited information (e.g., quantity take-offs) for the downstream scheduling analysis, entailing a large amount of manual effort to generate the schedule. • Schedule generated from BIM is at the project-level, and do not delve into different construction operations requesting different construction resources, thereby producing activity-level construction schedules. • Resource constraints is not taken into consideration in BIM-based scheduling.

Processor

MS Project

Access

On-Site Schedule

WBS Information

Relationship Analyser (SSR Analyser)

Objectives: • Automatically generate detailed construction schedules. • Automatically optimize construction schedules under resource constraints.

Proposed Methodology:

Process Simulation Model

Revit Application Programming Interface (API)

Case Study:

Work Breakdown Structure (WBS)

3D BIM Product Model (Autodesk Revit)

• • • •

Table-based Work Package Information

On-Site Schedule (MS Project)

Move Through

Entities with Enriched Information

Work Package Priority for Resoures

Fitness value

Record Schedule Results

Construction Process Simulation Model (Simphony)

Total Project Duration (min)

Object-oriented Rich Product Information

Priority-based Evolutionary Optimization Model

Optimization Model

3D Product Model

52000 51000 50000 49000 48000 47000 46000 45000 44000 43000

Evolution process Project duration of swarm Project duration of one particle

0

10

20

30

40 50 Iterations

60

70

80

90

100

Schedule Results from the Prototype System

2 storeys 60 non-bearing walls 122 bearing walls 29 concrete wall footings


Copyright © 2009 Jonathan Feinberg

Ras Tanura Refinery, 1947

“ Piping & Instrumentation Diagrams (P&IDs) is used to define the process. It provides a list of engineering Pre-Processing items and their functional Density Equivalence – Point Spacing relationships.” (Sampath and Shan 2007; Tooke et al. 2013)

© 2012 Saudi Arabian Oil Co.

Process Engineering, 1970 Created by Tyler Leach, Denise Smith & Melissa Trowsdale, UWO-LIS 9672, 2012

Segmentation Disjoint Intersection – Smoothness Constraint (Rabbani et al. 2006)

“ Unfortunately, there are no reliable digital drawings of as-built 3D conditions for the old, outdated plants…”

Pipeline Extraction Segment Classification – Local Surface Curvature

by Masuda and Tanaka (2010); Trigui (2011)

(Son et al. 2013a)

Laser Scanning http://www.cowi.com/

Neighborhood Searching

Manual Modeling from Scan Data

Infer & Find Adjacent Objects – Adjacency

http://www.tecnatom.es/

(Bremer et al. 2013; Son et al. 2013b)

Feature Extraction Geometric & Topological Representation – Tree-Structured Graph (Agathos et al. (2010), Shapira et al. (2010), Chang and Kimia (2011), and Chao et al. (2011); Son et al. 2013b)

Graph Matching Assessing “ Goodness of Fit ” – Similarity (Conte et al. 2004; Marini et al. 2007; You and Tsai 2010; Son et al. 2013b)

(Leica Geosystems AG, Heerbrugg, Switzerland)

Retrieving 3D Models

“ There has been an upsurge of industrial interest in the field of …”

Leica Cyclone 8.0

Industrial Practice Using Commercial Software Step 1. Import scan data Step 2. Find all of the pieces of instrumentation at the installed locations in a 3D environment <· · · by Manual Step 3. Identify the instrumentation type for each individual piece <· · · by Manual Step 4. Create 3D instrumentation models by fitting primitives or with other http://hdsblog.co.uk/category/high-definition-surveying2/page/4/ spatial data sources <· · · Semi-Automated Step 5. Repeat “ Steps 1–4 ” until the hundreds of pieces are completely reconstructed · · ·> Time Consuming & Unreliable! Masuda and Tanaka (2010)

“ There have been few studies that have attempted to address the issue of automatic, accurate, and cost-efficient method for 3D as-built reconstruction.” Rabbani et al. (2006)

Reisner-Kollmann et al. (2010)

Data-driven Approach “ It is challenging to reconstruct each object with their details without any supplementary information, as objects become increasingly complex.”

Industrial Plant Located in Yeosu, South Korea, 2013 · · ·> Laser-Scan Data

Main Objective: To use available information to facilitate and automate the process for 3D as-built reconstruction of industrial instrumentation from scan data Photographic Image <· · ·

Summary The proposed approach makes use of available information in the process – equipment valve such as the information that is extracted from the existing P&ID. This prior information was utilized to identify and characterize each instrumentation in the laser-scan data. Results The experimental results showed the practicality of the approach for modeling as-built industrial instrumentation. Guided by this supplementary information, the approach facilitates the process. Contributions/ Impacts This approach overcomes major limitations that arise when industrial instrumentation is treated as primitive shape and when only geometric characteristics are considered in the matching and retrieving steps. All object models were tagged with their information predefined in the P&ID. Adding such semantic information is beneficial for many managerial purposes. Recommendations Future work will be devoted to experimentation on domains with more complex scenes, as well as a wider range of processes. reactor tower heat exchanger pump davit tank etc.


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