Posterpresentationbinder alitouran

Page 1

Monitoring Construction Progress at the Operation-Level using 4D BIM and Site Photologs Kevin Han (kookhan2@Illinois.edu), University of Illinois at Urbana-Champaign; Advisor: Mani Golparvar-Fard Limitations of Previous Research

Project: Student Dining and Residence Hall, Champaign, IL Images used courtesy of Turner Construction Co.

Unordered and uncalibrated daily construction site photos 

4D as-built point cloud model automatically generated from daily site photos

4D point cloud models superimposed over 4D BIM generating D4AR models

BIM LOD and Schedule WBS may not be detailed

-

Material Classification Using small image patches

-

Z-buffer for dealing with static occlusions

-

Formalized knowledge of Construction Sequencing

-

Leveraging IFC-based BIM for reasoning in models with low LOD in BIM

For those elements with no visibility, progress is not reported.

IFC-based building information model is superimposed with the asbuilt point cloud model.

Automated as-built reconstruction with 160 photos collected along the site walk

Integrated & automated visualization of progress with photos, point cloud and BIM

FP and FN in monitoring

Point of Departure

• Model-based construction progress monitoring leveraging “relationship” and “flexibility” semantics from IFC

FN in Detection: Occlusion should be accounted for on:

FP in Detection: Formwork should not be detected as evidence of progress

Method for Inferring State of Progress based on Construction Material Classification

Results, Contributions, and Practical Significance Results per Image

Appearance-based Detection • Has potential for operation-level assessment • May work in low visibility situations (only requires 30x30 image patches)

Back-Projected BIM onto an Image Depth map of BIM model

Construction Material Library

Machine Learning Algorithm (Dimitrov & Golparvar-Fard 2014)

Confusion Matrix

Train

Sample Image Patches Infer Material

Machine Learning Algorithm ( HSV + LM)

HSV (3D)

Class #5: Concrete

Performance of the Proposed Algorithm:

LM Filters (48D)

Formwork: 100% Concrete: 90%

36 oriented filters at 6 orientations, 3 scales, and 2 filters & 8 derivative filters and 4 low-pass Gaussian filters.

Statistical Representation of Results: Appearance Frequency Diagram

Classified Material Types For Inspected Elements

Hue, saturation, & value (brightness)

K-Means Intensity Contrast and Color Cluster and Texton+HSV Normalize Filter Bank Form Texton Histograms Dictionary

Mat’l w/ Highest Freq. vs Second Highest Freq. Categorize & Label Material

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131

D4AR Web Viewer

Overall Workflow

Back-projected BIM onto an Image

D4AR Visualization

Frequency

Elements # Material with highest freq

Material with second highest freq.

Open Research Questions • Formalizing sequence of construction activities • Comprehensive “Construction” Material Datasets • Dealing with intra-class variability

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

Estimating Optimal Labor Productivity: A Two-Prong Strategy Author: Krishna Kisi; email: kkisi@unomaha.edu School: The Durham School of Architectural Engineering and Construction, University of Nebraska-Lincoln Advisor: Eddy Rojas; email: er@unl.edu

Introduction: In an attempt to evaluate the efficiency of labor-intensive construction operations, project managers compare actual with historical productivity for equivalent operations. However, this approach towards examining productivity only provides a relative benchmark for efficiency and may lead to the characterization of operations as authentically efficient when in reality such operations may be only comparably efficient. Optimal productivity, the highest sustainable productivity achievable in the field under good management and typical field conditions, can provide an absolute benchmark for gauging performance. This poster demonstrates development and implementation of a two-prong strategy for estimating optimal productivity in labor-intensive construction operations. The first prong represents a top-down approach that estimates optimal productivity by introducing system inefficiencies into the productivity frontier which is theoretical maximum productivity that would be achieved under absolutely perfect conditions in all respects. A qualitative factor model is used to estimate the impact of system inefficiencies. This topdown approach yields the upper level estimation of optimal labor productivity. The second prong is a bottom-up approach that determines optimal labor productivity by removing non-contributory work from actual productivity in a discrete event simulation. This bottom-up approach generates the lower level estimation of optimal labor productivity. An average of the upper and lower limits reveals the best estimate for optimal labor productivity.

CONCEPT DEVELOPMENT

BACKGROUND

PF (Productivity Frontier)

Productivity

∆si

∆’si

OPUL (Upper Limit of Optimal Productivity)

OP (Optimal Productivity)

∆i ∆oi

OPLL (Lower Limit of Optimal Productivity)

∆’oi

AP (Actual Productivity)

tn

Time

Where,

Δi = Total Inefficiencies Δsi = System inefficiencies Δoi = Operational inefficiencies Δ’si = Estimate of System inefficiencies Δ’oi = Estimate of Operational inefficiencies

tn+1

Figure 2: Different Produc5vity Levels at Steady State Phase

RESEARCH METHODOLOGY Figure 1: Produc5vity Dynamics (Source: Son and Rojas 2010)

FEASIBILITY TEST: PILOT STUDY Data collection and analysis were based upon video recording collected during a pilot project. The project included lighting replacement activity conducted by Common Wealth Electric Company at Omaha South High Magnet School. In this study, QFM was used to estimate system inefficiencies and DES yielded operational inefficiency estimates.

Qualitative Factor Model (QFM) Where: ∆′!" = estimate of productivity loss due to system inefficiencies. ∆′(!"!!"!! ) = estimate of the difference between productivity frontier and the lower limit of optimal productivity. n = number of parameters. m = number of productivity factors. z = work zone (classrooms, lockers, and corridor/hallways). i = system inefficiency factors in each work zone z. Si = severity score of individual productivity factor i. Pi = probability of individual productivity factor i. TSi = total severity score (sum of severity scores for all productivity factors). Wz = relative weights of each work zone.

Figure 3: Research Methodology

CONCLUSIONS

Discrete Event Simulation (DES) Model Resource Arrival

Site Mobiliza5on

Frame Cover Removal

Close Ballast Cover

New Bulb (T8) Installa5on

Old Bulb (T12) Removal

Test OK? New Bulb Replacement

True

Ballast Cover Removal

Old Ballast Removal

Frame Cover Closure

Transfer

New Ballast Installa5on

Disposal

False

Figure 4: Discrete Event Simula5on (DES) Flow-­‐Chart

PILOT STUDY RESULTS Problem Identification

Breakdown of Problem

Modeling Technique

Productivity Value

FUTURE WORK Model Estimate

Model Output

Optimal Estimate

Δsi QFM PF (20.23) Δ’si (2.15) OPUL(18.08) OP (15.92) Δoi DES AP (12.62) Δ’oi (1.14) OPLL(13.76) • Unit of all numbers are in stations per hour!! • Fluorescent bulb replacement task achieved 80.4% efficiency (AP as a percentage of OP) Δi

An accurate estimation of optimal productivity allows project managers to determine the absolute (unbiased) efficiency of their labor-intensive construction operations by comparing actual vs. optimal rather than actual vs. historical productivity. The proposed two-prong strategy for estimating optimal labor productivity was successfully applied in this pilot study. Therefore, this pilot study demonstrates that the proposed methodology for estimating optimal productivity in labor-intensive construction operation is adequate when applied to a simple electrical operation. This study contributes to the body of knowledge in construction engineering and management by introducing a twoprong strategy for estimating optimal productivity in labor-intensive construction operations.

Future research will be focused towards the applicability of this methodology, its usefulness, and reliability across all construction operations. The research will be conducted on more complex construction operations, such as those involving crews with multiple workers performing parallel and sequential work, such as Sheet metal, plumbing, and electrical works.

An Investigation of Occupant Energy Use Behavior and Interventions within a Residential Context Kyle Anderson and SangHyun Lee Civil and Environmental Engineering, University of Michigan

UM Dynamic Project Management Group Kyle Anderson (kyleand@umich.edu) Advisor: SangHyun Lee Civil & Environmental Engineering University of Michigan

Introduction

Research Framework

Energy Wastes in Residences

To date, innumerable efforts have focused on technological approaches to reduce building energy consumption; however, in the end, all buildings are operated by humans, and how humans decide to behave in them has a significant and meaningful effect on energy demand. Unfortunately, it is still not well understood how and why occupants choose to behave as they do in buildings, how behavior settings influence the spread of behavior, and what methods are effective at promoting durable improvements in occupant energy use behavior.

In order to complete the research objectives I have developed a twopronged research framework that integrates computer modeling and field experiments.

Unfortunately, studies to date using field-collected data have only investigated energy waste within commercial and/or non-domestic buildings and little is known regarding the quantity of energy waste in the residential sector.

Data Collection -Seven midrise dormitory buildings housing over 1,000 single and double occupancy rooms at Seoul Nation University in Seoul, South Korea. -Hourly individual occupancy data and hourly room electricity use from January 1, 2013 through December 31, 2013.

Objectives 1. Explore occupant behavior patterns in order to identify opportunities for improvement and barriers to change 2. Enhance our understanding of how contextual factor, especially social networks, influence the spread of energy use behavior 3. Test the durability of novel behavior intervention strategies and identify how and why residents alter their behavior

Exploring the Role of Social Networks in the Diffusion of Energy Use Behavior Agent-based modeling is used to model the diffusion of behavior using the theory of social influence [1]. These studies simulate deploying a normative behavior intervention (e.g., installing HEMS that provide comparative feedback to residents) in a building. The first study looks at network type, size, and density, while the second examines dynamicity. START

END

Check Moveout

YES

YES

NumOcc < Max

NO

t = t +1

NO

Dynamic? Variance in Outcome by Dynamicity

NO

7

Experiment Settings Connectivity (K) Social Network Size (N) Turnover Rate Occ. Energy Use

Study 1-1 Study 1-2 Study 2 2, 4, 6, 8, 10, 12 6 4 SFN, SWN, RND, REG SFN, SWN, RND, REG SWN 35 49 7, 35, 441 N/A N/A 32% annual All use mean 168 W and SD 123 W

Static

35

441

Dynamic

Network Size

Social Network

120 100 80 60

1. It was found that the average room consumed 30.2% of all electrical energy use (including plug loads, lighting, heating, and cooling) during periods of non-occupancy. 2. No meaningful relationship was found between total energy use and percentage of energy waste for individuals; high and low users had energy waste proportional to his/her total consumption.

Ongoing Research

40

120 100

Kruskal-Wallis p = 0.001186

Kruskal-Wallis p < 2.2e-16

20

YES

20 to Reach 40 Steady State 60 Behavior 80 Time

i < NumOcc?

80

i= 0

60

NO

40

Dynamic?

Mean SD in Energy Use Behavior (Watts)

t = t +1

NO

Influence & Energy Use Calculation

20

Steady State? Time = Max?

State Time -50to Reach Steady 0 50 Behavior100

Change in Energy Use by Dynamicity

YES

Social Network Generation

Main Energy Waste Conclusions

Change in Energy Use Behavior (Watts)

Occupant Initialization

Initialize New Occupant

7

35

Static

441

Dynamic

Network Size

Social Network

Main Modeling Conclusions 1. Social network structure can affect the time for interventions to take effect and increase uncertainty. 2. Dynamic social networks increase uncertainty in intervention outcome relative to static networks.

We are currently conducting a large scale field experiment at Seoul National University that is testing the effectiveness and durability of automated personalized normative feedback messages. Additionally, through multiple surveys we are gathering data on social network structure, the role of social networks in norm diffusion, identifying salient behavioral determinants, measuring spillover effects, and change in each of the aforementioned items over time. [1]

Friedkin, N. E. (2001). “Norm formation in social influence networks.” Social Networks, 23(3), 167–189.

Modeling and Visualizing the Flow of Trade Crews Using Agents and Building Information Models (BIM) Lola Ben-Alon, slola@tx.technion.ac.il

The Faculty of Civil and Environmental Engineering

VClab

SeskinVirtual Construction Laboratory

Step #1 - Simulating LEAPCON(TM) construction game

Problem

First step was to develop of an agent based model to simulate the process incorporated in the LEAPCON game. The model was calibrated according to ďŹ eld observations of LEAPCON players, with respect to the players' motivations and production rate variability. This step has been implemented using the STARLOGO TNG tool for multi-agent simulations which provides a development environment within a 3D visual context. The agent-based simulation of the 6 Descrete Event Simulation LEAPCON game was developed with 18000 ABS with 4 agents 5 agents for the four independent specialty ABS with 5 agents 14000 4 ABS with 3 agents subcontractors, the client representative 3 10000 and the quality controller, And for each of Descrete Event Simulation ABS with 4 agents 2 the 32 apartments considered. The 6000 ABS with 3 agents 1 results show good calibration with ABS with 5 agents 2000 0 existing observed ďŹ eld data, and to the -2000 0 500 1000 1500 0 500 1000 1500 existing DES. The effects are shown by Time [sec] Time [sec] -6000 Fig. 5: LEAPCON Agent Based Simulation measuring Work In Progress (WIP), Cycle Fig. 3,4: Results of 1000 runs of the DES and the ABS Time (CT), cash ow patterns and efďŹ ciency of the operations.

Objective The main objective is to develop a visual simulation for studying and improving production control in construction processes, which accounts for individuals' decision making process and acquired knowledge. The goal is to make the model robust by attempting to calibrate it with ďŹ eld observations. Unlike the few existing research models, the simulation is situated in a realistic virtual environment modeled using BIM, allowing future experimental setups that can incorporate human subjects.

Cash Flow [$]

Simulation methods are useful for experimentation of production in construction because ďŹ eld experiments suffer difďŹ culties with isolating cause and effect. However, existing methods such as DES (Discrete Event Simulation) are limited in terms of their ability to model decision-making by individuals who have distinct behavior, context and knowledge representation.

WIP [units]

Assoc. Prof. Rafael Sacks, cvsacks@tx.technion.ac.il

Step #2 - Simulating ow of trade crews in Unity 3D An Agent-Based model developed in UNITY 3D game engine was created to simulate production control of a process in a full-scale building project. This step is being pursued in collaboration with a construction company. Data on workers' motivations, behavior and performance was collected using interviews and observations of a crew performing ďŹ nishing works in a high-rise residential tower project. This step presented the following challenges: • Correct observation and formulation of the variables and target function (motivations) of the agents. • Modeling the behavior of the agents while classifying professions. • Validation by calibration with actual performance.

Method The method employs agent-based simulation (ABS), with a “bottom-upâ€? approach to model the interactions between individual agents. It uses BIM models to deďŹ ne the physical and the process environment for the simulation. We apply agents programmed with decision making rules and utility functions to a to-be-built environment represented as a BIM. By varying parameters such as reliability between workers, thresholds for information gathering and approach to making-do in terms of risk, it is possible to generate aggregate system performance similar to those found in an actual building context in the construction

SigniďŹ cance

General Model The agents’ behavior was modeled using Behavior Trees (BT), an AI technique for modeling decision making used in commercial games, integrated with utility function. Moreover, the model reects the reliability of information, and distinguish between reality and perceived reality. SigniďŹ cantly, this allows the experimentation of uncertainty, lack of and ignored information

Lean construciton and BIM research has revealed the potential of novel ways to organize production on site that exploit the beneďŹ ts of pull ow and thorough yet exible planning. The simlation platform developed in this work is uniqly capable of testing their impact because it models the complex, emergent patterns of production behavior that result from the interaction of the myriad subcontracting teams and suppliers that perform construction work on and off site. In particular, the inuence of each participants' knowledge, context and motivations on their day to day decisions about resource allocation and work sequence can be modeled in the ABS, while they could not be modeled using DES. To-date, there has been no simple and reliable way to test different ideas for production control paradigms in construction. Employment

Formula 1: The subcontractor’s utility fuction

Work

S elect Where to Work

References Wait

G ather Info

Low U tility in Low C ertainty ? Is In S elected Location?

đ?‘˜ đ?‘– đ?‘Š đ?‘ˆ đ??ś đ?‘– đ?‘˜ đ?‘Š đ?‘Š

Maturity factor of apartment i Actual work performed on project i during any period T Unit price for the works at apartment i Unit cost of the materials for the works at apartment i Ratio of resources supplied to resources demanded Work promised/demanded by general contractor of project I in period T Work actually made available in period T, according to the sub-contractor Cost/unit of time for one unit of resources allocated by the subcontractor to đ??śđ?‘– project I, assumed constant over period T r (S, k m i Wi ) Work rate (units/time) for a single unit of resource R, which varies over time. đ?‘† đ?‘† Resource amount of units đ?‘‡ ,đ?‘— Transference time from apartment I to apartment j. đ?‘? Waste factor for materials that remain unused at the end of any period T đ?‘ž Brings matertials? đ?‘Š Project manager reliability đ?‘Š

During Day /Week?

Work N ot C ompleted?

M aterials A v ailable or E xpected?

S pace?

E quipment?

A bandon

Wait

Low U tility in Low C ertainty ?

A bandon

Work High U tility in Low C ertainty ?

Agent i A ccording to sub

S elect P otential A ctiv ity

A ccording to plan

S elect C ommunication M ethod

G ather Info

Legend

S elector

S equence Collision

Physiological Needs

Threshold for Rest/Food > Ô? ?

Is Other Agent on Sight?

Is Reliability OK?

Collide and Exchange Info

Rest/ Eat!

Employment

Reduce W orking Rate for T=t

Fig. 6,7: Visualizing the agents and the BIM in Unity 3D.

Random/Ratio

Esteem S elect N ext A ctiv ity in P lan

Is Manager on Sight?

Enhance W orking Rate

C ompute U is,C is

Threshold for Work O K? High U i, High C i

S elect Best A ctiv ity

G o to Workplace Location

G o to Workplace Lcation

Fig 1,2: The BT describing the individual’s behavior

C ondition

A ction

[1] Brodetskaia, I., Sacks, R., & Shapira, A. (2012). Stabilizing production ow of interior and ďŹ nishing works with reentrant ow in building construction. Journal of Construction Engineering and Management, 139(6), 665-674. [2] LEAPCON (2005), Technion LEAPCON management simulation game http://www.technion.ac.il/~cvsacks/tech-leap.htm , November 21, 2005. [3] Sacks, R., Esquenazi, A., & Goldin, M. (2007). "LEAPCON: Simulation of lean construction of high-rise apartment buildings. Journal of Construction Engineering and management", 133(7), 529-539. [4] Sawhney, A., Bashford, H., Walsh, K., and Mulky, A. (2003). “Agent based modeling and simulation in construction.â€? Proc., 35th Winter Conf. on Simulation, ACM, New York, Vol. 2, 1541–1547. [5] Watkins, M., Mukherjee, A., Onder, N., & Mattila, K. (2009). "Using agent-based modeling to study construction labor productivity as an emergent property of individual and crew interactions." Journal of Construction Engineering and Management, 135(7), 657-667.

Ex-Ante Simulation and Visualization of Sustainability Policies in Infrastructure Systems: A Hybrid Methodology for Modeling Agency-User-Asset Interactions Mostafa Batouli1, and Ali Mostafavi2 1PhD

Candidate, Civil and Environmental Engineering, Florida International University, Email: sbatouli@fiu.edu 2Assistant Professor, OHL School of Construction, Florida International University

PROBLEM STATEMENT

ILLUSTRATIVE EXAMPLE

BACKGROUND The existing methodologies do not consider: 1) Dynamic behaviors and interactions between user/asset/agency 2) Inherent uncertainties and complexities in analysis

RESEARCH OBJECTIVE Create and test an integrated framework for assessment of sustainability in Infrastructure systems

• A network comprised of 12 roads (Haas 2008) (Figure 3) Road Name

A B C D E F G H I J K L

Type

Pavement Type

Length (miles)

Width (Yards)

R I I I R R I R R I I I

Flex Com Flex Flex Flex JPCP JPCP JRCP JRCP Com Flex Flex

1.55 0.50 0.68 0.19 0.43 2.73 0.62 1.06 2.80 1.37 1.68 0.62

12.03 12.47 13.67 12.47 14.22 13.78 15.53 17.94 13.01 13.56 12.90 18.15

Construction Last STRN year Activity

1987 1962 1985 1960 1997 1991 1975 2000 1973 1973 1990 1960

1987 2006 1985 2006 2007 1991 2005 2002 2001 1999 2008 2004

ESAL/Day (in base year)

3.53 14.57 4.35 7.22 4.79 11.02 17.72 13.39 13.39 14.57 5.60 7.71

224 1185 1645 1756 864 688 1142 1785 1785 1185 1479 1756

Figure 5: Simulation of performance over 40 years PSR<3

Figure 3: Characteristics of the illustrative example

RESEARCH FRAMEWORK A System-of-Systems approach for base-level abstraction and multi-level aggregation of interrelations between agency/asset/user (Figure 1).

Economic Outcome

Policy Requirements

Year 5

Year 10

Year 15

Year 20

Year30

Year35

Year40

Modeling Asset Performance: đ?‘ƒđ?‘†đ?‘… = đ?‘ƒđ?‘†đ?‘…đ?‘– − đ??´. đ??š ∗ đ?‘Ž ∗ đ?‘†đ?‘‡đ?‘… đ?‘? ∗ đ??´đ?‘”đ?‘’ đ?‘? ∗ đ??śđ??¸đ?‘†đ??´đ??żđ?‘‘ Modeling User Behavior: Traffic growth depends on road performance Modeling Agency Behavior: Condition-based M/R strategy

SIMULATION MODEL

Year25

(2) Impacts of user behavior and budget Level on performance (Figure 7 and Figure 8) 3.8

A hybrid simulation model created to capture the dynamic behaviors of the system (Figures 4a and 4b)

Average Weighted PSR of Network

Social Benefit

Budget Limitations Demand/Pressure

Environmental Condition

Environmental Impact

Service

PSR=4.5

Figure 6: Visualization of performance at the network level

Budget Saturation 4.0

3.6

3.4

Tipping Points

3.2

3.0

3.5 3.0

2.5

Budget ($) 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 110,000 120,000 130,000 140,000 150,000

2.0 0

0 0 0 0 00 00 00 00 00 00 00 00 00 00 00 00 20 40 60 80 0 2 4 6 1 1 1 1 Available Budget

Figure 7: Impact of user behaviors on performance

Figure 1: Integrated framework for assessment of sustainability

4.5

Fixed TGR Behav ior A djusted TGR

Average Weighted PSR

• Problem: Deterioration of infrastructure systems (ASCE 2013) • Challenge: Sustainable restoring and improving (NAE 2008) • Solution: Policy and decision-making based on integrated assessment of infrastructure systems

1.5 0

10

20 Year

30

40

Figure 8: Impact of availability of budget on performance

SINGNIFICANCE

METHODOLOGY Use of a hybrid Agent-Based/System Dynamics simulation model based on three interrelated computational modules (Figure 2): Behavioral Assessment (Agent-Based Modeling)

Infrastructure Performance (System Dynamics)

Performance

Figure 4a: Class diagram of Figure 4b: Sequence diagram of the simulation model the simulation model

RESULTS Environmental Assessment (Adjusted LCA)

Figure 2: Hybrid AB-SD simulation model

(1) Simulation and visualization of the performance at the network level (Figure 5 and Figure 6):

(i) Simulation and visualization of the outcomes of policies (ii) Exploring of the emergent properties affecting the sustainability of infrastructure systems (iii) Fostering adaptive policies based on varying infrastructure characteristics, agency priorities, and user behaviors.

REFERENCE • • •

American Society of Civil Engineers (ASCE) (2013). Report Card for America's Infrastructure, Reston, VA, US. National Academy of Engineering (NAE) (2008). Grand Challenges for Engineering in the 21st Century, http://www.engineeringchallenges.org. (accessed December 15, 2013) Haas, Ralph. "The ICMPA7 investment analysis and communication challenge for road assets." Prepared for the 7th Int. Conf. on Managing Pavement Assets, Calgary. 2008.

Construction Workers’ Behavior Influenced by Social Norms:

A Study of Workers’ Behavior Using Agent-Based Simulation Integrated with Empirical Methods Seungjun Ahn (esjayahn@umich.edu) and SangHyun Lee Civil and Environmental Engineering, University of Michigan

Background Normative Message

Social Control

Objectives

• “Norm as motivational capital” • Limitation of individuals-focused, formal-rule centered approaches for improving worker behavior - Ineffectiveness - Development of adverse norms

• Research Topic: Absence Behavior - 24% productivity loss when absence rate is 6-10% - Absence behavior influenced by absence culture

• To enhance our understanding of the dynamic processes of social controls of workers’ absence behavior in construction by creating a formal behavior model • To extend our understanding of the group-level absence phenomenon in construction workgroups using computer simulation • To identify effective policies and interventions to reduce absenteeism by creating positive social norms in construction projects

Results

Methodology • Agent-Based Modeling and Simulation integrated with Empirical Research

• A. Agent-Based Modeling and Simulation - Based on Social Cognitive Theory of SelfRegulation (Bandura 1991) - Model parameters o Social Adaptation (S) o Formal Rule Adaptation (F) o Strictness of Self-Regulation (R)

• C. Model Testing - Individual-Level Behavior Rule Testing (Level 0): Logistic Regression Predictors of Worker Absence Level Perceived salience of social norms Perceived explicit social control

B -1.065 1.037

S.E. .314 .384

Sig. .001 .007

- Group-Level Behavior Testing (Level 1 & 2)

• B. Data Collection & Analysis - Survey data collected from a total of 228 construction workers at 3 different job sites

[L: Low, H:High, S:Social adaptation, F:Formal rule adaptation R:Strictness]

• Conclusions - Informal, social controls in work groups influence construction worker’s absence behavior despite the transient nature of employment in construction - Fostering a positive social norm in workgroups can be a cost-effective means of maintaining a low absence level in construction projects - Workers’ social adaptation and self-regulation play an importance role in the emergence of positive social norms

UM AEC

UM Dynamic Project Management Group

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