ADVANCES IN CONSTRUCTION PROJECT MANAGEMENT2015.dinamikler.org/2012/doc/2012/David_Arditi.pdf · ADVANCES IN CONSTRUCTION PROJECT MANAGEMENT David Arditi, ... (3D), development of

ADVANCES IN CONSTRUCTION PROJECT

MANAGEMENT

David Arditi, PhD

Professor and Director

Construction Engineering and Management Program

Illinois Institute of Technology

Chicago, IL 60616, USA

Dynamics Congress – Istanbul, Turkey – April 2012

Private accredited university

Located close to downtown Chicago

Approximately 8,000 students

Four campuses

Engineering, architecture, design, sciences, business, and law

Construction Engineering and Management Program Located within the CAEE Department

Established in 1981

Well recognized nationally and internationally

Illinois Institute of Technology


Challenges in Construction Project Management Research

Construction Project Management Research at IIT Dynamic scheduling

Dispute prevention

Risk of contractor default

Bidding practices

Women in construction management

Contents


Advanced Critical Path diagrams and computational analysis tools to find the longest path through a network Parametric linear programming algorithms

Ford & Fulkerson’s algorithm

Min


Historical Perspective - 1955-1975

)XCk(Z ijijijj,i

ijj,i

CONSTRAINTS

Xij ≤ Dij All activities

Xij ≥ dij All activities

λX ij1path

λX ijipath

All paths

λX ijnpath


Simulation and optimization of construction processes Queuing theory

Simulation models



1s

0n

sn0

1!s

s

!n

s

1P


Modeling and optimizing bidding processes

Computerized Critical Path calculations

Improving construction safety

Studies about worker motivation




Assessing risk allocation in construction contracts

Investigations in innovation, management science,

organizational theory, and strategic planning

Attempts to combine computerized design tools,

database design and management, and artificial

intelligence applications

Development of intelligent tools to visualize construction

scheduling and control

Development of expert system prototypes

Use of genetic algorithms and artificial neural networks

in solving construction management problems

Challenges in CEM Research



Development of web-based systems to enhance cost,

time, quality, safety, and organizational performance

Use of sensors and sensor networks (e.g., RFID) to

achieve automation and optimization in construction

processes

Visual modeling of building systems (3D), development

of 4D/5D systems to manage schedule and cost

Challenges in CEM Research



CM Research at IIT

Dynamic scheduling

Dispute prevention


Bidding practices

Women in construction

Repetitive scheduling

Quality management

Sustainable systems

International construction

Contracting


Synchronized Visualization of Construction Operations and

the Work Schedule

4D/5D CAD systems

PMS-GIS

4D/5D Video systems

PHOTO-NET

PHOTO-NET I

PHOTO-NET II

Automatic progress control


Dynamic Scheduling Systems

4D CAD Systems

Koo and Fischer

Bentley Schedule Simulator by Jacobus Technology

SmartPlant Review by Telegraph

Balfour Technologies’ FourDviz technology

Visual Project Scheduler (VPS)

Common Point 4D (CP4D) by CIFE/Stanford U

Kamat and Martinez

Retik and Shapira


4D CAD Systems

After 1987 (3D drawing + work schedule)

Involves analysis of the work at planning and design

stages, and analysis of the project in the post-

completion stage (forensic analysis)

Impossible to update work program in a synchronized

way with construction operations

Only objective: visualization, not management

Single level of detail

Impossible to monitor parameters like cost and safety

No web-based applications


Geographical Information Systems (GIS)

PEOPLE

STREETS

BUILDINGS

SOILS

UTILITIES

NUMERIC

STRING

DATE

THEMES/ GEOGRAPHIC DATA FILES DATABASE FILES


Project Monitoring System with Geographical Information

Systems - PMS-GIS

Architectural design done using AutoCAD

Project broken down into activities

Work schedule done using P3 Primavera

AutoCAD files transferred to ArcView

Activity themes created in ArcView

Database established in ArcView

Percent complete information input

3D drawings created in ArcView

3D drawings and P3 work schedule synchronized


PMS-GIS – As-planned

PMS-GIS – Exterior walls – Month 2

PMS-GIS – Exterior walls – Month 3

Advantages of PMS-GIS

Decision-making in the construction phase (not only

visualization purposes)

Observing progress in individual activities as well as in

smaller work packages

Updating the work schedule easily as long as the

scheduler is familiar with ArcView

Seamless operation of ArcView, P3 and AutoCAD

Monitoring of cost, safety, materials, labor resources,

equipment, etc. through a database

Web-based system possible


4D Video System – PHOTO-NET

Project broken down into activities

Work schedule done using P3 Primavera

Events on construction site recorded by camera system

using time-lapse photography

Time-lapse movie and P3 work schedule synchronized

Percent complete information input

Synchronized time-lapse output of movie and work

schedule



PMS-GIS

PHOTO-NET Automatic progress control

PHOTO-NET I –Video Operations in the Field


Video Operations

NTSC Standards: 30 frames/second

PMS-GIS

Problems of filming with NTSC standards

Playing time = Filming time

Electronic storage

In the time-lapse system, filming speed is lowered

but playing speed remains 30 frames/second

After experimentation, the optimum filming speed

was determined to be 1 frame/second


Viaduct Project - Beginning

Viaduct Project - End

PHOTO-NET II AND III – Second and Third Generations

Use more than one camera

Use advanced digital cameras

Transfer data through wireless Internet connection

Allows remote control of the cameras

Calculate optimum filming rate (frame/second)

automatically based on hard disk capacity and

project duration

Produce printed reports (bar charts, cost curves,

etc.)


Figure 2 – Dynamic Schedule Screen

PHOTO-NET II

Advantages of PHOTO-NET II

The project manager is able to monitor construction

progress relative to as-planned schedule on a day-

by-day basis

Higher management is able to monitor project

progress through the cameras

Probable disagreements between owner and

contractor are prevented


http://www.remontech.com/what-we-do/dynamic-scheduling/

http://remontech.com/tutorial1.swf











Automatic

Progress Control


3D/4D model

Point cloud data

Registration

Object definition

Progress

measurement


PMS-GIS

PHOTO-NET Automatic progress control

Automatic Progress Control – Lab Experiment


Camera shots, point cloud model, 3D model, and progress


3D model

Progress

Progress on Day 3


Components Interpretation

Column A 100% completed

Column B Partially completed, 40%

Column C 0% completed

Column D 100% completed

Progress measured by using the system

corresponds to observed actual progress

Conclusion – Dynamic Scheduling Systems

Bar charts and CPM schedules do not convey the

spatial aspects of the planning information

4D-CAD systems developed so far have limitations

PCS-GIS and PHOTO-NET compare favorably to

existing systems

Automatic progress control improves productivity

Being able to visualize project activities and the

work schedule in a synchronized way improves

project controls and minimizes disagreements


Arditi, D. (2008). “Innovative 4D Management of Construction Projects.” Proceedings

of the 5th International Conference on Innovation in Architecture, Engineering and

Construction, Antalya, Turkey.

Poku, S. and Arditi, D. (2006). “Construction Scheduling and Progress Control Using

Geographical Information Systems.” Journal of Computing in Civil Engineering, ASCE,

20(5), 351-360.

Abeid, J., Allouche, E., Arditi, D. and Hayman, M. (2003). “PHOTO-NET II: A Computer

Based Monitoring System Applied to Project Management.” Automation in Construction,

12(5), 603-616.

Abeid, J. and Arditi, D. (2003). “PHOTO-NET: An Integrated System for Controlling

Construction Progress.” Engineering, Construction and Architectural Management,

10(3), 162-171.

Abeid Neto, J. and Arditi, D. (2002). “Time-Lapse Digital Photography Applied to

Project Management.” Journal of Construction Engineering and Management, ASCE,

128(6), 530-535.

Abeid, J. and Arditi, D. (2002). “Linking Time-Lapse Digital Photography and Dynamic

Scheduling of Construction Operations.” Journal of Computing in Civil Engineering,

ASCE, 16(4), 269-279.

Abeid Neto, J. and Arditi, D. (2002). “Using Colors to Detect Structural Components in

Digital Pictures.” Computer-Aided Civil and Infrastructure Engineering, 17(1), 61-67.

Arditi, D. and Robinson M.A. (1995). “Concurrent Delays in Construction Litigation.”

Cost Engineering, AACE, 37(7), 20-31.

References

CM Research at IIT

Dynamic scheduling

Dispute prevention


Bidding practices



Quality management

Sustainable systems

Minority firms in construction

Contracting


Observations in the Construction Industry

The construction business is risky

Few construction contracts are completed

without disagreements

Disputes are resolved at different forums

A dispute-free environment is most desirable

Prevention of Disputes


Observations

Risky business Disagreements between parties

Diverse dispute resolution methods

No dispute desired

A dispute-free environment is most desirable

A prediction is a claim that a particular event will occur in

the future

A prediction is not an informed guess or an opinion

Prediction is not prophecy (astrology, numerology,

fortune telling, etc.)

Prediction


Niels Bohr stated:

"Prediction is very difficult, especially if it's about the

future."

Mathematical and computer models are frequently used

to both describe the behavior of something, and

predict its future behavior

Analysis of court data -1994

Artificial neural networks (ANN) -1998

Case based reasoning (CBR) - 1999

Boosted decision trees (BDT) - 2005

Ant colony system (Ant Miner) - 2007

Universal Prediction Model (UPM) – 2009-2010

Computerized information acquisition – 2011-2012----

IIT Research on Predicting the Outcome of Litigation


Claim

Settlement

Circuit Court

Acceptable

Appellate Court

Decision

Decision

Acceptable

Supreme Court

Decision

Acceptable

Affirmed or Reversed

Affirmed or Reversed

ENDYes

END

END

Yes

Yes

ENDYes

No

No

No

END No

Remanded

Remanded

Illinois

Court

System

Inspired by the structure of

biological systems

Artificial neurons used to develop

an artificial neural net

Nets with numerous

interconnections among different

neurons

Capable of carrying out parallel

computations for different tasks,

including prediction

Artificial Neural Networks (ANN)


Analysis - 1994

ANN -1998

CBR - 1999

BDT - 2005

Ant Miner - 2007

UPM - 2009-2010

Info acquisition - 2012

45 input elements

1 output element

102-case data set

12-case test set

Brainmaker (back propagation)

Experimentation with variations of the data, the

cases, and the parameters of the program

Artificial Neural Networks (ANN) Study


Defining feature names and

input types

Deciding training

parameters

Training

and

testing

Different

combinations of

parameters

exhausted

Solution

Unsuccessful

Successful No

Yes

Methodology of

the ANN study

Artificial Neural Networks (ANN)

Prediction rate: 67%


Problem-solving paradigm that

utilizes the specific knowledge of

previously experienced cases

Mimics human reasoning

Makes use of a cyclic process of

retrieving past experiences,

reusing, revising and retaining

them

Case Based Reasoning (CBR)


Analysis - 1994

ANN -1998

CBR - 1999

BDT - 2005

Ant Miner - 2007

UPM - 2009-2010


43 input elements

1 output element

102-case data set

12-case test set

Esteem

Experimentation with similarity assessment

methods

Case Based Reasoning (CBR) Study


Definition and Objectives

for the CBR Problem

Defining Feature Names (All

Features and Restricted

Features)

Deciding

Feature Values

Inputting the Cases

into Case Base

Deciding Similarity

Assessment Method

Retrieving Cases and

Generating Similarity

Scores

CASE-BASE

Inputting the

Target Cases

Calculation

Methods for

Selection

Adapting

Final Solution

Design Stage

Run Stage

Methodology of the CBR Study


Case Based Reasoning (CBR)



Decision trees are simple tools that can

Handle large amounts of complex information

Generate alternative decisions

Lay down the implications of these decisions

Decision Trees


Boosting

Boosting refers to a method that “boosts” a weak

learning algorithm into a strong learning algorithm

It produces an accurate prediction rule by combining

rough and moderately inaccurate rules of thumb

Analysis - 1994

ANN -1998

CBR - 1999

BDT - 2005

Ant Miner - 2007

UPM - 2009-2010


40 input elements

1 output element

102+12+18=132 cases in data set

See 5

Experimentation with data and test sets, and with

boosting trials

Boosted Decision Tree (BDT) Study


Boosted Decision Trees (BDT)



Ant colony optimization is part of Swarm Intelligence

that is based on the collective behavior of

decentralized, self-organized systems

Real ants lay down pheromones directing each other

to resources while exploring their environment

Ant Colony Systems


Analysis

ANN -1998

CBR - 1999

BDT - 2005

Ant Miner - 2007

UPM - 2009-2010


Artificial ants mimic the behavior

of real ants.

The use of ant colony

optimization as a classification

system (Ant Miner) is rather new

38 input elements

1 output element

102+12+18+19=151 cases in data set

Ant Miner

Experimentation with data and test sets

Ant Colony Systems Study


Ant Colony Systems Methodology

Classification

with Ant Miner

Data

Data

Consolidation

Attribute

Selection

Assessment


Ant Colony Systems (Ant Miner)



Involves a large number of combinations of data

consolidation methods, attribute selection

methods, base classifiers, and meta learners

Involves a large number of experiments (brute-

force method)

Totally automated process in WEKA

Universal Prediction Model (UPM)


Analysis

ANN -1998

CBR - 1999

BDT - 2005

Ant Miner - 2007

UPM - 2009-2010


Knowledge Environments

Knowledge

Environment Authors Reference

WEKA

Ian H. Witten and Eibe Frank,

University of Waikato, New

Zealand

http://www.cs.waikato.

ac.nz/ml/weka/

Rapid Miner

(Formerly

known as

Yale)

Artificial Intelligence Unit of the

University of Dortmund,

Germany

http://sourceforge.net/

projects/Yale/

http://rapidminer.com

Tanagra Ricco Rakotomalala, Universite

Lyon 2, France

http://eric.univ-

lyon2.fr/~ricco/tanagra

/en/tanagra.html


http://www.cs.waikato.ac.nz/ml/weka/











http://sourceforge.net/projects/yale/






http://rapidminer.com/





http://eric.univ-lyon2.fr/~ricco/tanagra/en/tanagra.html














Classification using

Hybrid systems

+

Hybrid Classifier System

Pre-Processing

Base

Learner

Enhancing

Algorithms Data

Data

Consolidation

Attribute

Selection

Assessment


Universal Prediction Model (UPM) Test

Pre-processing and Attribute Selection

Development of Hybrid Systems

Data

Consolidation

6 datasets

6 Unsupervised

Attribute Filters

Original

Add Noise

Normalize

Remove Useless

Replace Missing

Values

Class Order

3 Meta Learners

Boosting

Bagging

Multi Boost AB

Attribute

Selection

12 combinations

3 Subset

Evaluation

Methods

Correlation-based

Classifier Subset

Consistency

Subset

4 Search Methods

Best First

Genetic Search

Greedy Stepwise

Rank Search

Total of

72

datasets

3 Ensemble methods

Grading

Stacking C

Vote

72 * (11*3) = 2,376 experiments

72 * [4+(4*3)] = 1,152 experiments

Total of

3,528 experiments

4 Base

Classifiers

J4.8 (C4.5)

REP Tree

J Rip

Part

11 Combinations

of Base

Classifiers

J4.8 (C4.5)

REP Tree

J Rip

Part

x

x

x =





Artificial Intelligence Machine Prediction Rate

Artificial Neural Networks 67%

Case Based Reasoning 83%

Boosted Decision Trees 90%

Ant Colony Systems 92%

Universal Prediction Model 96%

Comparison of Results


Accuracy of prediction is dependent upon quality

of input data

The number of attributes should be

commensurate with the number of cases

Experimentation is essential in all AI applications

A large number of cases is required

UPM is the most versatile system that can be used

in any domain with confidence

Conclusion – Prevention of Disputes


Text searching algorithms, software, open source

systems

Lucid Solr by Lucid Imagination

Fast by Microsoft

Seamark by Endeca

Google

Dieselpoint Search

Autonomy

Computerized Information Acquisition


Analysis

ANN -1998

CBR - 1999

BDT - 2005

Ant Miner - 2007

UPM - 2009-2010


References

Arditi, D. and Pulket, T. (2010). “Predicting the Outcome of Construction Litigation Using an Integrated

Artificial Intelligence Model.” Journal of Computing in Civil Engineering, ASCE, 24(1), 73-80.

Pulket, T. and Arditi, D. (2009). “Universal Prediction Model for Construction Litigation.” Journal of Computing

in Civil Engineering, ASCE, 23(3), 178-187.

Arditi, D. and Pattanakitchamroon, T. (2009). “Analysis Methods in Time-Based Claims.” Journal of

Construction Engineering and Management, ASCE, 134(4), 242-252.

Pulket, T. and Arditi, D. (2009). “Construction Litigation Prediction System Using Ant Colony Optimization.”

Construction Management and Economics, 27(3), 241-252.

Arditi, D. and Pattanakitchamroon, T. (2006). “Selecting a Delay Analysis Method in Resolving Construction

Claims.” International Journal of Project Management, 24(2), 145-155.

Arditi, D. (2006). “Artificial Intelligence in Predicting the Outcome of Construction Litigation.” Invited keynote

lecture, Proceedings of the Tenth East Asia-Pacific Conference on Structural Engineering and Construction,

Bangkok, Thailand.

Arditi, D. and Pulket, T. (2005). “Predicting the Outcome of Construction Litigation Using Boosted Decision

Trees.” Journal of Computing in Civil Engineering, ASCE, 19(4), 387-393.

Arditi, D. (2002). “Predicting the Outcome of Construction Litigation.” Invited keynote lecture, Proceedings of

the Fifth International Conference on Advances in Civil Engineering, Istanbul, Turkey.

Arditi, D. and Tokdemir, O.B. (1999). “Using Case Based Reasoning to Predict the Outcome of Construction

Litigation.” Journal of Computer Aided Civil and Infrastructure Engineering, 14(6), 385-393.

Arditi, D. and Tokdemir, O.B. (1999). “A Comparison of Case Based Reasoning and Artificial Neural

Networks.” Journal of Computing in Civil Engineering, ASCE, 13(3), 162-169.

Arditi, D., Oksay, F.E., and Tokdemir, O.B. (1998). “Predicting the Outcome of Construction Litigation Using

Neural Networks.” Computer-Aided Civil and Infrastructure Engineering, 13(2), 75-81.

Arditi, D., Tunca, A.A., and Yuyan, R. (1995). “Analysis of Construction Claims that Lead to Litigation.”

Proceedings of the International Symposium on Modern Project Management: Unification of Professionals for

Individual Success, INTERNET – International Project Management Association and SOVNET – Russian

Project Management Association, St. Petersburg, Russia, 14-16 September.

CM Research at IIT

Dynamic scheduling

Dispute prevention


Bidding practices



Quality management

Sustainable systems


Contracting


Managing the Risk of Contractor Default

The research literature focuses on company

success factors

Can we learn from company failures?


Business failure probabilities by age

Environment/

Response Matrix

Input/Output

Model

of Business

Failure/

Survival SYMPTOMS

Feedback

PERFORMANCE FACTORS

Insufficient profits 46.3%

Heavy operating expenses 32.8%

Burdensome institutional debt 10.3%

Inadequate sales 3.9%

Business conflicts 3.6%

Receivable difficulties 2.7%

Not competitive 0.5%

100.0%

FAILURE

ORGANIZATIONAL FACTORS (39.3%)

Human, organizational and financial capital

Insufficient capital 47.2%

Lack of business knowledge 21.2%

Fraud 9.7%

Lack of managerial experience 5.4%

Family problems 5.1%

Lack of line experience 3.7%

Lack of commitment 3.4%

Poor working habits 3.4%

Overexpansion 0.8%

100.0%

ENVIRONMENTAL FACTORS (60.7%)

Macroeconomic and natural factors

Industry weakness 87.2%

Disaster 11.6%

Poor growth prospects 1.0%

High interest rates 0.2%

100.0%

DETERMINANTS


Early Detection of Decline

Time

Pe

rfo

rma

nce

Early

detection

of decline

Successful

turnaround

and desired

performance

Undesirable

decline that

can lead to

failure

Time

Pe

rfo

rma

nce


Methods of

Transferring

Risk

Transfer of Risk to Subcontractor

Bonding

Retainage

Insurance


Conclusion – Risk of Contractor Default

Company failure is age dependent

There are liabilities of newness, adolescence and

smallness

Most causes of failure can be prevented by short-term

management action

It is possible to predict the state of decline by using

non-financial data

The owner can use intelligent/economic protection

against contractor default

Subcontractor default is an important part of the risk

equation


References

Arditi, D. (2009). “The Risk of Contractor Default.” Invited keynote lecture, Proceedings of

the Fifth Conference of Construction Industry in the Twenty First Century (CITC-V),

Istanbul, Turkey.

Arditi, D. and Chotibhongs, R. (2005). “Issues in Subcontracting Practice.” Journal of


Al-Sobiei, O. S., Arditi, D. and Polat, G. (2005). “Managing Owner’s Risk of Contractor

Default.” Journal of Construction Engineering and Management, ASCE, 131(9), 973-978.

Al-Sobiei, O.S., Arditi, D. and Polat, G. (2004). “Predicting the Risk of Contractor Default in

Saudi Arabia Utilizing ANN/GA Techniques.” Construction Management and Economics,

23(4), 423-430.

Köksal, A. and Arditi, D. (2004). “Predicting Construction Company Decline.” Journal of


Köksal A. and Arditi, D. (2004). “An Input Output Model for Business Failures in the

Construction Industry.” Journal of Construction Research, 5(1), 1-16.

Arditi, D., Köksal, A. and Kale, S. (2000). “Business Failures in the Construction Industry.”

Engineering Construction and Architectural Management, 7(2), 120-132.

Kale, S. and Arditi, D. (1999). “Age-Dependent Business Failures in the U.S. Construction

Industry.” Construction Management and Economics, 17(4), 493-503.

Kale, S. and Arditi, D. (1998). “Business Failures: Liabilities of Newness, Adolescence and

Smallness.” Journal of Construction Engineering and Management, ASCE, 124(6), 458-

464.

CM Research at IIT

Dynamic scheduling

Dispute prevention


Bidding practices



Quality management

Sustainable systems


Contracting


Bidding Practices from the Point of View of the Owner

Detection and prevention of unbalanced bids

Detection and prevention of collusive bids

Detection and prevention of claims games









Formulation of regression model

Acquisition of data

all bids

108 contracts

awarded by a public agency

between 2001-2010

80 bidders

min 2, max 26, ave 5 bids per contract

t,it,i10

t,i9t8i7t6t5

i4t,i3t,i2t10t,i

)ECON(LN

)WON(LNDUR)AVERAGE(LNNOBID)RATIO(LN

)TOTAL(LNLMDISTLDIST)EST(LN)BID(LN



Analysis of the data

The residual test (residuals must not be correlated)

The cost structure stability test (coefficients of the cost

structure model must be identical)

Analysis of the bid distributions (cartel bidders are expected

to bid higher than non-cartel bidders relative to the engineer’s

estimate)

Analysis of model dispersion (cartel bids are expected to be

more tightly concentrated than non-cartel bids)

Analysis of the differences in cost structures (the

independent variables are expected to affect the models of

cartel bidders and non-cartel bidders differently)


Conclusion - Detection and prevention of collusive bids

Out of the 80 bidders it was found that 6 are likely to

have colluded with each other

Validation: Out of the 6 potential cartel bidders, 1 was

convicted of bidding irregularities, and 2 were

investigated by authorities.


References

Chotibhongs, R. and Arditi, D., Detection of Collusive Behavior, ASCE Journal of

Construction Engineering and Management, accepted for publication.

Chotibhongs, R and Arditi, D., Analysis of Collusive Bidding Behavior,

Construction Management and Economics, Vol. 30, No. 3, March 2012, pp: 221-

231.

Arditi, D. and Chotibhongs, R., Detection and Prevention of Unbalanced Bids, Construction Management and Economics, Vol. 27, No. 8, August 2009, pp: 721-732.

CM Research at IIT

Dynamic scheduling

Dispute prevention


Bidding practices



Quality management

Sustainable systems


Contracting


Women in Construction

Women are underrepresented in the construction

industry Women in national workforce: 46%

Women in construction: 9.6%

There are many reasons why construction is

male dominated


Management Development Questionnaire (MDQ)

Twenty competencies measured by eight

statements each: a total of 160 questions

Three rounds of electronic solicitation

1,400 project managers at random

(www.salesgenie.com)

63 responses received (32 from men and 31 from

women)

Methodology of the Study

Are female CMs less competent than male CMs?


http://www.salesgenie.com/

Average STEN scores for Managerial Competencies

Women Men Initiative 5.71 5.16 0.236 Risk Taking 4.61 4.74 0.845 Innovation 5.65 5.13 0.442 Flexibility and adaptability 4.23 3.32 0.159

Analytical thinking 5.32 4.94 0.530 Decision making 4.90 4.65 0.548 Planning 4.94 4.81 0.648 Quality focus 5.35 5.06 0.420

Oral communication 5.52 5.39 0.484 Sensitivity 4.87 3.52 0.019* Relationships 5.10 5.00 0.866 Teamwork 4.58 3.61 0.096

Achievement 4.61 5.19 0.094 Costumer focus 4.23 3.23 0.015* Business awareness 5.52 5.48 0.900 Learning orientation 5.61 4.94 0.117

Authority and presence 6.16 5.03 0.004* Motivating others 4.90 4.71 0.743 Developing people 4.39 3.90 0.311 Resilience 3.77 3.84 0.820

Leadership

STEN scores

Managing change

Planning & organizing

Interpersonal skills

Result orientation

Global competencies Competencies p-Value


No statistically significant difference between men and

women in 17 managerial competencies

Women outperformed men in 3 managerial competencies

Sensitivity

Customer focus

Authority and presence

Conclusion – Women in Construction


References

Kim, A. and Arditi, D., Performance of Minority Firms Providing Construction Management

Services in the U.S. Transportation Sector, Construction Management and Economics, Vol.

28, No. 8, August 2010, pp: 839-851.

Kim, A. and Arditi, D., Performance of MBE/DBE/WBE Construction Firms in Transportation

Projects, Journal of Construction Engineering and Management, ASCE, Vol. 136, No. 7,

July 2010, pp: 768-777.

Arditi, D. and Balci G., Managerial Competencies of Female and Male Construction

Managers, Journal of Construction Engineering and Management, ASCE, Vol. 135, No. 11,

November 2009, pp: 1275-1278.

Chun, B.L., Arditi D., Balci, G., Women in Construction Management, CMAA eJournal, June

2009.

Arditi, D. and Balci, G., Management Capabilities of Male and Female Managers in the

Construction Industry, Proceedings of the First Project and Construction Management

Congress, METU Cultural and Convention Center, Ankara, Turkey, September 29 –

October 1, 2010.

Arditi, D. and Balci, G., Managerial Competencies of Construction Managers. Proceedings

of the Fifth International Conference on Construction in the 21st Century (CITC-V), Istanbul,

Turkey, May 20-22, 2009.

CM Research at IIT

Dynamic scheduling

Dispute prevention


Bidding practices



Quality management

Sustainable systems


Contracting


THANK YOU FOR YOUR ATTENTION

Recent Research in Construction Management at IIT


Documents

ADVANCES IN CONSTRUCTION PROJECT MANAGEMENT2015.dinamikler.org/2012/doc/2012/David_Arditi.pdf · ADVANCES IN CONSTRUCTION PROJECT MANAGEMENT David Arditi, ... (3D), development of