Assessing the Impact of Construction Risks on Cost Overruns: A Risk Path Simulation–Driven Approach
Publication: Journal of Management in Engineering
Volume 38, Issue 6
Abstract
The lack of effective and comprehensive risk identification leads to ineffective risk management, which accordingly leads to failure in achieving project goals. Previous research efforts related to simulating risks in construction projects mostly have been limited to viewing risks as segregated factors, and thus providing static analysis. This paper addressed this knowledge gap through a risk path simulation–driven approach that addresses construction risks as a continuum of risk sources, events, and project characteristics. The authors (1) identified 131 construction project risks by conducting an extensive literature review, (2) designed a risk path by analyzing risk paths available in the literature, capitalizing on identified benefits, and avoiding drawbacks, (3) conducted a project-based survey to collect data from 53 construction projects related to the magnitude and impact of occurrence of the identified risks on cost overruns, (4) developed a series of two genetic algorithm (GA) models and one artificial neural network (ANN) model to quantify the impact of construction risks on cost overruns, and (5) created an ontology model to facilitate data structuring and retrieval. An ontology is a data modeling tool used to represent unstructured information into structured forms. Results show that 80% of the risks identified in this research were found to have a significant impact on cost overruns, and 89% can be recognized at project initiation. The contribution of this framework lies in its ability to represent the cost impact of a chain of conditions and events, rather than a single-point risk factor using GA and ANN modeling techniques. The proposed framework may help industry practitioners assess the impact of significant construction risks on cost overruns.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request.
Acknowledgments
The authors thank Dr. Islam H. El-adaway for his insights and comments that greatly improved the manuscript. The authors also express their gratitude to Sherin Elzeiny, Eng. Sherif Elbashbishy, and Dana Elbashbishy for their support during the course of this research.
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Information
Published In
Journal of Management in Engineering
Volume 38 • Issue 6 • November 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 14, 2021
Accepted: May 23, 2022
Published online: Jul 23, 2022
Published in print: Nov 1, 2022
Discussion open until: Dec 23, 2022
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Bibliographies
- Business management
- Computer programming
- Computing in civil engineering
- Construction costs
- Construction engineering
- Construction management
- Decision making
- Decision support systems
- Disaster risk management
- Engineering fundamentals
- Information management
- Models (by type)
- Neural networks
- Practice and Profession
- Project management
- Risk management
- Structural models
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