A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies: Cost overrun in construction projects
Abstract
Purpose In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty. Design/methodology/approach This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system. Findings The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty. Research limitations/implications This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis. Practical implications These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice. Originality/value This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.
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Structural equation modeling does not assume the uncertainty and stochastic behavior of events, limiting its applications for complex projects under high uncertainty.
“One of the limitations of SEM is that it does not assume the uncertainty and stochastic behavior of events [34]; therefore, it has limited applications for complex projects under high uncertainty [1].”
From Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach (2022) · cites this paper as [1]
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- Authors
- Afzal, F., Yunfei, S., Nazir, M., & Bhatti, S. M.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- University of the Punjab (PK)
- University of Engineering and Technology Lahore (PK)
- Corresponding author
- Afzal, Farman
- Venue
- International Journal of Managing Projects in Business
- Publisher
- Emerald
- Published
- 2019-09-24
- Volume
- 14
- Issue
- 2
- Pages
- 300-328
- ISSN
- 1753-8378 (ISSN-L)
- OpenAlex
- W2980412932
- Cited by
- 172
- References
- 154
- Citations by year
- 2026: 40 · 2025: 47 · 2024: 32 · 2023: 21 · 2022: 22 · 2021: 6 · 2020: 4
- FWCI
- 5.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Managing Projects in Business (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Construction Project Management and Performance
- Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
- Social Sciences › Economics, Econometrics and Finance › Finance › Capital Investment and Risk Analysis
- Keywords
- construction risk management, cost risk assessment, cost overruns, artificial intelligence, fuzzy logic, fuzzy neural network, fuzzy simulation, Bayesian networks, uncertainty, hybrid algorithm, subjective risk assessment
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