A review of artificial intelligence based risk assessment methods for capturing complexity-risk interdependencies Cost overrun in construction projects

• Published in: International journal of managing projects in business Vol. 14; no. 2; pp. 300 - 328
• Main Authors: Afzal, Farman, Yunfei, Shao, Nazir, Mubasher, Bhatti, Saad Mahmood
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Group Publishing Limited 15-02-2021
• Subjects: Artificial intelligence; Content analysis; Infrastructure; Monte Carlo simulation; Project management; Research methodology; Risk assessment; Risk factors; Nepal
• ISSN: 1753-8378; 1753-8386
• DOI: 10.1108/IJMPB-02-2019-0047

PurposeIn 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/approachThis 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.FindingsThe 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/implicationsThis 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 implicationsThese 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/valueThis 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.