Moussa, A; Ezzeldin, M and El-Dakhakhni, W (2025) Data-driven assessment of complexity-induced risks in infrastructure projects. Journal of Construction Engineering and Management, 151(7): 04025074, ISSN 0733-9364
Abstract
Infrastructure projects are characterized by inherent complexities that often lead to their poor performance. Notwithstanding challenges posed by various risks and their interactions, the additional non-linear and dynamic interdependence-induced complexities make infrastructure projects susceptible to systemic risks - probable component disruption that can lead to cascade (system-level) disruptions. The study of teams/resource interdependence-induced systemic risks in an environment of interacting risks is scarce in the literature. In addition, several previous studies demonstrated that current risk interactions and systemic risk analysis models are impractical due to their complexity and limited theoretical application domains. In this respect, the current study fills this knowledge gap by developing a data-driven risk interactions and systemic risk management approach. This approach is formulated in three stages: (1) quantifying risk interactions and teams/resources interdependence; (2) building machine learning model (ML) models to predict project performance based on the quantified characteristics; and (3) devising relevant mitigation strategies. The study also includes a practical demonstration application of the approach to present a step-by-step demonstration for each stage - thus guiding practitioners to proactively safeguard against risk interactions and systemic risks. The current work contributes to the body of knowledge by laying out the foundations of investigating the compound phenomenon of risk interactions and systemic risks as well as by presenting an effective approach to achieve that endeavor. Overall, the current study introduces a reliable and practical approach to enhance the performance of infrastructure projects through interacting risks- and systemic risk-informed management strategies.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | complexity; infrastructure projects; interdependence; machine learning; mitigation; network analysis; risk interactions; systemic risks |
| Index terms: | strategy, mitigation, practitioner, risk analysis, body of knowledge, machine learning, complexity, network analysis, management strategy, risk-informed, project performance, risk management, foundations, infrastructure project, interaction, interdependence |
| Subjects: | knowledge management, practitioner, environmental hazards, behavioral psychology, structural engineering, financial risk, artificial intelligence, data analysis and analytics, risk assessment, organizational analysis, systems engineering, infrastructure and transport systems, management, project management theory and practice |
| Topics: | Cost Management, Business Strategy, Information Management, Research Practice, Roles and Professions, Digital Applications, Organizational Design, Engineering Principles, Project Management, Risk Management, Sustainability |
| Descriptive scope: | 3 PCA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here