A Comprehensive Review of Machine Learning and Multi-Criteria Decision Analysis in Construction Delay Management
DOI:
https://doi.org/10.47392/IRJASH.2025.002Keywords:
Construction Delay Management, Machine Learning, Multi-Criteria Decision Analysis (MCDA), Predictive Analytics, Decision-Support FrameworkAbstract
Construction delays remain a critical challenge globally, significantly affecting project performance metrics such as cost, schedule adherence, quality, and safety. Traditional methods like the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT) are widely used but lack the predictive capabilities and adaptability required for dynamic project environments. Machine Learning (ML) and Multi-Criteria Decision Analysis (MCDA) have emerged as innovative tools for addressing these limitations. ML excels in predicting delay impacts by analysing historical data and uncovering hidden patterns, while MCDA provides a structured framework for prioritizing delay factors based on their influence on project performance. This paper provides a comprehensive review of the application of ML and MCDA in construction delay management, highlighting their strengths, limitations, and potential integration. The review identifies research gaps, including the need for hybrid frameworks that combine predictive insights with decision support. It proposes future directions to develop real-time tools for delay mitigation, ultimately enhancing construction project outcomes through data-driven decision-making.
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