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Artificial Intelligence in Construction Delay Analysis and Management: A Review
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Putri Zulaiha Razi1
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Ramadhansyah Putra Jaya1
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Ummu Najwa Nur Yusrizal1
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Authors Info & Affiliations
Abstract
Introduction
Construction delays remain a challenge in projects, often resulting in cost overruns, disputes, productivity losses, and schedule inefficiencies. Growing project complexity has increased the need for data-driven approaches to delay analysis and management. This study reviews the application of artificial intelligence in construction delay analysis and management by examining application areas, techniques, project contexts, implementation maturity, and research gaps.
Methods
A structured literature review was conducted using a PRISMA-guided search and screening process. Scopus and Web of Science were searched for studies published between 2021 and 2025. Following database filtering, title and abstract screening, and full-text eligibility assessment, 42 studies were retained for qualitative synthesis.
Results
The reviewed studies applied artificial intelligence to delay prediction, delay-cause identification, risk prioritisation, progress monitoring, schedule recovery, and optimisation using machine learning, deep learning, ensemble learning, and hybrid modelling approaches. Several studies reported predictive accuracy above 90%, particularly for artificial neural networks, ensemble techniques, and hybrid optimisation models. However, evidence remains constrained by limited datasets, project-specific applications, weak real-time data integration, and insufficient consideration of contractual and external factors.
Discussion
The reviewed studies indicate that Artificial Intelligence (AI) models have improved predictive capability in construction delay management. However, practical implementation remains limited by data availability, model interpretability, and weak integration with project decision-making processes.
Conclusion
Artificial intelligence can improve construction delay management through transparent and data-driven approaches. Further research is needed to develop integrated frameworks linking predictive analytics with practical delay-management decisions in dynamic construction environments.
