Transforming Construction Risk Management and Resource Optimization: A Machine Learning and Big Data Perspective - Ratan Lal; Mehdi Oldache; Sumit Keswani (Journal of Civil Engineering, 2026)
This integrative literature review examines the role of machine learning (ML) and big data analytics in transforming risk management and resource optimization in construction projects. Following a PRISMA-P-guided search across Scopus, Google Scholar, Web of Science, and PubMed, 5,756 initial records were identified and screened, resulting in a final dataset of 154 eligible studies, of which 59 high- and moderate-quality studies were retained for detailed synthesis after quality assessment. The review highlights ML’s enhanced predictive capabilities for risk assessment and resource allocation, supported by cross-industry comparisons and construction-specific case studies. Representative findings from the literature include reported prediction accuracies of up to 93.75% for delay-risk prediction and 97.6% for equipment matching, indicating the potential of MLbased tools to improve forecasting, monitoring, and resource-allocation decisions. The review also highlights the synergistic integration of ML with Building Information Modeling (BIM), the Internet of Things (IoT), and digital twin technologies, which collectively enhance project efficiency despite challenges in data-sharing standardization, interoperability, and regulatory compliance. Key barriers to ML and big data adoption are identified, along with strategic measures to address them. The study proposes two novel frameworks for the construction sector: an AI-Enhanced Construction Risk Prediction and Mitigation Framework and a Smart Build: AI-Optimized Resource Management Framework. These frameworks, informed by insights from industry practitioners, policymakers, and researchers, aim to advance digital transformation in construction by providing structured approaches for leveraging ML in risk management and resource optimization.