Integrating Risk Assessment and Scheduling in Highway Construction: A Systematic Review of Techniques, Challenges, and Hybrid Methodologies

This study presents a comprehensive review of risk assessment and scheduling techniques in highway construction, addressing the complex interplay between uncertainty, project planning, and decision-making. The research critically reviews key risk assessment methods, including Probability–Impact (P-I), Monte Carlo Simulation (MCS), Fuzzy Set Theory (FST), and the Analytical Hierarchy Process (AHP), alongside traditional scheduling approaches such as the Critical Path Method (CPM) and the Program Evaluation and Review Technique (PERT). The findings reveal that, although traditional methods like CPM and PERT remain widely used, they exhibit limitations in addressing the dynamic and uncertain nature of construction projects. Advanced techniques such as MCS, FST, and AHP enhance decision-making capabilities but require careful adaptation. The review further highlights the growing relevance of hybrid and integrated approaches that combine risk assessment and scheduling. Bayesian Networks (BNs) are identified as highly promising due to their capacity to integrate both qualitative and quantitative data, offering potential for greater reliability in risk-informed scheduling while supporting improvements in cost efficiency, schedule reliability, and adaptability under uncertainty. The study outlines recommendations for the future development of intelligent, risk-based scheduling frameworks suitable for industry adoption.