Prioritizing HSE Management Risks in Bridge Construction Projects Using Monte Carlo Modeling

Bridge construction projects, particularly those within large-scale infrastructure developments, represent some of the most complex and risk-intensive activities in the global construction industry. Their dynamic nature, driven by uncertainty, time pressure, and environmental variability, necessitates the adoption of intelligent and data driven HSE (Health, Safety, and Environment) management systems to ensure resilience and operational safety. This study introduces a smart HSE risk prioritization model based on Monte Carlo probabilistic simulation, designed to support evidence-based decision-making in high-risk bridge construction environments. The research was conducted on a freeway megaproject comprising 25 major bridges with a total span of 3137 m, where numerous HSE challenges such as adverse weather conditions, working at heights, and fatal occupational incidents were observed. Expert judgments were collected using structured questionnaires and refined through the Delphi technique to identify critical safety factors. The Monte Carlo simulation method was applied to generate 2000 probabilistic scenarios, capturing uncertainty across multiple HSE dimensions. The resulting data were analyzed using the Copeland prioritization approach, providing a weighted ranking of risk categories based on their relative impact on project safety outcomes. The findings revealed that personnel-related and materials and equipment risks exerted the highest influence on overall HSE performance, while political and legal risks ranked lowest. These results emphasize the need for continuous organizational engagement across all project phases, from planning and design to contractor management and site operations, to enhance safety maturity and system reliability. By integrating probabilistic modeling with structured safety frameworks such as HSE-MS and Integrated Management Systems (IMS), this study demonstrates how traditional safety practices can evolve into smart, adaptive systems capable of learning from data and anticipating risks in real time. The approach illustrates a paradigm shift from reactive hazard control to proactive, intelligent safety management, supporting the broader vision of smart construction ecosystems where human expertise and computational intelligence work in synergy to prevent accidents and optimize project outcomes.