Construction sites remain among the most hazardous work environments, with safety risks threatening workers and project performance. While safety-leading indicators are widely recognized in accident prevention, there remains a gap in integrating these indicators within decision-support frameworks that account for time, cost, and safety trade-offs. This study addresses this gap by proposing a framework that combines multiobjective optimization and safety visualization to support construction decisions. The framework evaluates safety performance through an analytical hierarchy process and structured interviews to weight key safety indicators. These scores are incorporated into a time–cost–safety trade-off analysis using the nondominated sorting genetic algorithm II to generate a Pareto front of optimal solutions. The model generated a set of Pareto-optimal solutions with project durations ranging from 196 to 232 days, total costs between 22 and 26 million Egyptian pound (EGP), and safety risk scores between 180 and 188, demonstrating the trade-offs among the three objectives. A selected solution is integrated into a building information modeling environment, where a custom plug-in visualizes safety performance through a 3D heat map. The framework is applied in a case study to demonstrate its implementation and potential to enhance decision-making. Future work may automate the linkage between safety indicators and project activities, incorporating uncertainty and sustainability into the optimization process.
Shams et al. (Wed,) studied this question.