A Multi-Dimensional Construction Safety Risk Optimization Model

Occupational accidents in the construction sector are a significant concern for government agencies and enterprises globally. A detailed assessment of the potential consequences of accidents is essential for supervisory teams. This study presents a novel multidimensional safety assessment optimization model that assesses the cost–benefit relationship of safety measures, considering their impact on workers, company reputation, project cost, project duration, society, and the environment. First, safety risks and measures for primary work items in a typical building construction were determined. The experts subsequently assessed these risks based on precautions. Then, an optimization model was designed using a genetic algorithm and implemented for the risk assessment of a building construction project to identify the optimal measures for reducing risk scores and precautionary costs. Despite the total risk score achieved using the developed approach increased by 17.86% compared to the traditional risk assessment technique, the precautionary measures cost was reduced by 43.60%. Comparing the proposed model with the traditional risk assessment approach, it is observed that the model provides near-optimal risk scores and precautionary costs. The study offers significant implications for both practice and theory by examining risks from multiple perspectives and providing flexibility to users.