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.