A Modeling of Human Reliability Analysis on Dam Failure Caused by Extreme Weather

Abstract

Human factors are introduced into the dam risk analysis method to improve the existing dam risk management theory. This study constructs the path of human factor failure in dam collapse, explores the failure pattern of each node, and obtains the performance shaping factors (PSFs) therein. The resulting model was combined with a Bayesian network, and sensitivity analysis was performed using entropy reduction. The study obtained a human factor failure pathway consisting of four components: monitoring and awareness, state diagnosis, plan formulation and operation execution. Additionally, a PSFs set contains five factors: operator, technology, organization, environment, and task. Operator factors in a BN (Bayesian network) are the most sensitive, while the deeper causes are failures in organizational and managerial factors. The results show that the model can depict the relationship between the factors, explicitly measure the failure probability quantitatively, and identify the causes of high impact for risk control. Governments should improve the significance of the human factor in the dam project, constantly strengthen the safety culture of the organization’s communications, and enhance the psychological quality and professional skills of management personnel through training. This study provides valuable guidelines for the human reliability analysis on dam failure, which has implications for the theoretical research and engineering practice of reservoir dam safety and management.