Development of a method for weight determination of disaster-causing factors and quantitative risk assessment for tailings dams based on causal coupling relationships

Tailings dam failures may cause catastrophic losses of personnel and property as well as environmental pollution. Therefore, the accurate assessment of the risks of tailings dams and the implementation of effective control measures are critical to ensuring the safety and stability of tailings dams. In this study, first, based on the Decision-Making Trial and Evaluation Laboratory and interpretive structural modeling (DEMATEL-ISM), a causal coupling hierarchical structure model of the disaster-causing factors of tailings dams was established. The importance and causal relationships among the disaster-causing factors were analyzed, and the key disaster-causing factors and total degree were identified. Second, according to the hierarchical structure model, the Pythagorean fuzzy analytic hierarchy process was utilized to calculate the hierarchical weights of the factors. According to DEMATEL and complex network analysis, the centrality and clustering coefficient of each disaster-causing factor are obtained. A model for determining the weights of disaster-causing factors is proposed with centrality (alpha), hierarchical weight (beta) and clustering coefficient (theta) as the core. This model reflects not only the importance of each disaster-causing factor to the risk of tailings dam failure but also the causal coupling relationship between the factors and the structural importance of each factor in the tailings dam system. Finally, combined with the cloud model and matter-element extension model, a risk assessment model for tailings dam failure was proposed. An example of a tailings dam in China was analyzed. The results show that the case risk assessment results are consistent with the actual project assessment results. The weight determination and risk assessment method reduce the impact on the accuracy of risk assessment results due to the ambiguity, randomness and correlation of disaster-causing factors.