New stability forecasting model for goaf slope based on the AHP–TOPSIS theory

The goaf slopes commonly found in China have serious stability problems. These areas experience several landslides yearly, which results in casualties, property damage, and economic loss. Therefore, investigating the accurate identification and prediction of the stability state of goaf slopes is important to prevent disasters. In this study, a new prediction model of goaf slope stability is established based on the introduced theories of the analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The factors affecting goaf slope stability are classified, and a multilevel evaluation structure is constructed with the target, criterion, and index layers. Then, the weight vectors of evaluation indicators are determined scientifically using the exponential scale method. The TOPSIS method is introduced to construct a stability judgment matrix using the indicator values of critical levels. Finally, the relative closeness values are calculated to evaluate and predict the stability level of goaf slopes comprehensively. A goaf slope example is used as the case study to demonstrate this model, and results are compared with common evaluation methods (i.e., Swedish slice, Bishop, BP neural network, Janbu, limit equilibrium, and Spencer method of strength reduction). Their calculated safety factors all exceed 1.25. The goaf slope stability level is grade II and in the stable state. The proposed model has an engineering application value in predicting goaf slope states, showing good performance, applicability, and rationality.