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As one of the most concerned risk sources of mining enterprises, the safety and stability of the tailings storage facility (TSF) is closely connected to the operating activity, surrounding ecological environment, and residents' lives safety and properties safety. In order to evaluate the risk of the tailings dam break, the extension matter-element model has been applied in the work. First, the risk evaluation indicator system was established, included 3 levels, 4 criteria and 17 individual indicators. Next, the weight of each indicator was computed by using the analytic hierarchy process (AHP) method. Then, the classical field, controlled field and closeness function within the extension matter-element model were determined considering the TSF height and volume, and the principle of the maximum membership degree has been improved by the closeness. Finally, a case study was illustrated to demonstrate the effectiveness of the model. The result indicated that the target TSF operated at a relatively lower risk level, which accurately reflected the real operating situation. This work can provide a reasonable reference for dam break risk evaluation.

Fault localization is one of time-consuming and labor-intensive activity in the debugging process. Consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with high accuracy and minimal human intervention. Despite the research of neural network and decision tree has made some progress in software multiple fault localization, there is still a lack of systematic research on various algorithms of machine learning. Therefore, a novel machine-learning-based multiple faults localization is proposed in this paper. First, several concepts and connotation of software multiple fault localization are introduced, move on to the status and development trends of the research. Next, the principles of machine learning classification algorithm are explained. Then, a software multiple fault localization research framework based on machine learning is proposed. The process is taking the Mid function as an example, compares and analyzes the performance of 22 machine learning models in software multiple fault localization. Finally, the optimal machine learning method is verified in the multiple fault localization of the Siemens suite dataset. The experimental results show that the machine learning based on Random Forest algorithm has more accuracy and significant positioning efficiency. This paper effectively solved the problem of large amount of program spectrum data and multi-coupling fault location, which is very helpful for improving the efficiency of software multiple fault debugging.