| Abstract:Tailings is a huge landslides hazard in mine. It will result in unable estimate losses once outburst. In recent years, more and more security issues arouse national attention, and safety monitoring technology has been further improved. Multiple parameters can be monitored. However, the data is fuzzy and disordered. It can’t be used to warn accurately. And there is also an association among multiple parameters, which caused difficulty to risk analysis and integration. Therefore, it is necessary to extract safety feature sets and establish the risk calculation model that select the dam accident as object.With the complexity of the parameters for risk calculation and difficulty of quantifying the status, we propose a simple and workable fuzzy risk calculation method based on the hazard path. First, we analyze the tailings hazards in order to establish a event tree model, which can tie the parameter to the multi-path. Then to analyze the parameters in classification based on different incidents. And introduce a triangular distribution to do homogenization with all monitoring parameters, considering that the parameters are excessive and complex. It can eliminate the relevance coupling between different types of data, and a security feature set can be built to reflect deviation degree of stable membership. Finally, based on the event tree and feature set, we propose a comprehensive risk calculation model, and give an example to verify the effectiveness of risk calculation. Then the membership of spatial distribution and the possible consequences of different incidents are quantified to synthesize the overall risk of dam failure.The quantized risk of tailings calculated by the model fully reflected the trend and the distributed probability of the overall state of tailings. And it is consistent to the actual situation, which indicated that the risk calculation method is good for the guidance of the protection with tailings safety. |
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