Collapse risk assessment based on linear programming variable weight-cloud model

Collapse risk assessment is an important basis for the prevention and control of geological disasters in mountainous areas. The existing research on collapse hazard is less, and there is still no further advancement in the evaluation of collapse hazard for the traditional indicator assignment method for the diversification of the assignment results of the indicators and the comprehensive evaluation method that cannot consider the ambiguity and randomness of the indicator data at the same time. In this paper, we utilize the respective advantages of the linear programming theory and the cloud model from the prevention and control point of view, and evaluate the collapse samples. Firstly, the weight interval of evaluation index is determined by improved analytic hierarchy process, entropy weight method and coefficient of variation method. Secondly, the linear programming algorithm is used to select the specific weight of each collapse sample when the risk is the largest in the interval. Finally, a comprehensive evaluation model of cloud model is constructed to determine the risk level of collapse. In this paper, 20 collapse samples counted by predecessors in G4217 Wenchuan-Lixian section are taken as research cases. The evaluation results of 20 collapse samples are compared with other evaluation methods and field survey conditions to prove the reliability and rationality of the method. The evaluation results show that 13 of the 20 collapse samples are extremely dangerous, 2 are highly dangerous, 4 are moderately dangerous, and 1 is lowly dangerous. Among them, the extremely dangerous collapse samples account for 65% of the total number of collapses. Compared with other methods, this method is more in line with the actual situation.