Design of a Wireless Sensor Network-Based Risk Assessment Algorithm for Cave Collapse

Abstract

Accidents frequently happen during tunnel and subway construction. This study defines the cave collapse types based on the investigation results of a cave collapse in Yanchang County, the precise scenario of the cave collapse, paired with Yan’an City. Flac3D software is used to simulate the slope before and after the excavation of the cave. The findings of comparing the stress and strain characteristics of the soil slope lead to the cave’s destruction. A comprehensive risk probability estimation method based on interval probability level of accident tree and Bayesian network, weight confidence index, and confidence interval is proposed to solve the problems of subjectivity and inaccurate results in tunnel risk assessment, and it is applied to the risk assessment of cave collapse. First, the Bayesian network was constructed by using accident tree, and the conditional probability (joint probability) of sensor nodes is obtained by using the dependence relationship between the case factors. Then, the piJ estimated value of expert J for the occurrence probability of the fundamental event XI was calculated using the interval probability grade division and weight confidence index approach. The sample space U and its statistics were constructed from the estimated values of all experts, and the probability range of the occurrence probability of the basic event Xi was obtained by introducing the confidence interval method. For risk inference, the probability range of all occurrences is derived and integrated with the conditional probability of a Bayesian network, ensuring the scientific correctness and precision of risk assessment. At the same time, the model can be used to determine the cause of an accident. According to our experimental results, cave collapse causes were investigated soon after an accident occurred, allowing for the development of risk management strategies to prevent future occurrences.