Based on the reality of frequent collapse accidents in subway construction in China，a method of collapse risk prediction based on numerical simulation，artificial neural networks and MonteCarlo method is put forward. The risk of collapse accident is predicted under the condition of pipeline leakage and damage. On the basis of investigation of accident statistics，the main reason that resulted in collapse accidents is leakage of pipelines. Through the method of finite difference fluid-solid coupling numerical simulation，the maximum ground settlement values of the three different pipeline locations，namely，just above the tunnel，above the tunnel 5m on the right and above the tunnel 10m on the right，are calculated. When pipelines are not damaged and leaking，the maximum ground settlement is located at the surface just above the tunnel and the values are 0.012 85，0.016 05 and 0.018 53m，respectively. When pipelines are damaged and leaking，the maximum ground settlement located at the surface just above the tunnel and above the tunnel about 2m on the right. The maximum ground settlement values are 0.028 75，0.027 17 and 0.021 8m，respectively. The numerical simulation results are used as the training and test samples of neural network，and the non-linear mapping relationship between the basic parameters and the ground settlement is established by RBF neural network，which is used to replace the performance function of Monte-Carlo method. According to Monte-Carlo method，the probability of collapse risk of the three locations under the condition of damaged and leaking pipelines is calculated when the tunnel is excavated. The probabilities are 36.75%，25.08% and close to 0. This research can provide reference for similar risk control of subway tunnel construction.
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WANG Yan，LIU Baoguo，QI Yi. Prediction of the collapse accident probability of urban subway tunnel construction under the condition of damaged and leaking pipelines. , 2018, 37(S1): 3432-3440.
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2018, Vol. 37 
