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Risk assessment of water inrush caused by karst cave in tunnels based on reliability and GA-BP neural network

机译：基于可靠性和GA-BP神经网络的隧道岩溶洞穴浪涌风险评估

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摘要

In order to evaluate the risk level of water inrush caused by karst cave accurately and effectively, a novel quantitative assessment model was established based on the reliability theory and genetic algorithm-back propagation (GA-BP) neural network. First, the reliability theory and the calculation formula of the minimum safe thickness were used to calculate the water inrush probability. Second, the GA-BP neural network was applied to predict the disaster consequence caused by water inrush. Six factors, including water pressure, hydraulic supply, type of gap, filling situation, degree of water enrichment and reserves of cave, were selected as the input layer of the neural network. The disaster consequence was selected as the output layer. Similar projects were screened to obtain statistical information for indices, and the Normand function in MATLAB was used to transform the information into quantitative data. Finally, the model was established by combining the probability and disaster consequence of water inrush. The 602cave in Yesanguan tunnel was taken as an engineering sample to verify the feasibility of the novel model. The obtained results showed that the proposed model is comprehensive and accurate in quantitative assessment, which has good application prospects in engineering.
机译：为了评估岩溶洞穴准确且精确地有效地引起的水涌入风险水平，基于可靠性理论和遗传算法 - 后传播（GA-BP）神经网络建立了一种新的量化评估模型。首先，使用可靠性理论和最小安全厚度的计算公式来计算水浪涌概率。其次，应用GA-BP神经网络以预测浪涌引起的灾害后果。选择了六种因素，包括水压，液压供应，差距，灌装情况，水富集程度，洞穴等级，作为神经网络的输入层。选择灾难后果作为输出层。筛选类似的项目以获得索引的统计信息，并使用MATLAB中的NORMAND函数将信息转换为定量数据。最后，通过结合浪涌的概率和灾难后果来建立该模型。 yesghan隧道中的602加勒布被视为工程样本，以验证新型模型的可行性。所得结果表明，拟议的模型在定量评估中是全面的准确，在工程中具有良好的应用前景。

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