人民长江 ›› 2023, Vol. 54 ›› Issue (1): 140-144.doi: 10.16232/j.cnki.1001-4179.2023.01.019

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基于人工神经网络的隧道断层带突涌水风险评估

袁青;于锦;熊齐欢;张子平;陈世豪;   

  • 发布日期:2023-02-28

Risk assessment of water gushing in fault zones in tunnels based on artificial neural network

YUAN Qing,YU Jin,XIONG Qihuan,ZHANG Ziping,CHEN Shihao   

  • Published:2023-02-28

摘要: 为准确评估山岭隧道施工穿越岩溶断层带时突涌水风险,以人工神经网络为预测工具,开展了隧道断层带不良地质体的突涌水风险评估研究。在广泛参阅各类规范及文献的基础上,考虑影响岩断层带涌水灾害发生的13个主要风险因子构建了风险评估指标体系。应用工程实例数据训练、测试神经网络,采用反向传播算法计算并传递误差,在数次训练中优化网络参数,实现了隧道断层带涌水风险等级评估。泛化测试结果表明:建立的人工神经网络模型能够快速、精准地预测断层带涌水风险等级。提出的隧道断层带涌水风险评估方法克服了传统风险评估方法主观性和不确定性强、计算冗繁等问题,可为隧道施工突涌水风险评估提供参考。

关键词: 隧道断层带;涌水风险;风险评估;人工神经网络;

Abstract: In order to accurately assess the risk of water gushing when a tunnel crossing through faults and avoid the occurrence of surge disasters, water gushing risk assessment is carried out based on artificial neural network(ANN).On the basis of extensive reference to various norms and documents, a risk evaluation index system was constructed based on 13 main risk factors affecting the occurrence of surge disasters in fault zones.The ANN was trained and tested using engineering example data and the backpropagation algorithm.When the network parameters were optimized in several trainings, the risk assessment on water inrush ina tunnel fault zone was realized.The generalization test results show that the ANN can predict water gushing risk level quickly and accurately.The proposed method overcomes some problems of traditional risk assessment methods, such as subjectivity, uncertainty and complex calculation, and provides a reference for risk assessment of water gushing from fault zones in tunnels.

Key words: tunnel fault zone; water gushing risk; risk assessment; artificial neural network;