Tunnel lining crack is the most common disease and also the manifestation of other diseases, which widely exists in plain concrete lining structure. Proper evaluation and classification of engineering conditions directly relate to operation safety. Particle flow code (PFC) calculation software is applied in this study, and the simulation reliability is verified by using the laboratory axial compression test and 1 : 10 model experiment to calibrate the calculation parameters. Parameter analysis is carried out focusing on the load parameters, structural parameters, dimension, and direction which affect the crack diseases. Based on that, an evaluation index system represented by tunnel buried depth (H), crack position (P), crack length (L), crack width (W), crack depth (D), and crack direction (A) is put forward. The training data of the back propagation (BP) neural network which takes load-bearing safety and crack stability as the evaluation criteria are obtained. An expert system is introduced into the BP neural network for correction of prediction results, realizing classified dynamic optimization of complex engineering conditions. The results of this study can be used to judge the safety state of cracked lining structure and provide guidance to the prevention and control of crack diseases, which is significant to ensure the safety of tunnel operation.

中文翻译：

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基于PFC-BP神经网络的隧道衬砌裂缝智能分类方法

隧道衬砌裂缝是最常见的疾病，也是其他疾病的表现形式，广泛存在于普通混凝土衬砌结构中。正确评估和分类工程条件直接关系到操作安全。本研究使用粒子流代码（PFC）计算软件，并通过实验室轴向压缩试验和1:10模型实验校准计算参数来验证仿真可靠性。进行参数分析的重点是影响裂纹疾病的载荷参数，结构参数，尺寸和方向。在此基础上，建立了以隧道埋深（H），裂缝位置（P），裂缝长度（L），裂缝宽度（P）为代表的评价指标体系。提出了W），裂纹深度（D）和裂纹方向（A）。获得了以承载安全性和裂纹稳定性为评价标准的BP神经网络训练数据。将专家系统引入BP神经网络以校正预测结果，实现复杂工程条件的分类动态优化。研究结果可用于判断裂缝衬砌结构的安全状态，为裂缝疾病的防治提供指导，对确保隧道的安全运行具有重要意义。