A reverse calculation method termed soil and lining physics‐informed neural network (SL‐PINN) is proposed for the estimation of load for tunnel lining in elastic soil based on radial displacement measurements of the tunnel lining. To achieve efficient and accurate calculations, the framework of SL‐PINN is specially designed to consider the respective displacement characteristics of surrounding soil and tunnel lining. A multistep training method based on the meshless characteristics of SL‐PINN is established to promote calculation efficiency. The multistep training method involves increasing the number of collocation points in each calculation step while decreasing the learning rate after scaling of SL‐PINN. The feasibility of SL‐PINN is verified by numerical simulation data and field data. Compared to other inverse calculation methods, SL‐PINN has lower precision requirements for the measurement instrument with the same level of calculation accuracy.

中文翻译：

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使用物理信息神经网络估计弹性土中隧道衬砌的荷载

提出了一种称为土壤和衬砌物理信息神经网络（SL-PINN）的逆计算方法，用于根据隧道衬砌的径向位移测量来估计弹性土壤中隧道衬砌的荷载。为了实现高效、准确的计算，SL-PINN的框架经过专门设计，考虑了周围土壤和隧道衬砌各自的位移特征。建立了基于SL-PINN无网格特性的多步训练方法以提高计算效率。多步训练方法涉及增加每个计算步骤中的搭配点数，同时降低 SL-PINN 缩放后的学习率。通过数值模拟数据和现场数据验证了SL-PINN的可行性。与其他反演计算方法相比，SL-PINN在相同计算精度水平下对测量仪器的精度要求较低。