首页|基于多域物理信息神经网络的复合地层隧道掘进地表沉降预测

基于多域物理信息神经网络的复合地层隧道掘进地表沉降预测

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
复合地层中盾构掘进诱发地表沉降的准确预测是隧道工程安全建设与施工决策的关键问题.基于隧道施工诱发地层变形机制构建隧道收敛变形与掘进位置的联系,并将其耦合至深度神经网络(deep neural network,简称DNN)框架,建立了预测盾构掘进诱发地层变形的物理信息神经网络(physics-informed neural network,简称PINN)模型.针对隧道上覆多个地层的地质特征,提出了多域物理信息神经网络(multi-physics-informed neural network,简称MPINN)模型,实现了在统一的框架内对不同地层的物理信息分区域表达.结果表明:MPINN模型高度还原了有限差分法的计算结果,可以准确预测复合地层中隧道开挖诱发的地表沉降;由于融入了物理机制,MPINN模型对隧道施工诱发地表沉降的问题具有普适性,可应用于不同地质和几何条件下隧道诱发地表沉降的预测;基于工程实测数据,提出的MPINN模型准确预测了监测断面的地表沉降曲线,可为复合地层下盾构掘进过程中地表沉降的预测预警提供参考.
Prediction of tunneling-induced ground surface settlement within composite strata using multi-physics-informed neural network
Accurate prediction of tunneling-induced ground surface settlement is crucial for ensuring safe construction and decision-making in tunneling projects.In this study,a physics-informed neural network(PINN)model is established for predicting shield tunneling-induced stratum deformation.This model is constructed by incorporating the relationship between tunnel convergence deformation and tunneling position into a deep neural network(DNN)framework.Considering the geological characteristics of multiple strata,a multi-physics-informed neural network(MPINN)model is proposed to represent the physical information of different strata in a unified framework.The results show that the MPINN model can highly reproduce the results by the finite difference method,and can accurately predict the tunneling-induced ground surface settlements considering the complex geological information of the composite strata.Due to the integrated physical mechanism,the MPINN model is applicable to the problem of tunnel-induced ground surface settlement,and it can be employed to predict the tunneling-induced ground surface settlement under different geological and geometric conditions.Based on the measured data,the proposed MPINN model accurately predicts the ground surface settlement curve of the monitored cross-section,thus it can provide a reference for the prediction and early warning of ground surface settlement during tunneling process.

physics-informed neural network(PINN)shield tunnelground surface settlementmachine learningdata-driven and physics-informed model

潘秋景、吴洪涛、张子龙、宋克志

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中南大学土木工程学院,湖南长沙 410075

鲁东大学土木工程学院,山东烟台 264025

物理信息神经网络(PINN) 盾构隧道 地表沉降 机器学习 数据物理驱动

国家自然科学基金国家自然科学基金国家自然科学基金湖南省科技创新计划湖南省自然科学基金青年科学基金

5197832252108388523784242021RC30152022JJ40611

2024

10.16285/j.rsm.2023.0296

岩土力学
中国科学院武汉岩土力学研究所

岩土力学

CSTPCD北大核心
影响因子：1.614
ISSN：1000-7598
年,卷(期)：2024.45(2)
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