基于代理模型的地铁隧道结构变形预测数字孪生方法
顾亦宁1艾青1王少纯2徐磊2
Digital Twin Method for Metro Tunnel Structure Deformation Prediction Based on Proxy Model
GU YiningAI QingWANG ShaochunXU Lei
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作者信息:1.上海交通大学船舶海洋与建筑工程学院， 200240， 上海
2.上海建工一建集团有限公司， 200120， 上海
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Affiliation:School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, 200240, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.09.023
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中图分类号/CLCN:U456.3
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栏目/Col:研究报告
摘要:
目的：以地铁隧道邻近基坑开挖为典型场景，提出一种基于代理模型的地铁隧道结构变形预测数字孪生方法。方法：构建了邻近基坑开挖引发地铁隧道变形的参数化有限元模型，筛选13个关键参数作为模型输入，以隧道上5个不同位置的横向位移与竖向位移作为模型输出；通过均匀设计生成有限元算例样本，基于BP(反向传播)神经网络建立代理模型，以工程实测数据进行模型验证。结果及结论：隧道横向位移和竖向位移在训练集和验证集上的预测精度大于9138%，在测试集上的预测精度大于8000%，计算时间为毫秒级；以上海浦东地区某地块基坑工程案例进行验证，代理模型预测值与工程实测值的误差小于15 mm，表明代理模型能够准确预测邻近基坑开挖引发的地铁隧道变形；该方法准确率和实时性初步达到了隧道运维数字孪生应用的性能要求。
Abstracts:
Objective: In the typical scenario of foundation pit excavation adjacent to metro tunnel, a digital twin method for predicting metro tunnel structure deformation based on surrogate model is proposed.Method: A parameterized finite element (FE) model is constructed to simulate the metro tunnel deformation induced by adjacent foundation pit excavation. 13 key parameters are selected as inputs, lateral and vertical displacements at five different positions along the tunnel are taken as outputs. The uniform design is employed to generate FE samples, and a surrogate model is established based on BP (back propagation) neural network. The model is validated using engineering measured results.Result & Conclusion: The predicted lateral and vertical displacements exhibit a precision of over 91.38% in the training and validation sets and over 80.00% in the testing set, with the computation times in the millisecond range. Verification is performed on a foundation pit engineering case in Shanghai Pudong area, where the error between surrogate model predicted and engineering fieldmeasured values is less than 1.5 mm. This indicates that the surrogate model can accurately predict metro tunnel deformation induced by adjacent foundation pit excavation. The above method fulfils preliminary level requirements of accuracy and realtime performance for the application of digital twin in tunnel maintenance.
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