Research on LSTM-based Model for Predicting Deformation of Tunnel Section During Construction Period

Jiwen Zhang, Kai-Qi Yuan, Jianjun Mao, Yincai Cai, Dongfeng Lei, Jinyang Deng, Ting Peng
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Abstract

In order to ensure the smooth construction of highway tunnel construction project, it is necessary to monitor and analyze the tunnel deformation. Most of the existing monitoring systems at home and abroad are for independent projects or independent equipment monitoring, the system application scope is small, and the data processing is not perfect. Based on this, this paper takes the tunnel deformation monitoring during highway tunnel construction as the research object, and adopts LSTM to predict the tunnel section deformation. The short-duration memory neural network model can learn from memory and then predict the subsequent information. After establishing the neural network model, the model parameters such as learning rate, number of hidden nodes, number of iteration steps and unit input are tested and adjusted by comparative experiment, and the best fitting effect is obtained at last. The tunnel prediction model can predict the deformation of tunnel section in real time, and has high precision. At the same time, it can leave enough reaction time for construction personnel. It can be predicted that it has good development potential in the future.
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基于 LSTM 的施工期隧道断面变形预测模型研究
为了保证公路隧道建设工程的顺利施工,对隧道变形进行监测和分析是十分必要的。国内外现有的监测系统大多是针对独立工程或独立设备的监测,系统应用范围小,数据处理不完善。基于此,本文以高速公路隧道施工过程中的隧道变形监测为研究对象,采用 LSTM 对隧道断面变形进行预测。短时记忆神经网络模型可以从记忆中学习,然后预测后续信息。建立神经网络模型后,通过对比实验对模型的学习率、隐节点数、迭代步数和单位输入等参数进行测试和调整,最终得到最佳拟合效果。该隧道预测模型可实时预测隧道断面的变形,精度较高。同时,还能为施工人员留出足够的反应时间。可以预见,它在未来具有良好的发展潜力。
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