数据驱动的水下盾构隧道衬砌应变行为预测研究

doi:10.13722/j.cnki.jrme.2021.1108

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摘要预测水下盾构隧道结构响应趋势是保证隧道长期安全运维的重要保障。以武汉长江隧道为例，通过自编码器Autoencoder和循环神经网络RNN算法分别对结构响应的空间关联性和时间依赖性学习，同时考虑作用在结构上的外荷载影响，提出一种多因素耦合的深度学习预测模型，并在结构健康监测数据上对所提模型实例化，讨论模型的合理性及预测能力。数据试验结果表明：历史行为尤其是过去15 d内的响应特征、空间关联性、温度、水压是影响结构发展趋势的主要因素，且随着预测时间尺度的增大，结构长期的力学状态受当前状态的影响越来越小；所提模型的预测性能优于传统模型，该模型对数据信息的学习率达96%，对未来两周内隧道应变响应趋势的预测精度超过93%。研究成果可用于预测不利荷载环境下水下盾构隧道的力学响应特征，为结构稳定性分析和采取防护措施提供科学指导依据。

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	谭旭燕1
	陈卫忠1
	杜博文2
	杨建平1
	邹桃2

关键词 ：隧道工程, 盾构隧道, 健康监测, 机器学习, 预测

Abstract：Predicting the mechanical response of underwater shield tunnel is essential to maintain the long-term stability. Therefore，a multi-factor coupling model driven by SHM(structural health monitoring) data is developed on the basic of deep learning algorithm，where Autoencoder and RNN(recurrent neural network) are adopted to learn the spatial and temporal dependencies respectively，and considering the effect of external load applied on tunnel structure. As a study case，the presented model is formalized on the monitoring data obtained from the SHM system installed in Wuhan Yangtze River tunnel. A series of data experiments are conducted to discuss the reasonability and predicted capability of the presented model. Experimental results indicated structural historical behaviors especially in last 15 days，spatial correlation，temperature，and water level are the main factors affecting the future mechanical behavior. The predicted capability of model dropped with the increase of prediction time scale. Compared with some typical models，the model presented in this study expressed the best performance，whose learning rate reaches 96%，and predictive accuracy exceeds 93% in next two weeks. As a promising application，the proposed model can be used to predict structural mechanical response under extreme conditions and provide scientific guidance for structural stability evaluation.

Key words： tunnelling engineering shield tunnel structural health monitoring machine learning prediction

引用本文:

谭旭燕1，陈卫忠1，杜博文2，杨建平1，邹桃2. 数据驱动的水下盾构隧道衬砌应变行为预测研究[J]. 岩石力学与工程学报, 2022, 41(S2): 3317-3326.
TAN Xuyan1，CHEN Weizhong1，DU Bowen2，YANG Jianping1，ZOU Tao2. Prediction for strain variation of underwater shield tunnel via data-driven modeling. , 2022, 41(S2): 3317-3326.

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http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2021.1108 或 http://rockmech.whrsm.ac.cn/CN/Y2022/V41/IS2/3317

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