基于GA-Bi-LSTM的盾构隧道下穿既有隧道沉降预测模型

doi:10.13722/j.cnki.jrme.2021.1187

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摘要为了对盾构隧道下穿既有隧道的安全控制提供支撑，以既有地铁隧道的沉降预测误差为目标，采用遗传算法(GA)对双向长短期记忆网络(Bi-LSTM)结构中的时间序列的长度、隐藏层的单元数、隐藏层层数、LSTM的层数以及dropout进行参数优化，并在综合考虑工程地质参数、空间参数和盾构施工参数的基础上，构建GA-Bi-LSTM既有隧道沉降预测模型。以长沙轨道交通3号线平行下穿长沙轨道交通1号线区间工程为依托，基于该区间内的既有隧道沉降监测值以及对应的输入参数数据，对模型进行训练和测试。研究结果表明：提出的GA-Bi-LSTM模型对既有隧道沉降预测的平均绝对误差(MAE)、均方根(RMSE)、样本回归值(R2)分别为0.42，0.45，0.90，平均相对误差仅为10.78%，相较于BP，SVM，LSTM，Bi-LSTM神经网络模型拥有更好的预测精度，说明该模型具有较好的可靠性和实用性，可为新建隧道下穿既有隧道的沉降预测提供一种新的思路和方法。

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	周中
	张俊杰
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关键词 ：隧道工程, 盾构隧道, 神经网络, 遗传算法, 预测模型, 沉降量

Abstract：In order to provide support for the safety control of shield tunnel under-crossing the existing tunnel，genetic algorithm(GA) is used to optimize the structural parameters of Bi-directional Long Short-Term Memory(Bi-LSTM)，including the time series，units in hidden layer，hidden layers，Bi-LSTM layers，and dropout. And then，the existing tunnel settlement prediction model，named GA-Bi-LSTM，is constructed by comprehensively considering engineering geological parameters，spatial parameters，and shield construction parameters. Based on the settlement monitoring values and the corresponding construction parameters of the section project of Changsha Rail Transit Line 3 crossing Changsha Rail Transit Line 1 in parallel，the model is trained and tested. The results show that the mean absolute error(MAE)，root mean square error(RMSE) and sample regression value(R2) of the GA-Bi-LSTM model are 0.42，0.45 and 0.90 respectively，and the average relative error is only 10.78%. Compared with BP，SVM，LSTM and Bi-LSTM models，GA-Bi-LSTM model has better prediction accuracy，indicating that the model has better reliability and practicability，which can provide a new idea and method for the settlement prediction of the new tunnel under-crossing the existing tunnel.

Key words： tunnelling engineering shield tunnel neural network genetic algorithm prediction model settlement

引用本文:

周中，张俊杰，丁昊晖，李繁. 基于GA-Bi-LSTM的盾构隧道下穿既有隧道沉降预测模型[J]. 岩石力学与工程学报, 2023, 42(1): 224-234.
ZHOU Zhong，ZHANG Junjie，DING Haohui，LI Fan. Settlement prediction model of shield tunnel under-crossing existing tunnel based on GA-Bi-LSTM. , 2023, 42(1): 224-234.

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http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2021.1187 或 http://rockmech.whrsm.ac.cn/CN/Y2023/V42/I1/224

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