基于LSTM-DNN（长短期记忆-深度神经网络）融合模型的土压平衡盾构土仓压力预测方法
王伯芝1,2黄永亮1,2陈文明3,4丁爽3,4刘浩2刘学增5彭子晖6吴炜枫6王嘉烨6
Earth Chamber Pressure Prediction Method for Earth Pressure Balance Shield Based on LSTM-DNN Fusion Model
WANG Bozhi1,2HUANG Yongliang1,2CHEN Wenming3,4DING Shuang3,4LIU Hao2LIU Xuezeng5PENG Zihui6WU Weifeng6WANG Jiaye6
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作者信息:1.山东大学齐鲁交通学院，250002，济南
2.济南轨道交通集团有限公司，250014，济南
3.上海同岩土木工程科技股份有限公司，200092，上海
4.上海地下基础设施安全检测与养护装备工程技术研究中心，200092，上海
5.同济大学土木工程学院，200092，上海
6.上海市隧道工程轨道交通设计研究院，200235，上海
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Affiliation:1.School of Qilu Transportation, Shandong University, 250002, Jinan, China
2.Jinan Rail Transit Group, Co., Ltd., 250014, Jinan, China
3.Shanghai Tongyan Civil Engineering Technology Co., Ltd., 200092, Shanghai, China
4.Shanghai Underground Infrastructure Safety Testing and Maintenance Equipment Engineering Technology Research Center, 200092, Shanghai, China
5.Civil Engineering College, Tongji University, 200092, Shanghai, China
6.Shanghai Tunnel Engineering & Rail Transit Design and Research Institute, 200235, 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.2024.12.007
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中图分类号/CLCN:U455.43
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栏目/Col:学术专论
摘要:
［目的］土仓压力是土压平衡盾构施工安全评估的关键参数，准确预测土仓压力有助于施工技术人员及时采取管控措施，进而保障地铁隧道的建设安全性。因此，有必要对土压平衡盾构土仓压力预测方法进行研究。［方法］提出一种多分支的LSTM（长短期记忆）-DNN（深度神经网络）融合模型。LSTM分支通过回溯历史数据提取其时序演变特征，DNN分支提取掘进状态特征，将两者组合后通过全连接层进行融合，实现对土仓压力的预测。依托济南轨道交通1号线实际盾构隧道数据对模型进行验证，并与LSTM模型、DNN模型进行了对比分析。［结果及结论］基于LSTM-DNN融合算法建立的土仓压力预测模型可以高效收敛，且所提模型在训练集和验证集上的预测效果良好。在后续的100步测试中，由LSTM-DNN融合模型得出的土仓压力预测值较好地反映了真实值的变化趋势，其平均偏差为7.65kPa，相对误差为6.09%，预测精度较高。
Abstracts:
［Objective］ Earth chamber pressure is a key parameter for EPB (earth pressure balance) shield construction assessment. Accurate prediction of earth chamber pressure helps construction technicians take timely control measures to ensure subway tunnel construction safety. Therefore, it is necessary to study the earth chamber pressure prediction method of EPB shield. ［Method］ A multi-branch LSTM (long and short term memory)-DNN (deep neural network) fusion model is proposed. LSTM branch extracts its time series evolution characteristics by backtracking historical data, while DNN branch extracts excavation state characteristics. The two branches are combined and then integrated through a fully connected layer to realize the prediction of earth chamber pressure. This multi-branch model is verified based on the actual shield tunnel data of Jinan Rail Transit Line 1, and compared with LSTM and DNN models respectively. ［Result & Conclusion］ The prediction model of earth chamber pressure based on LSTM-DNN fusion algorithm can converge efficiently, and has good prediction effects on the training set and the verification set. In the subsequent 100-step test, the predicted value of earth chamber pressure obtained by the LSTM-DNN fusion model better reflects the change trend of the actual value, with an average deviation of 7.65 kPa and a relative error of 6.09%, indicating a higher prediction accuracy.
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