基于机器学习的盾构施工地表沉降预测方法研究
Research on Surface Settlement Prediction Method of Shield Construction Based on Machine Learning
孙森 1葛爱迪 2康建国 1庞宇哲 1周亚东2
作者信息
- 1. 中交一公局第八工程有限公司,天津 300170
- 2. 天津城建大学土木工程学院,天津 300384
- 折叠
摘要
对长短期记忆网络(LSTM)模型进行了网络结构优化改进,依托几何参数、地质参数和掘进参数等多源数据进行了特征提取,深入分析了隧道盾构施工引发的地表沉降,并对比分析了 BP神经网络模型和LSTM模型的预测精度.分析结果表明:LSTM模型相较于BP神经网络模型具有更好的预测能力,与实际工程监测数据更加吻合;在施工过程中,可利用模型预测数据对地表沉降变形提供超前预警,通过调整盾构掘进参数来实现地表变形控制.相关研究结论可为类似盾构施工地表沉降预测提供参考.
Abstract
The network structure of the long-term and short-term memory(LSTM)model is optimized and improved.The feature is extracted by multi-source data such as geometric parameters,geological parameters and driving pa-rameters.An in-depth analysis is conducted on the surface settlement caused by tunnel shield construction.The prediction accuracy of BP neural network model and LSTM model was compared and analyzed.The analysis results show that the LSTM model has better prediction ability than the BP neural network model,and is more consistent with the actual engineering monitoring data;During the construction process,surface settlement & deformation can be pre-warned by the model prediction data.The surface deformation control can be realized by adjusting the shield tunneling parameters.The relevant research conclusions can provide reference for the prediction of surface settle-ment in similar shield construction.
关键词
盾构施工/地表沉降/机器学习/神经网络/预测方法Key words
shield construction/surface settlement/machine learning/neural network/prediction method引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据