岩土力学2024，Vol.45Issue(8)：2474-2482,2491,10.DOI:10.16285/j.rsm.2023.1426
基于Self-CGRU模型的地铁基坑周边地表沉降预测
Prediction of surface settlement around subway foundation pit based on Self-CGRU model
摘要
Abstract
To improve the prediction accuracy of surface settlement around subway foundation pit,a deep attention hybrid prediction model,termed self-Attention convolutional gated recurrent units(Self-CGRU),is proposed based on the self-attention mechanism and deep learning.The Self-CGRU model can capture the spatio-temporal characteristics of settlement data.The Self-CGRU model is constructed by integrating a spatial module and a temporal module.In the spatial module,the convolutional neural network is selected to capture the spatial correlations of settlement data obtained from the adjacent monitoring points.In the temporal module,the gated recurrent units neural network is used to analyze the temporal rules of settlement data.In addition,the self-attention mechanism is introduced into the Self-CGRU model to capture the autocorrelation in settlement data.Then,the predicted values of settlement can be obtained.Surface settlement data around the subway foundation pit in Shenzhen,China are selected to verify the performance of Self-CGRU model.The results indicate that the Self-CGRU model outperforms existing models,achieving a prediction accuracy improvement ranging from 17.48％to 29.17％compared to these models.The research results can provide an accurate and stable new model for the prediction of surface settlement around subway foundation pit.关键词
沉降预测/组合模型/时空特性/深度学习/自注意力机制Key words
settlement prediction/hybrid model/spatio-temporal characteristics/deep learning/self-attention mechanism分类
土木建筑引用本文复制引用
张文松,贾磊,姚荣涵,孙立..基于Self-CGRU模型的地铁基坑周边地表沉降预测[J].岩土力学,2024,45(8):2474-2482,2491,10.基金项目
河北省教育厅科学研究项目(No.BJK2024090) （No.BJK2024090）
国家自然科学基金(No.52172314).This work was supported by the Science Research Project of Hebei Education Department(BJK2024090)and the National Natural Science Foundation of China(52172314). （No.52172314）
