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
张文松 1贾磊 1姚荣涵 2孙立3
作者信息
- 1. 河北地质大学城市地质与工程学院,河北石家庄 050031;河北地质大学河北省地下人工环境智慧开发与管控技术创新中心,河北石家庄 050031;河北地质大学京津冀城市群地下空间智能探测与装备重点实验室,河北石家庄 050031
- 2. 山东理工大学交通与车辆学院,山东淄博 255049
- 3. 大连理工大学建设工程学部,辽宁大连 116024
- 折叠
摘要
为提升地铁基坑开挖引发的地表沉降的预测精度,基于自注意力机制和深度学习提出一种能捕捉沉降数据时空特性的深度注意力组合预测模型(self-attention convolutional gated recurrent units,Self-CGRU).Self-CGRU 模型由空间模块和时间模块搭建.空间模块中,选择卷积神经网络捕捉相邻监测点沉降数据的空间相关性;时间模块中,使用门控循环单元神经网络分析沉降数据的时间规律,并引入自注意力机制捕获沉降数据内部的自相关性,进而得到沉降预测值.选取中国深圳市地铁基坑周边地表沉降数据验证Self-CGRU模型,结果表明:相比现有模型,Self-CGRU模型预测性能更好,使预测精度提高了 17.48％～29.17％.研究成果可为地铁基坑周边地表沉降预测提供一种准确且稳定的新模型.
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引用本文复制引用
基金项目
河北省教育厅科学研究项目(BJK2024090)
国家自然科学基金(52172314)
出版年
2024
国家科技期刊平台
NSTL
万方数据