数据不完备下基于CNN-GRU神经网络的地铁基坑变形预测方法研究
周意1王章琼1邹原耕2蔡永辉1徐晓雅1赵歧林1
Metro Foundation Pit Deformation Prediction Method Based on CNN-GRU Neural Network under Incomplete Data Condition
ZHOU Yi1WANG Zhangqiong1ZOU Yuangeng2CAI Yonghui1XU Xiaoya1ZHAO Qilin1
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作者信息:1.武汉工程大学土木工程与建筑学院， 430074， 武汉
2.中国水利水电第八工程局有限公司， 410004， 长沙
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Affiliation:1.School of Civil Engineering and Architecture, Wuhan Institute of Technology, 430074, Wuhan China
2.Sinohydro Engineering Bureau 8 Co., Ltd., 410004, Changsha, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.20230612
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中图分类号/CLCN:U231.3
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栏目/Col:土建工程
摘要:
［目的］为应对地铁基坑变形监测数据不完备导致的预测滞后和精度下降问题，提出一种基于CNN-GRU神经网络（卷积神经网络-门控循环单元神经网络）的基坑变形预测方法，并对此方法进行验证。［方法］利用基坑不完备变形监测数据和缺失监测点附近点位监测数据构建数据样本集，输入CNN模型中，完成缺失数据的填补修复，得到完整连续的基坑变形监测数据。利用小波分解提取变形监测数据中低频趋势分量和高频误差分量，利用GRU神经网络模型和ARMA（自回归滑动平均）模型分别对低频趋势分量和噪声分量进行预测，再将预测结果合并得到最终变形预测结果。结合南京某地铁车站基坑工程案例，对该方法的有效性进行了验证。［结果及结论］采用基于CNN-GRU神经网络的基坑变形预测方法对缺失率达到18.5%和10.1%的基坑变形数据修复后进行预测时，预测误差分别为1.926 6%和1.274 6%，预测精度分别提高了35%和6%，可以看出该方法的数据修复能力表现良好，数据修复可靠度较高。对比GA-BP神经网络预测方法和LSTM预测方法，该方法的预测精度提升了1倍以上，且较好解决了预测滞后的问题，预测精度能够满足实际工程需要。
Abstracts:
［Objective］ To address the issues of prediction delay and accuracy degradation caused by incomplete deformation monitoring data in metro foundation pits, a prediction method based on a CNN-GRU (convolutional neural network-gated recurrent unit) neural network is proposed and verified. ［Method］ A data sample set is constructed using incomplete foundation pit deformation monitoring data and missing monitoring data from nearby monitoring points, and then input into the CNN model to complete data imputation and obtain continuous and complete monitoring data. Wavelet decomposition is applied to extract low-frequency trend components and high-frequency error components from the deformation data. The GRU neural network model and ARMA (autoregressive moving average) model are respectively used to predict the low-frequency trend and noise components, which are then combined to yield the final deformation prediction results. The foundation pit project at a metro station in Nanjing is used as case study to verify the effectiveness of the proposed method. ［Result & Conclusion］ When the proposed CNN-GRU-based prediction method is applied to foundation pit deformation data imputation with missing rates of 18.5% and 10.1%, the resulting prediction errors are 1.926 6% and 1.274 6%, respectively, and the prediction accuracy improves by 35% and 6%, respectively. These results demonstrate strong data recovery capability and high reliability of this method. Compared to the GA-BP neural network and LSTM prediction methods, the proposed method improves prediction accuracy by more than 100% and effectively addresses the issue of prediction lag. The accuracy of this method could meet the requirements of practical engineering applications.
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