A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Complex subway station foundation pit deformation space-time prediction method
The invention discloses a complex subway station foundation pit deformation space-time prediction method, which comprises the following steps: S1, obtaining foundation pit deformation data, and preprocessing the foundation pit deformation data to obtain a training set and a test set; and S2, constructing a neural network model of GCN-GRU according to the training set and the test set, obtaining a prediction result of the neural network model, and completing deformation prediction of the complex subway station foundation pit site. The invention provides a complex subway station foundation pit deformation space-time prediction method, and solves the problems that a previous complex subway station foundation pit deformation prediction model can only excavate time information or a future development trend of deformation data of a single foundation pit deformation measuring point, and cannot fully utilize a complex dependency relationship of a plurality of foundation pit deformation measuring points in a space dimension; and the problems that the foundation pit deformation monitoring data of the foundation pit site is affected by various conditions, noise is likely to occur, and the deformation prediction precision is further limited are solved.
本发明公开了一种复杂地铁车站基坑变形时空预测方法，包括以下步骤：S1、获取基坑变形数据，并对其进行预处理，得到训练集和测试集；S2、根据训练集和测试集构建GCN‑GRU的神经网络模型，得到神经网络模型的预测结果，完成复杂地铁车站基坑现场的变形预测。本发明提供了一种复杂地铁车站基坑变形时空预测方法，解决了既往复杂地铁车站基坑变形预测模型只能挖掘单个基坑变形测点变形数据的时间信息或未来发展趋势，未能充分利用多个基坑变形测点在空间维度上的复杂依赖关系，制约了基坑变形预测的精度提升的问题，以及基坑现场的变形监测数据受多种条件影响易出现噪音问题，进一步限制变形预测的精度等问题。
Complex subway station foundation pit deformation space-time prediction method
The invention discloses a complex subway station foundation pit deformation space-time prediction method, which comprises the following steps: S1, obtaining foundation pit deformation data, and preprocessing the foundation pit deformation data to obtain a training set and a test set; and S2, constructing a neural network model of GCN-GRU according to the training set and the test set, obtaining a prediction result of the neural network model, and completing deformation prediction of the complex subway station foundation pit site. The invention provides a complex subway station foundation pit deformation space-time prediction method, and solves the problems that a previous complex subway station foundation pit deformation prediction model can only excavate time information or a future development trend of deformation data of a single foundation pit deformation measuring point, and cannot fully utilize a complex dependency relationship of a plurality of foundation pit deformation measuring points in a space dimension; and the problems that the foundation pit deformation monitoring data of the foundation pit site is affected by various conditions, noise is likely to occur, and the deformation prediction precision is further limited are solved.
本发明公开了一种复杂地铁车站基坑变形时空预测方法，包括以下步骤：S1、获取基坑变形数据，并对其进行预处理，得到训练集和测试集；S2、根据训练集和测试集构建GCN‑GRU的神经网络模型，得到神经网络模型的预测结果，完成复杂地铁车站基坑现场的变形预测。本发明提供了一种复杂地铁车站基坑变形时空预测方法，解决了既往复杂地铁车站基坑变形预测模型只能挖掘单个基坑变形测点变形数据的时间信息或未来发展趋势，未能充分利用多个基坑变形测点在空间维度上的复杂依赖关系，制约了基坑变形预测的精度提升的问题，以及基坑现场的变形监测数据受多种条件影响易出现噪音问题，进一步限制变形预测的精度等问题。
Complex subway station foundation pit deformation space-time prediction method
一种复杂地铁车站基坑变形时空预测方法
ZHU LIMING (author) / YANG ZIJIANG (author) / SHAO JIE (author) / ZHANG FEIFEI (author) / MA HONGYUE (author) / JIANG JIAOLONG (author) / WANG HAIBING (author) / CUI BIN (author) / LI NING (author) / PAN DESHENG (author)
2023-06-13
Patent
Electronic Resource
Chinese
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