地铁车站深基坑开挖变形智能多步预测方法

Multi-Step Prediction of Excavation Deformation of Subway Station Based on Intelligent Algorithm

为更好预测深基坑开挖期间地下连续墙的侧向位移变形,基于长短期记忆神经网络(LSTM)智能算法理论构建了LSTM多步预测模型.首先对多步预测模型的多输出策略进行论述,其次详细介绍了LSTM多步预测模型的构建方法,并对模型输入集空间维度和时间维度两项超参数进行探究,以提高模型的预测准确度.最后依托某富水砂土深基坑工程实例,分析了模型预测值与实际监测值的差异.3个典型监测点的数据分析结果表明LSTM多步预测模型具有较强的泛化能力,相关算法对深基坑开挖变形预测方法的改进优化具有参考价值.

To better predict the lateral displacements of diaphragm walls during deep excavation,a long short-term memory(LSTM)multi-step prediction model is developed in this paper based on the LSTM algorithm.First,the multi-output strategy of multi-step prediction model is discussed.Then,the construction method of the LSTM multi-step prediction model is introduced in detail,and the two hyperparameters,i.e.,the space and time dimensions of the model input set,are explored to improve the prediction accuracy of the model.Finally,the errors between the predicted values and the field monitoring data are analyzed based on an excavation project buried in water-rich sandy strata.The analysis results of three typical monitoring points indicate that the LSTM prediction model is characterized by solid generalization ability,and the relevant algorithm is practically helpful for improving and optimizing deformation prediction methods of deep excavation.