深基坑变形的准确预测一直是基坑工程面临的关键问题之一,地下空间的复杂性和基坑施工环境的多样性使得传统的预测方法在应对这一问题时显得力不从心。为了提高深基坑变形预测的精准度,提出一种结合卷积神经网络(CNN)、双向长短期记忆神经网络(BiLSTM)和注意力机制(Attention)的深基坑变形预测模型。通过构建时空网格,利用卷积神经网络提取基坑变形的空间特征,结合双向长短时记忆网络对时序特征进行建模,引入注意力机制提高模型对关键时空位置的关注度,最后通过全连接层整合特征,输出预测的监测值。基于广州市某人才公寓深基坑的监测数据进行工程案例验证,通过消融试验和对比试验结果表明,所提方法在深基坑变形预测中精度较高。

Accurate prediction of pit deformation has always been one of the key problems in pit engineering, and the complexity of underground space and the diversity of pit construction environments make the traditional prediction methods incompetent in dealing with this problem. In order to improve the accuracy of pit deformation prediction, a deep pit deformation prediction model combining convolutional neural network (CNN), bi-directional long and short-term memory neural network (BiLSTM), and attention mechanism (Attention) is proposed. By constructing a spatio-temporal grid, spatial features of foundation pit deformation are extracted using convolutional neural network, temporal features are modeled by combining bidirectional long and short-term memory network, the attention mechanism is introduced to improve the model's attention to the key spatio-temporal locations, and finally the features are integrated by the fully-connected layer to output the predicted monitoring values. Based on the monitoring data of a deep foundation pit of a talent apartment in Guangzhou City for engineering case validation, the results of ablation and comparison experiments show that the proposed method has higher accuracy in the prediction of deformation in deep foundation pits.