ANN deformation prediction model for deep foundation pit with considering the influence of rainfall

Deep foundation pits involving complex soil-water-structure interactions are often at a high risk of failure under heavy rainfall. Predicted deformation is an important index for early risk warning. In the study, an ANN model is proposed based on the Wave Transform (WT), Copula method, Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM). The total deformation was firstly decomposed into low and high frequency components with WT. The CNN and LSTM were then used for prediction of the two components with rolling training and prediction. The input variables of the CNN and LSTM were determined and optimized based on the correlations analysis of Copula method of the two components with different random variables, especially with the rainfall. And finally, the predicted total deformation was obtained by adding the two prediction components. A deep foundation pit in Chengdu, China was taken as a case study, of which the horizontal deformation curves at different measuring points shows three types of developed trend, as unstable, less stable, and stable types. The predictions of the deformations of different development types by the proposed ANN model show high accuracies with a few input variables and can accurately prompt risk warning in advance.