Prediction for Nonlinear Time Series of Geotechnical Engineering Based on Wavelet-Optimized LSTM-ARMA Model

Abstract:

In order to predict the nonlinear time series of geotechnical engineering more precisely， a wavelet-optimized LSTM-ARMA model is proposed. First， the monitoring series are decomposed into a trend term and a noise term through wavelet analysis. Then， the trend term is predicted by the long short-term memory network （LSTM）， while the noise term by the autoregressive moving average model （ARMA）. Finally， the sum of the predicted values of both terms is taken as the total predicted results. The performance of the method is validated through the case analysis of an ultra-deep foundation pit which also indicates that the combined model gives a more precise and stable prediction than the LSTM network. Besides， the elastic-plastic finite element method is also used to predict the ground settlement induced by foundation pit excavation， and its results are compared with those of the artificial intelligence method， verifying the rationality of the latter. The analysis shows that the prediction error of the artificial intelligent method will increase significantly when the deformation mechanisms of the previous and the subsequent working conditions change suddenly， but it will decrease gradually with the progress of the subsequent working conditions.