Ground Settlement Prediction by Vacuum Preloading Based on LSTM

In order to explore a more accurate method for predicting settlement in vacuum preloading foundation treatment, a vacuum preloading settlement prediction model based on long short-term memory (LSTM) neural network was developed, taking the second-phase land reclamation project in the East Park of Xiamen New Airport planning area as an example. Measured settlement data from two regions were selected as the dataset, and the results were compared with traditional settlement prediction methods including the Asaoka method, three-point method, and hyperbolic method. The results show that the prediction model based on the LSTM neural network considering only sedimentation time series outerperforms the traditional methods that rely only on sedimentation time series. When the vacuum film is damaged and settlement rebound occurs under vacuum precompression foundation treatment, the root mean squared error (eRMSE) and the mean absolute error (eMAE) of LSTM model decrease by more than 45% compared to the traditional methods. Additionly, this model accurately captures the settlement rebound trend, providing more reliable prediction. In terms of prediction error, the eRMSE and eMAE values of the LSTM model which considers vacuum degree and sedimentation are lower than those of the LSTM model which only considers sedimentation time series by over 60%. This paper offers an advanced data-driven prediction method for prediction in vacuum preloading foundation settlement.