An encoder-decoder model with embedded attention-mechanism for efficient meshfree prediction of slope failure

The particle-based meshfree methods provide an effective means for large deformation simulation of the slope failure. Despite the advances of various efficient meshfree algorithmic developments, the computational efficiency still limits the application of meshfree methods for practical problems. This study aims at accelerating the meshfree prediction of the slope failure through introducing an encoder-decoder model, which is particularly enhanced by the attention-mechanism. The encoder-decoder model is designed to capture the long sequence character of meshfree slope failure analysis. The discretization flexibility of meshfree methods offers an easy match between the meshfree particles and machine learning samples and thus the resulting surrogate model for meshfree slope failure prediction has a quite wide applicability. In the meantime, the embedding of the attention-mechanism into the encoder-decoder neural network not only enables a significant reduction of the number of meshfree model parameters, but also maintains the key features of meshfree simulation and effectively alleviates the information dilution issue. It is shown that the proposed encoder-decoder model with embedded attention mechanism gives a more favorable prediction on the meshfree slope failure simulation in comparison to the general encoder-decoder formalism.