Slope multi-step excavation displacement prediction surrogate model based on a long short-term memory neural network: for small sample data and multi-feature multi-task learning

Machine learning-based surrogate models have become the preferred approach for large-scale and frequent simulation tasks due to its significant improvement in computational efficiency. In order to overcome the potential effects of learning with small sample data and the challenges of multi-feature multi-task learning, we developed a novel deep learning long short-term memory (LSTM) model. Taking slope excavation displacement prediction as a case study, we employed the Latin hypercube sampling method to generate a synthetic dataset for training LSTM and other mainstream models. Experimental results demonstrate that the model's prediction accuracy decreases with a reduction in sample size, while support vector regression (SVR), back propagation neural network (BPNN), LSTM and Gaussian process regression (GPR) demonstrate a stronger resistance. It is feasible to utilise excavation features as model inputs to establish a unified multi-step excavation model, but the accuracy of the SVR model decreased by 32.5% after supplementing excavation features. Even when the sample size is less than 50, both LSTM and GPR exhibit excellent performance, achieving model R-squared and RMSE surpassing 0.99 and 0.07 mm. However, when addressing multi-output learning tasks, LSTM stands out as the optimal choice. This study will assist researchers or engineers in swiftly selecting appropriate surrogate models.