Hybrid machine learning models to predict the shear strength of discontinuities with different joint wall compressive strength

The shear strength of discontinuities with different joint wall compressive strength (DDJCS) is an important mechanical property. In this paper, the prediction of shear strength of DDJCS is performed using a hybrid machine learning model of artificial neural network (ANN) model with particle swarm optimisation (PSO). To develop the proposed model, a total of 168 cases are collected. Two classical models are introduced, and four performance indexes (i.e. coefficient of determination R2, mean absolute error MAE, root mean square error RMSE, and variance account for VAF) are utilised to evaluate the comprehensive predictive performances of these models. Finally, a graphical user interface (GUI) for predicting the shear strength of DDJCS is developed. The comparison results demonstrate that compared to the remaining models, this hybrid model has a better predictive performance and generalisation capability, with R2, RMSE, VAF, and MAE values of 0.999, 0.02, 99.9%, and 0.01, respectively. GUI signifies that this novel model provides an intuitive, efficient, and visual production experience for researchers. These findings can provide valuable insights for developing accurate predictive models.