This study combines three optimization algorithms, Tunicate Swarm Algorithm (TSA), Whale Optimization Algorithm (WOA), and Jellyfish Search Optimizer (JSO), with random forest (RF) to predict the shear bond strength of cable bolts under different types and grouting conditions. Based on the original dataset, a database of 860 samples was generated by introducing random noise around each data point. After establishing three hybrid models (RF-WOA, RF-JSO, RF-TSA) and training them, the obtained models were evaluated using six metrics: coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), variance account for (VAF), and A-20 index. The results indicate that the RF-JSO model exhibits superior performance compared to the other models. The RF-JSO model achieved an excellent performance on the testing set (R2 = 0. 981, RMSE = 11. 063, MAE = 6. 457, MAPE = 9, VAF = 98. 168, A-20 = 0. 891). In addition, Shapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Local Interpretable Model-agnostic Explanations (LIME) were used to analyze the interpretability of the model, and it was found that confining pressure (Stress), elastic modulus (E), and a standard cable type (cable typeₛtandard) contributed the most to the prediction of shear bond strength. In summary, the hybrid model proposed in this study can effectively predict the shear bond strength of cable bolts.
Xu et al. (Sun,) studied this question.