Predicting axial-bearing capacity of fully grouted rock bolting systems by applying an ensemble system

In this paper, the potential of the five latest artificial intelligence (AI) predictive techniques, namely multiple linear regression (MLR), multi-layer perceptron neural network (MLPNN), Bayesian regularized neural network (BRNN), generalized feed-forward neural networks (GFFNN), extreme gradient boosting (XGBoost), and their ensemble soft computing models were evaluated to predict of the maximum peak load (PL) and displacement (DP) values resulting from pull-out tests. For this, 34 samples of the fully cementitious grouted rock bolts were prepared and cast. After conducting pull-out tests and building a dataset, twenty-four tests were randomly considered as a training dataset, and the remaining measurements were chosen to test the models’ performance. The input parameters were water-to-grout ratio (%) and curing time (day), while peak loads and displacement values were the outputs. The results revealed that the ensemble XGBoost model was superior to the other models. It was because having higher values of R² (0.989, 0.979) and VAF (99.473, 98.658) and lower values of RMSE (0.0201, 0.0435) were achieved for testing the dataset of PL and DP’ values, respectively. Besides, sensitivity analysis proved that curing time was the most influential parameter in estimating values of peak loads and displacements. Also, the results confirmed that the ensemble XGBoost method was positioned to predict the axial-bearing capacity of the fully cementitious grouted rock bolting system with extreme performance and accuracy. Eventually, the results of the ensemble XGBoost modeling technique suggested that this novel model was more economical, less time-consuming, and less complicated than laboratory activities.