The main objective of implementing primary ground-controlling methods, such as applying rock bolting systems, is to increase the strength of surrounding rock mass. Among all rock bolting systems, fully grouted rock bolting systems are the most popular and reliable retaining systems due to their simplicity, availability of materials, ease of installation in the field, and cost-effectiveness. While these types of rock bolts experience both axial and shear forces, understanding their response to axial loads remains complex and dependent on several factors. Extensive research has addressed the overall behaviour of the fully grouted rock bolting system, but a systematic review of the axial load transfer mechanism and its impact on overall performance is lacking. This study addresses this gap by employing a bibliometric analysis of 77 peer-reviewed publications to explore the current state of knowledge regarding the axial load transfer mechanism in fully grouted rock bolting systems. The analysis identifies influential journals, publishers, researchers, highly cited articles, and emerging keywords within this field. Furthermore, it reveals three key parameters significantly impacting the axial behaviour: (a) rock mass and boundary conditions, (b) mechanical behaviours of the grouts, and (c) the geometry and surface profile of the rock bolt. These parameters are subsequently discussed in detail, highlighting their influence on the axial performance of the system. Finally, this article concludes by suggesting promising directions for future research.

In this paper, after determining an appropriate experimental design and testing scheme to test the shear performance suitably, the impacts of pretension and shear interfaces on fibreglass rock bolts were investigated. For this purpose, at the first stage, the double shear testing apparatus was modified to address the issues and shortcomings of the established testing methodology. Then, 20-tonne and 30-tonne rock bolts were tested with pretension loads of 0 kN, 10 kN, 15 kN, and 20 kN for each test scheme. For this, a 40 MPa small aggregate concrete was used for all samples to simulate the host rock. The grout used to anchor the rock bolts was mixed to the product’s recommended guidelines to ensure close resemblance to field mixtures. To maintain consistency, all samples were manufactured with the same concrete and grout recipes. The results of double shear tests revealed that increasing the pretension also increased the confining pressures at the shear interfaces for clean joints. This, in turn, reduced the damage propagating from the bolt at the shear interface and decreased the hinge point bending. The results can be applied for further analytical and numerical studies, which could utilise for a better understanding of the shear behaviour of fibreglass rock bolts.

This article investigates the application of eXtreme gradient boosting (XGBoost) and hybrid metaheuristics optimisation techniques to predict the axial load bearing capacity of fully grouted rock bolting systems. For this purpose, a comprehensive dataset of 72 pull-out tests was built, considering various influential parameters such as three water-to-grout (W/G) ratios, five ranges of curing time (CT), three different grout admixtures with two different fly ash (FA) contents, and two different diameter confinements (DCs). Additionally, to find out the effect of the mechanical behaviour of grouts on the performance of fully grouted rock bolting systems, seventy-two uniaxial compression strength (UCS) samples were cast and tested simultaneously with pull-out samples. The UCS samples were prepared with the same details as the pull-out samples to avoid any inconsistency. The results highlight that peak load values generally increase with longer curing times, lower W/G, and higher UCS and DC values. The main novelty of this paper lies in its data-driven approach, using various XGBoost models. This method offers a time-, cost-, and labour-efficient alternative to traditional experimental methods for predicting rock bolt performance. For this purpose, after building the dataset and dividing it randomly into two training and testing datasets, five different XGBoost models were developed: a standalone XGBoost model and four hybrid models incorporating Harris hawk optimisation (HHO), the jellyfish search optimiser (JSO), the dragonfly algorithm (DA), and the firefly algorithm (FA). These models were subsequently evaluated for their ability to predict peak load values. The results demonstrate that all models effectively predicted peak load values, but the XGBoost-JSO hybrid model demonstrated superior performance, achieving the highest R-squared coefficients of 0.987 and 0.988 for the training and testing datasets, respectively. Sensitivity analysis revealed that UCS values were the most influential parameter, while FA content had the least impact on the maximum peak load values of fully cementitious grouted rock bolts.

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.