## 1 Introduction
[18], found that the various ratios of (W/G) can influence the minimum required EL for a secure anchorage in the fully grouted rock bolting systems. [PERSON] et al. [14] conducted a series of comprehensive PL tests to examine how various types of rock bolts, cable bolts, embedment lengths (EL), confining pressure such as constant normal loads (CNL), constant normal stiffness (CNS), grouting types, and boundary conditions impact the debonding process between the interface of B-G. [PERSON] [19] studied how different additives affected the bonding strength of fully grouted rock bolts. He conducted systematic PL tests on samples with 150 mm of EL and various CT ranges. The increase of 5% to 12% of MLAPS values revealed that adding some additives might improve the mechanical properties of the grout. [PERSON] et al. [20] examined how different specifications of rock bolting profiles, including various rib angles, thickness, and spacing impacted on axial load bearing capacity, using combinations of results from PL tests and discontinuous deformation analysis (DDA) numerical models. Although conducting a comprehensive series of pull-out tests is one of the best options to evaluate the performance of a fully grouted rock bolting system, these tests are time-consuming, costly, and labour-intensive. Artificial intelligence (AI) and machine learning (ML) offer a solution by predicting the maximum axial load bearing capacity more efficiently than laboratory or in-situ tests. Indeed, AI and ML have been successfully applied in various geotechnical, mining, and civil engineering fields [21; 22; 23; 24; 25; 26; 27]. For instance, [PERSON] and [PERSON] [28] proposed a feed-forward neural network model to predict the failure stresses and strains in cohesive soils subjected to shallow foundation loads. Their work demonstrates the accuracy and efficiency of neural networks in handling stochastic material variabilities and complex loading conditions. Nevertheless, it is noteworthy that the application of AI and ML techniques in studying fully grouted rock bolts under axial load conditions is too limited. In one of the latest pieces of research, [PERSON], [PERSON] [29] used five different AI techniques (MLR, MLPNN, BRNN, GFFNN, and XGBoost) to predict the pull-out capacity of fully grouted rock bolts, though limited by a small dataset considering only two parameters (CT and W/G ratios). In another research, [PERSON], [PERSON] [30] predicted the UCS of grouts using a weighted averaging ensemble model (WAE-JS), considering CT, W/G ratios, fly ash (FA) content, and grout types. Given the significance of this topic, this study aims to evaluate the predictive capability of the XGBoost models for assessing the axial load bearing capacity of fully grouted rock bolting systems. Regarding time-, cost-, and labour-consuming traditional approaches to predicting rock bolt performance in in-situ testing, ML models, especially ensemble techniques like XGBoost, offer a data-driven alternative to reduce time and cost while maintaining high prediction accuracy. In this paper, we presented the development and comparison of five XGBoost-based models, including hybrid models incorporating optimisation algorithms such as Harris hawk optimisation (HHO), the jellyfish search optimiser (ISO), the dragonfly algorithm (DA), and the firefly algorithm (FA) to predict the axial load bearing capacity of fully grouted rock bolts. The main contribution of this study is to demonstrate how integrating these metaheuristic optimisation techniques with XGBoost improves model performance. Furthermore, a detailed sensitivity analysis was conducted to identify the most influential parameters, such as CT, W/G, DC, grout types, UCS, and FA, on the performance of a fully grouted rock bolting system, providing valuable insights into the factors affecting the axial load transfer mechanism of fully grouted rock bolting systems.