## 5 Conclusions  
This paper studied the ability of different XGBoost techniques to predict the performance of fully grouted rock bolting systems. The following key findings and recommendations are presented:  
* Seventy-two fully grouted rock bolting samples were prepared, to investigate the factors influencing fully grouted rock bolt performance. Three (W/G) ratios (30%, 35%, and 40%), three grout admixtures (standard grout and two variations with 1% and 2% fly ash), two DC values (23 mm and 50 mm), and five CTs (1, 7, 14, 21, and 28 days) were considered. The results reveal that MPL values are increased by increasing CT and DC and decreasing (W/G) ratios.  
Figure 18: The importance of each effective parameter.  
* To assess the impact of grout mechanical behaviour on PL values, 72 UCS samples were simultaneously cast and tested alongside the PL samples. Rock bolts with larger diameters exhibited greater strength and MPL values.
* Five various XGBoost methods, including XGBoost, XGBoost-HHO, XGBoost-DA, XGBoost-JSO, and XGBoost-FA, were developed to evaluate the XGBoost's ability in predicting the performance of fully grouted rock bolts. After evaluating the developed models, it was determined that the hybrid model of XGBoost and the JSO optimiser exhibited the best performance among all other models, with the highest values of R\({}^{2}\) and VAF, and the lowest values of MSE, RMSE, and Bf. Sensitivity analysis revealed that, in descending order, UCS, grout admixture, DC, W/G, CT, and FA were the most influential parameters on the performance of fully grouted rock bolts.
* In the future, research should prioritise increasing the dataset to encompass various types of cementitious grouts and resins and other mechanical characteristics of bolts and grouts, such as shear strength, tensile strength, rib spacing, and bolt profiles, facilitating a more thorough examination of rock bolt effectiveness. Moreover, combining real-time monitoring systems with machine learning algorithms can improve the effectiveness of these models in practical situations, making it easier to implement proactive ground control measures.  
Conceptualisation, B.J.S., A.M., H.N., K.M., W.K., N.A., S.E. and S.H.; methodology, B.J.S., A.M., H.N., S.E. and S.H.; software, B.J.S. and S.H.; validation, B.J.S., A.M., H.N., S.E. and S.H.; formal analysis, B.J.S., A.M., H.N., K.M., W.K., N.A., S.E. and S.H.; investigation, B.J.S., A.M., H.N., K.M., W.K., N.A., S.E. and S.H.; resources, B.J.S., A.M., H.N., K.M. and W.K.; data curation, B.J.S., A.M., H.N., K.M., W.K., N.A., S.E. and S.H.; writing--original draft preparation, B.J.S., A.M., H.N. and S.E.; writing--review and editing, B.J.S., A.M., H.N., S.E. and S.H.; visualization, B.J.S., A.M., H.N., S.E. and S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to acknowledge the in-kind support of Minova and, in particular, [PERSON] for this research.  
The authors declare no conflicts of interest.