![]() Daftaribesheli constructed the M-SMR system using the mamdani fuzzy algorithm and the SMR system, which quantifies the fuzziness in the rock system 13. As the study progresses, the utilization of fuzzy theory has significantly improved the generalization performance and accuracy of classification methods 2. The M-RMR system as developed by Unal is also based on the RMR system and includes additional features for the characterization of weak, stratifified, anisotropic and clay bearing rock masses 12. ![]() For example, Laubscher combined RMR values and adjustment parameters for different factors under mining conditions to build MRMR systems 11. Subsequently, several other rock classification systems were built on this basis. used a Q-system for tunnel rock quality grading, which supported the choice of support method 10. have employed the product method to establish the rock tunneling index (Q) grading system 9. have utilized the sum-difference method to integrate different factors and construct an RMR rock grading system 8. With the development of systems engineering, the influence of multiple factors is considered in the assessment of rock quality. In addition, there are grading methods with juxtaposition of indicators such as Chinese engineering rock grading standard (BQ method) 7. For example, the classical single-index grading methods, such as the Protodyakonov coefficient f grading method, the tensile strength R t gradin g method, the compressive strength R c grading method 4, Deer's RQD grading method 5, and the elastic wave velocity Vp method 6. Thereafter, various other evaluation methods have also been proposed based on different engineering practices. For instance, Terzaghi’s rock-load classification scheme can be considered as the first empirical rock mass classification system 3. Numerous studies have been performed for the assessment of rock mass quality. Therefore, it is necessary to develop appropriate methods to predict and evaluate the quality of rock mass 2. The accurate assessment of rock mass quality reflects the physical and mechanical properties of the rock mass and provides reliable bases for engineering stability analysis, disaster prediction, prevention and control 1. ![]() The rock mass is a concrete manifestation of the non-linear coupling of multiple factors in complex rock systems, which is directly related to the selection of construction design parameters and overall safety. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. It shows that the sensitive factor in rock mass quality is the RQD. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. ![]() The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The three combined models are compared in accuracy, precision, recall, F 1 value and computational time. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. Over the past decades, various models have been proposed to evaluate and predict rock mass. Accurate rock mass classification is also essential to ensure operational safety. The rock mass is one of the key parameters in engineering design.
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