Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

Abstract

The rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential 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. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F1 value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F1 value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.

A Review on Machine Learning approaches for Himalayan Rock Mass Classification

“…In rock mass classification, classification models are used to determine rock lithology and structure [18]. While regression models can be used to understand the factors used to determine rock mass quality [13,17,20,22,23]. In this case, the data source's training sample data has already been given the correct categorization [24].…”

“…Notably, the majority of the studies have focused on devising new and improved methods for rock mass classification, with an emphasis on enhancing the existing RMR system over other classification systems. Table 1 presents the pertinent information and summary of the research papers in this field [13,16,17,19,20,22,23]. Additionally, some of the researchers have developed models based on the BQ method [16] and Qsystem [31].…”

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

“…No:of correctly clustered points Total number of points (13) In a scientific report of the ROCK algorithm [27], the researchers present the results of a sensitivity analysis conducted to determine the impact of input factors on rock mass classification. A new research study provides [28] a new point cloud segmentation technique employed in the boulder detecting application.…”

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“…In their studies for malaria risk prediction, Tai and Dhaliwal [36] applied GA to optimize the hyperparameter value of 3 machine learning algorithms (LightGBM, Ridge Regression and SVR). Hu et al [37] compared PSO with other SI models, Grey Wolf Optimizer (GWO) [38], and GA to optimize the SVM rock mass classifier model. The result showed that GWO optimized SVM performed the best.…”