Research on rock strength prediction model based on machine learning algorithm

• Published in: Discover applied sciences 2025-01, Vol.7 (1), p.16-26
• Main Authors: Ding, Xiang, Dong, Mengyun, Shen, Wanqing
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Applied and Technical Physics; Artificial intelligence; Chemistry/Food Science; Civil engineering; Compressive strength; Construction industry; Data mining; Datasets; Deep learning; Earth Sciences; Effectiveness; Engineering; Environment; Experimental methods; Feature selection; Geology; Geotechnical engineering; Learning algorithms; Machine learning; Materials Science; Mechanical properties; Mechanics; Optimization; Optimization techniques; Parameters; Performance evaluation; Prediction models; Predictions; Project engineering; R&D; Regression analysis; Regression models; Research & development; Rock mechanics; Rocks; Root-mean-square errors; Search algorithms; Supervised learning; The compressive strength of rocks; Triaxial tests
• ISSN: 2523-3963; 3004-9261; 2523-3971
• DOI: 10.1007/s42452-024-06387-y

The compressive strength of rocks is one of its mechanical characteristics. It has been a difficult problem to predict rock compressive strength conveniently and efficiently, and to solve the limitations of traditional rock compressive strength tests such as high cost, long time consumption, and reliability assurance. In this study, a data set containing 1774 groups of rock compressive strength test data was constructed through file retrieval, including 9 input parameters: rock type, temperature, confining pressure, dimension of specimen, shape of specimen, and experimental method. Eight supervised learning algorithms were used to learn the rock compressive strength test data, and eight rock compressive strength prediction models considering multiple factors were established to obtain a better method of predicting rock compressive strength. By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). Finally, four regression evaluation indicators, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R 2 ), were used to evaluate the predictive performance of the established regression models. The results showed that the best-trained model had a MAPE as low as 3.61%, MAE as low as 9.19 MPa, and R 2 as high as 0.995. It is noteworthy that AdaBoost was found to be the best model for predicting rock compressive strength. This study presents a significant advancement in the field by demonstrating the effectiveness of machine learning algorithms in this context, which have not been extensively applied to rock compressive strength predictions. The findings suggest that these models can offer substantial improvements over traditional methods, not only in accuracy but also in operational efficiency. This research is important for geotechnical engineering, as accurate rock strength predictions are critical for the design and stability assessments of construction projects, ultimately contributing to safer and more cost-effective engineering solutions. Article Highlights Collected 1774 datasets covering various rock types, experimental methods, and specimen sizes, forming a robust data foundation. Integrated SSA to optimize models, analyzed factors affecting rock strength, and evaluated machine learning performance. Pred