Research on rock strength prediction model based on machine learning algorithm
Abstract 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...
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Springer
2024-12-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-024-06387-y |
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| author | Xiang Ding Mengyun Dong Wanqing Shen |
| author_facet | Xiang Ding Mengyun Dong Wanqing Shen |
| author_sort | Xiang Ding |
| collection | DOAJ |
| description | Abstract 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 (R2), 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 R2 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. |
| format | Article |
| id | doaj-art-3f8da102862e4080b425fd39c2d0f47b |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-3f8da102862e4080b425fd39c2d0f47b2025-08-20T02:32:26ZengSpringerDiscover Applied Sciences3004-92612024-12-017112610.1007/s42452-024-06387-yResearch on rock strength prediction model based on machine learning algorithmXiang Ding0Mengyun Dong1Wanqing Shen2School of Civil EngineeringArchitecture and Environment, Hubei University of TechnologySchool of Civil EngineeringArchitecture and Environment, Hubei University of TechnologyLaboratoire de Mécanique Multiphysique Multiéchelle, CNRS FR 2016Abstract 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 (R2), 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 R2 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.https://doi.org/10.1007/s42452-024-06387-yMachine learningRock mechanicsFeature selectionThe compressive strength of rocksTriaxial tests |
| spellingShingle | Xiang Ding Mengyun Dong Wanqing Shen Research on rock strength prediction model based on machine learning algorithm Discover Applied Sciences Machine learning Rock mechanics Feature selection The compressive strength of rocks Triaxial tests |
| title | Research on rock strength prediction model based on machine learning algorithm |
| title_full | Research on rock strength prediction model based on machine learning algorithm |
| title_fullStr | Research on rock strength prediction model based on machine learning algorithm |
| title_full_unstemmed | Research on rock strength prediction model based on machine learning algorithm |
| title_short | Research on rock strength prediction model based on machine learning algorithm |
| title_sort | research on rock strength prediction model based on machine learning algorithm |
| topic | Machine learning Rock mechanics Feature selection The compressive strength of rocks Triaxial tests |
| url | https://doi.org/10.1007/s42452-024-06387-y |
| work_keys_str_mv | AT xiangding researchonrockstrengthpredictionmodelbasedonmachinelearningalgorithm AT mengyundong researchonrockstrengthpredictionmodelbasedonmachinelearningalgorithm AT wanqingshen researchonrockstrengthpredictionmodelbasedonmachinelearningalgorithm |