Machine learning models for predicting rock fracture toughness at different temperature conditions
The rock fracture toughness (RFT) is significantly influenced by thermal treatments. Accurate evaluation of RFT at different temperatures holds great importance in the fields of geotechnical engineering. Current analytical and empirical models, based on our current but incomplete understanding of th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Case Studies in Construction Materials |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509523008021 |
| _version_ | 1827636217961775104 |
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| author | Xunjian Hu Dong Liao Dongdong Ma Senlin Xie Ni Xie Haibo Hu Xiaonan Gong |
| author_facet | Xunjian Hu Dong Liao Dongdong Ma Senlin Xie Ni Xie Haibo Hu Xiaonan Gong |
| author_sort | Xunjian Hu |
| collection | DOAJ |
| description | The rock fracture toughness (RFT) is significantly influenced by thermal treatments. Accurate evaluation of RFT at different temperatures holds great importance in the fields of geotechnical engineering. Current analytical and empirical models, based on our current but incomplete understanding of the fracture mechanics theory, are unable to produce a priori predictions of RFT. As a result, researchers have to rely on experiments, which are often costly and time-consuming, to understand external environment, internal factors and RFT links in rocks. This research explores the potential of employing machine learning (ML) models as an effective approach to address such challenges. Six ML models are presented, including support vector machine (SVM), random forest (RF), back propagation neural network (BPNN), back propagation-particle swarm optimization (BP-PSO), convolutional neural network (CNN), and radial basis function neural network (RBF). These models are applied using a dataset of 297 samples derived from previous studies involving semi-circle bend tests. The dataset encompasses 15 input variables, including sample radius, sample thickness, notch length, support span, inclination angle of the notch, tensile strength, uniaxial compressive strength, density, quartz content, feldspar content, gypsum content, clay content, other minerals, loading rate, and temperature. The results of three statistical metrics (root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE)) confirm that the ML models are able to predict the temperature-dependent RFT in modes I, II and III with high accuracy. The results demonstrated that the SVM model shows a better performance than the other five models. In the case of testing dataset, the RMSE, MAE and R2 values for SVM model are 0.1122 MPa·m1/2, 0.0829 MPa·m1/2 and 0.9506, respectively. Additionally, feature importance analysis highlights that the temperature and inclination angle are the most influential variables affecting the RFT. |
| first_indexed | 2024-03-09T15:39:02Z |
| format | Article |
| id | doaj.art-7605b1b6309d44729460f3d880435eb7 |
| institution | Directory Open Access Journal |
| issn | 2214-5095 |
| language | English |
| last_indexed | 2024-03-09T15:39:02Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj.art-7605b1b6309d44729460f3d880435eb72023-11-25T04:49:32ZengElsevierCase Studies in Construction Materials2214-50952023-12-0119e02622Machine learning models for predicting rock fracture toughness at different temperature conditionsXunjian Hu0Dong Liao1Dongdong Ma2Senlin Xie3Ni Xie4Haibo Hu5Xiaonan Gong6Research Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, ChinaDepartment of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon 100872, China; Corresponding author.State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116 China; School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116 ChinaResearch Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, ChinaFaculty of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, ChinaResearch Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, ChinaResearch Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, ChinaThe rock fracture toughness (RFT) is significantly influenced by thermal treatments. Accurate evaluation of RFT at different temperatures holds great importance in the fields of geotechnical engineering. Current analytical and empirical models, based on our current but incomplete understanding of the fracture mechanics theory, are unable to produce a priori predictions of RFT. As a result, researchers have to rely on experiments, which are often costly and time-consuming, to understand external environment, internal factors and RFT links in rocks. This research explores the potential of employing machine learning (ML) models as an effective approach to address such challenges. Six ML models are presented, including support vector machine (SVM), random forest (RF), back propagation neural network (BPNN), back propagation-particle swarm optimization (BP-PSO), convolutional neural network (CNN), and radial basis function neural network (RBF). These models are applied using a dataset of 297 samples derived from previous studies involving semi-circle bend tests. The dataset encompasses 15 input variables, including sample radius, sample thickness, notch length, support span, inclination angle of the notch, tensile strength, uniaxial compressive strength, density, quartz content, feldspar content, gypsum content, clay content, other minerals, loading rate, and temperature. The results of three statistical metrics (root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE)) confirm that the ML models are able to predict the temperature-dependent RFT in modes I, II and III with high accuracy. The results demonstrated that the SVM model shows a better performance than the other five models. In the case of testing dataset, the RMSE, MAE and R2 values for SVM model are 0.1122 MPa·m1/2, 0.0829 MPa·m1/2 and 0.9506, respectively. Additionally, feature importance analysis highlights that the temperature and inclination angle are the most influential variables affecting the RFT.http://www.sciencedirect.com/science/article/pii/S2214509523008021Machine learningFracture toughnessRock mechanicsTemperatureSemi-circle bend test |
| spellingShingle | Xunjian Hu Dong Liao Dongdong Ma Senlin Xie Ni Xie Haibo Hu Xiaonan Gong Machine learning models for predicting rock fracture toughness at different temperature conditions Case Studies in Construction Materials Machine learning Fracture toughness Rock mechanics Temperature Semi-circle bend test |
| title | Machine learning models for predicting rock fracture toughness at different temperature conditions |
| title_full | Machine learning models for predicting rock fracture toughness at different temperature conditions |
| title_fullStr | Machine learning models for predicting rock fracture toughness at different temperature conditions |
| title_full_unstemmed | Machine learning models for predicting rock fracture toughness at different temperature conditions |
| title_short | Machine learning models for predicting rock fracture toughness at different temperature conditions |
| title_sort | machine learning models for predicting rock fracture toughness at different temperature conditions |
| topic | Machine learning Fracture toughness Rock mechanics Temperature Semi-circle bend test |
| url | http://www.sciencedirect.com/science/article/pii/S2214509523008021 |
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