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Predicting triaxial compressive strength of high-temperature treated rock using machine learning techniques
The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering. Five machine learning (ML) techniques were adopted in this study, i.e. back propagation neural network (BPNN), AdaBoost-based classification and regression tree (AdaBoost-CART), support vector machine (SVM), K-nearest neighbor (KNN), and radial basis function neural network (RBFNN). A total of 351 data points with seven input parameters (i.e. diameter and height of specimen, density, temperature, confining pressure, crack damage stress and elastic modulus) and one output parameter (triaxial compressive strength) were utilized. The root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) were used to evaluate the prediction performance of the five ML models. The results demonstrated that the BPNN shows a better prediction performance than the other models with RMSE, MAE and R values on the testing dataset of 15.4 MPa, 11.03 MPa and 0.9921, respectively. The results indicated that the ML techniques are effective for accurately predicting the triaxial compressive strength of rocks after different high-temperature treatments.
Predicting triaxial compressive strength of high-temperature treated rock using machine learning techniques
The accurate prediction of the strength of rocks after high-temperature treatment is important for the safety maintenance of rock in deep underground engineering. Five machine learning (ML) techniques were adopted in this study, i.e. back propagation neural network (BPNN), AdaBoost-based classification and regression tree (AdaBoost-CART), support vector machine (SVM), K-nearest neighbor (KNN), and radial basis function neural network (RBFNN). A total of 351 data points with seven input parameters (i.e. diameter and height of specimen, density, temperature, confining pressure, crack damage stress and elastic modulus) and one output parameter (triaxial compressive strength) were utilized. The root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) were used to evaluate the prediction performance of the five ML models. The results demonstrated that the BPNN shows a better prediction performance than the other models with RMSE, MAE and R values on the testing dataset of 15.4 MPa, 11.03 MPa and 0.9921, respectively. The results indicated that the ML techniques are effective for accurately predicting the triaxial compressive strength of rocks after different high-temperature treatments.
Predicting triaxial compressive strength of high-temperature treated rock using machine learning techniques
Xunjian Hu (author) / Junjie Shentu (author) / Ni Xie (author) / Yujie Huang (author) / Gang Lei (author) / Haibo Hu (author) / Panpan Guo (author) / Xiaonan Gong (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
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