Machine learning-based constitutive models for cement-grouted coal specimens under shearing

Cement-based grouting has been widely used in mining engineering; its constitutive law has not been comprehensively studied. In this study, a novel constitutive law of cement-grouted coal specimens (CGCS) was developed using hybrid machine learning (ML) algorithms. Shear tests were performed on CGCS for the analysis of stress-strain curves and the preparation of the dataset. To maintain the interpretation of the trained ML models, regression tree (RT) was used as the main technique. The effect of maximum RT depth (Max_depth) on its performance was studied, and the hyperparameters of RT were tuned using the genetic algorithm (GA). The RT performance was also compared with ensemble learning techniques. The optimum correlation coefficient on the training set was determined as 0.835, 0.946, 0.981, and 0.985 for RT models with Max_depth = 3, 5, 7, and 9, respectively. The overall correlation coefficient was over 0.9 when the Max_depth >= 5, indicating that the constitutive law of CGCS can be well described. However, the failure type of CGCS could not be captured using the trained RT models. Random forest was found to be the optimum algorithm for the constitutive modeling of CGCS, while RT with the Max_depth = 3 performed the worst. (C) 2021 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

Automated summary

A novel constitutive law of cement-grouted coal specimens (CGCS) was developed using hybrid machine learning algorithms in this study, with shear tests showing that the constitutive law of CGCS can be well described. Random forest was found to be the optimum algorithm for the constitutive modeling of CGCS, with the best performance among all methods tested.