#6555. Machine learning-based constitutive models for cement-grouted coal specimens under shearing

December 2026publication date
Proposal available till 05-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Geotechnical Engineering and Engineering Geology;
Geochemistry and Petrology;
Energy Engineering and Power Technology;
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Abstract:
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.
Keywords:
Cement-grouted coal specimens; Constitutive law; Ensemble learning; Machine learning; Regression tree

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