Grey-box solution for predicting thermo-mechanical response of rocks

• Published in: Geothermics Vol. 124; p. 103144
• Main Author: Nawaz, Muhammad Naqeeb
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Gene expression programming; Grey-box solutions; High temperature treatment, machine learning; Thermo-mechanical response; Grey-box solutions; Thermo-mechanical response; High temperature treatment, machine learning; Gene expression programming
• ISSN: 0375-6505
• DOI: 10.1016/j.geothermics.2024.103144

•Grey-box solutions to predict strength of temperature treated rocks are proposed.•Gene expression programming (GEP) algorithm is used for developing grey-box solutions.•The model input features include temperature dependent rock properties.•Proposed gene expression programming-based grey-box solutions are validated via multiple statistical checks. Evaluating the thermo-mechanical response of rocks under high temperature treatments is crucial for various engineering geology projects. Current predictions of rock thermo-mechanical response rely on simplistic mathematical fittings treating temperature as a reduction factor, while existing machine learning algorithms often present practical challenges due to their black-box solutions. In this study, highly practical grey-box solutions, utilizing gene expression programming (GEP) are proposed for forecasting rock strength following high-temperature treatments. The dataset, comprising temperature, rock type, rock density, sample size, crack damage stress, confining pressure, and elastic modulus, serves as input parameters, with rock strength from triaxial compression tests as the output. Three grey-box solutions (mathematical formulations) based on distinct input parameter sets are proposed, all demonstrating excellent accuracy with high R2-values (R2 > 0.95) and low error values across both the training and testing phases. Feature importance analysis highlights crack damage stress, confining pressure, and elastic modulus as statistically significant parameters influencing the strength of rocks subjected to high temperatures. External validation of the proposed models indicates strong generalization capabilities, underscoring their ability to perform well beyond the training data. Furthermore, a monotonicity study demonstrates that the proposed models align with the expected physical processes. The proposed formulations offer valuable field implications, effectively addressing the limitations of labor-intensive and costly laboratory processes for evaluating rock thermo-mechanical responses.