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

IF 3.5 2区 工程技术 Q3 ENERGY & FUELS Geothermics Pub Date : 2024-08-27 DOI:10.1016/j.geothermics.2024.103144
Muhammad Naqeeb Nawaz
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Abstract

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.

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预测岩石热机械响应的灰箱解决方案
评估高温处理下岩石的热机械响应对各种工程地质项目至关重要。目前对岩石热机械响应的预测主要依赖于将温度作为降低系数的简单数学模型,而现有的机器学习算法往往因其黑箱解决方案而面临实际挑战。本研究提出了非常实用的灰箱解决方案,利用基因表达编程(GEP)预测高温处理后的岩石强度。数据集包括温度、岩石类型、岩石密度、样本大小、裂缝破坏应力、约束压力和弹性模量,作为输入参数,并以三轴压缩试验的岩石强度作为输出。根据不同的输入参数集提出了三种灰箱解决方案(数学公式),在训练和测试阶段均表现出极佳的准确性,具有较高的 R2-值(R2 > 0.95)和较低的误差值。特征重要性分析表明,裂缝破坏应力、约束压力和弹性模量是影响高温下岩石强度的重要统计参数。对所提出模型的外部验证表明,这些模型具有很强的泛化能力,强调了它们在训练数据之外的良好表现。此外,单调性研究表明,所提出的模型符合预期的物理过程。所提出的公式具有宝贵的现场意义,有效地解决了实验室评估岩石热机械响应过程中劳动密集型和成本高昂的局限性。
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来源期刊
Geothermics
Geothermics 工程技术-地球科学综合
CiteScore
7.70
自引率
15.40%
发文量
237
审稿时长
4.5 months
期刊介绍: Geothermics is an international journal devoted to the research and development of geothermal energy. The International Board of Editors of Geothermics, which comprises specialists in the various aspects of geothermal resources, exploration and development, guarantees the balanced, comprehensive view of scientific and technological developments in this promising energy field. It promulgates the state of the art and science of geothermal energy, its exploration and exploitation through a regular exchange of information from all parts of the world. The journal publishes articles dealing with the theory, exploration techniques and all aspects of the utilization of geothermal resources. Geothermics serves as the scientific house, or exchange medium, through which the growing community of geothermal specialists can provide and receive information.
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