Thermal Damage Characterization and Modeling in Granite Samples Subjected to Heat Treatment by Leveraging Machine Learning and Experimental Data

Applied Sciences, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/app15116328
Data confidence 85 high
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

High temperatures significantly affect the physical and mechanical properties of rocks in deep geoengineering projects, such as geothermal development, deep mining, and the geological disposal of nuclear waste. Therefore, it is essential to explore the relationship between the thermal damage (TD) of granite and its influencing factors. This paper characterizes the TD of granite specimens subjected to high temperatures of up to 800 °C and proposes a predictive model for this thermal damage. A database, which includes publicly available experimental data of advanced microscopic observations of granite specimens exposed to high-temperature treatments and their changes in physical and mechanical properties, was compiled and analyzed. The collected data revealed a consistent trend: crack development among quartz, feldspar, and biotite minerals was observed to intensify notably between 400 °C and 600 °C, as indicated by changes in the mechanical properties. Based on these characteristics, the relationships between TD and its influential parameters were determined using regression models and several machine learning algorithms. The derived models indicated good predictability performance with a coefficient of determination (R2) varying between 0.60 and 0.97, with the boosted ensemble tree model being the best. Nevertheless, mineral contents were not found to be good predictors of TD, even if they control the evolution of the crack during the heat treatment. It was concluded that the findings of this study could serve as a valuable tool for assessing the thermal damage of rocks.