Machine Learning-Based Prediction of Heat Transfer and Hydration-Induced Temperature Rise in Mass Concrete

Barbara Klemczak (), Dawid Bąba and Rafat Siddique
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Barbara Klemczak: Department of Structural Engineering, Silesian University of Technology, 44-100 Gliwice, Poland
Dawid Bąba: Department of Machine Learning, University of Economics in Katowice, 40-287 Katowice, Poland
Rafat Siddique: Thapar Institute of Engineering & Technology, Deemed University, Patiala 147004, Punjab, India

Energies, 2025, vol. 18, issue 17, 1-27

Abstract: The temperature rise in mass concrete structures, caused by the exothermic process of cement hydration and concurrent heat exchange with the environment, results in thermal gradients between the core and outer layers of the structure. These gradients generate tensile stresses that may exceed the early age tensile strength of concrete, leading to cracking. Therefore, reliable prediction of the temperature rise and associated thermal gradients is essential for assessing the risk of early age thermal cracking. Traditional methods for predicting temperature development rely on numerical simulations and simplified analytical approaches, which are often time-consuming and impractical for rapid engineering assessments. This paper proposes a machine learning-based (ML) approach to predict temperature rise and thermal gradients in mass concrete. The dataset was generated using the analytical CIRIA C766 method, enabling systematic selection and gradation of key factors, which is nearly impossible using measurements collected from full-scale structures and is essential for identifying an effective ML model. Three regression models, linear regression, decision tree, and XGBoost were trained and evaluated on simulated datasets that included concrete mix parameters and environmental conditions. Among these, the XGBoost model achieved the highest accuracy in predicting the maximum temperature rise and the temperature differential between the core and surface of the analysed element. The results confirm the suitability of ML models for reliable thermal response prediction. Furthermore, ML models can provide a usable alternative to conventional methods, offering both tools to thermal control strategies and insight into the influence of input factors on temperature in early age mass concrete.

Keywords: heat transfer; hydration temperature; mass concrete; prediction methods; machine learning models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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