Predicting the Temperature Regime in Hardening Massive Monolithic Walls Using CatBoost Gradient Boosting

Thermal cracking due to hydration heat in massive monolithic walls poses a significant risk, but traditional prediction methods are often too complex for rapid engineering assessments. This study aims to develop machine learning models to predict the maximum temperature and center-to-surface tempera...

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Autors principals:	Tatiana Kondratieva, Vasilina Tyurina, Anton Chepurnenko
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2026-06-01
Col·lecció:	Buildings
Matèries:	mass concrete hydration heat thermal cracking machine learning CatBoost temperature prediction
Accés en línia:	https://www.mdpi.com/2075-5309/16/12/2287
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