@Article{cmes.2025.074651,
AUTHOR = {Lei Zheng, Hong Pan, Yuelei Ruan, Guoxin Zhang, Lei Zhang, Jianda Xin, Zhenyang Zhu, Jianyao Zhang, Wei Liu},
TITLE = {A Prediction Method for Concrete Mixing Temperature Based on the Fusion of Physical Models and Neural Networks},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {145},
YEAR = {2025},
NUMBER = {3},
PAGES = {3217--3241},
URL = {http://www.techscience.com/CMES/v145n3/65011},
ISSN = {1526-1506},
ABSTRACT = {As a critical material in construction engineering, concrete requires accurate prediction of its outlet temperature to ensure structural quality and enhance construction efficiency. This study proposes a novel hybrid prediction method that integrates a heat conduction physical model with a multilayer perceptron (MLP) neural network, dynamically fused via a weighted strategy to achieve high-precision temperature estimation. Experimental results on an independent test set demonstrated the superior performance of the fused model, with a root mean square error (RMSE) of 1.59°C and a mean absolute error (MAE) of 1.23°C, representing a 25.3% RMSE reduction compared to conventional physical models. Ambient temperature and coarse aggregate temperature were identified as the most influential variables. Furthermore, the model-based temperature control strategy reduced costs by 0.81 CNY/m<sup>3</sup>, showing significant potential for improving resource efficiency and supporting sustainable construction practices.},
DOI = {10.32604/cmes.2025.074651}
}