@article{Zheng2025, 
author = {Lei Zheng and Hong Pan and Yuelei Ruan and Guoxin Zhang and Lei Zhang and Jianda Xin and Zhenyang Zhu and Jianyao Zhang and Wei Liu},
title = {A Prediction Method for Concrete Mixing Temperature Based on the Fusion of Physical Models and Neural Networks},
year = {2025},
journal = {Computer Modeling in Engineering & Sciences},
volume = {145},
number = {3},
pages = {3217-3241},
keywords = {Concrete outlet temperature prediction, physical model, neural network, dynamic weight fusion, temperature control},
url = {https://www.sciopen.com/article/10.32604/cmes.2025.074651},
doi = {10.32604/cmes.2025.074651},
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/m3, showing significant potential for improving resource efficiency and supporting sustainable construction practices.}
}