TY  - JOUR
AU  - Zheng, Lei
AU  - Pan, Hong
AU  - Ruan, Yuelei
AU  - Zhang, Guoxin
AU  - Zhang, Lei
AU  - Xin, Jianda
AU  - Zhu, Zhenyang
AU  - Zhang, Jianyao
AU  - Liu, Wei
PY  - 2025
TI  - A Prediction Method for Concrete Mixing Temperature Based on the Fusion of Physical Models and Neural Networks
JO  - Computer Modeling in Engineering & Sciences
SN  - 1526-1492
SP  - 3217
EP  - 3241
VL  - 145
IS  - 3
AB  - 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.
UR  - https://doi.org/10.32604/cmes.2025.074651
DO  - 10.32604/cmes.2025.074651