TY  - EJOU
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 

TI  - A Prediction Method for Concrete Mixing Temperature Based on the Fusion of Physical Models and Neural Networks
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 145
IS  - 3
SN  - 1526-1506

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/m<sup>3</sup>, showing significant potential for improving resource efficiency and supporting sustainable construction practices.
KW  - Concrete outlet temperature prediction; physical model; neural network; dynamic weight fusion; temperature control

DO  - 10.32604/cmes.2025.074651