Lei Zheng1,*, Hong Pan2,3, Yuelei Ruan2,4, Guoxin Zhang1, Lei Zhang1,*, Jianda Xin1, Zhenyang Zhu1, Jianyao Zhang2,5, Wei Liu1
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3217-3241, 2025, DOI:10.32604/cmes.2025.074651
- 23 December 2025
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 More >
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