基于多机器学习模型的再生混凝土抗盐冻性能预测

doi:10.11896/cldb.24100032

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Abstract There are many factors affecting the salt-freeze resistance of recycled aggregate concrete (RAC), including external environmental coupling factors and its own material properties, etc. In order to accurately predict the salt-freeze resistance of RAC and provide data support for the durability design of RAC structure, in this work 9 factors affecting the salt-freeze resistance of RAC were selected as input features based on 1 363 sets of test data. The output targets are compressive strength, relative dynamic elastic modulus and mass loss rate. Four monitoring algorithms, including BPNN, RBFNN, SVM and RF, and two optimization monitoring algorithms, including PSO-BPNN and GA-BPNN, were adopted, and hyperparameters were selected according to the model characteristics to optimize the monitoring algorithm, so as to realize the ranking of factors affecting the salt-freeze resistance of RAC and the accurate prediction of performance. The results show that the crushing index of regenerated aggregate, the replacement rate of regenerated aggregate, the water-cement ratio and sand rate are the key characteristic parameters affecting the salt-freeze resistance of RAC. Among the six monitoring algorithms, the PSO-BPNN prediction model has the best comprehensive prediction performance for the three output targets after hyperparameter optimization, and has reasonable generalization ability and robustness. The RMSE, MAE and R² for the prediction of compressive strength are 2.490 0, 1.943 5 and 0.903 1, respectively. On this basis, two kinds of RAC mix schemes were designed according to the key characteristic parameters determined by RF model, and PSO-BPNN model was used to predict the 30-year salt-freeze resistance life of RAC, which provided a reference for the durability design of RAC in practical engineering.

Key words： monitoring algorithm recycled aggregate concrete characteristics of recycled aggregate salt-freeze resistance

Published: 10 October 2025 Online: 2025-09-24

CLC number:

TU528.01

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	Articles by authors
	KANG Tianbei
	LIANG Yu
	LIANG Yibo
	WANG Fengchi
	ZHOU Jinghai

Cite this article:

KANG Tianbei,LIANG Yu,LIANG Yibo, et al. Prediction of Salt-Freeze Resistance of Recycled Aggregate Concrete Based on Multiple Machine Learning Models[J]. Materials Reports, 2025, 39(19): 24100032-11.

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https://www.mater-rep.com/EN/10.11896/cldb.24100032 OR https://www.mater-rep.com/EN/Y2025/V39/I19/24100032

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