基于SR-SSA-SVR的铁尾矿砂混凝土抗压强度预测

doi:10.11896/cldb.24120042

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Abstract In order to accurately and reliably predict the compressive strength of iron tailing sand concrete, the compressive strength prediction model of iron tailing sand concrete with SR-SSA-SVR was proposed, which uses the method of stepwise regression (SR) to screen the characteristic variables, and uses the sparrow search algorithm (SSA) to optimise the support vector regression (SVR). A single index was used to evaluate the model performance before and after screening variables;and the accuracy of the proposed model was verified by comparing the prediction results of SVR, back-propagation neural network (BPNN), and SSA-BPNN. The entropy weight-TOPSIS method was introduced to propose a comprehensive index for evaluating the performance of the multi-model under different input variables. The results show that stepwise regression can eliminate high correlation coefficient variables, reduce the complexity of the SSA-SVR model, and improve the generalisation ability of the model. Compared with other models, the SR-SSA-SVR model has lower error and higher accuracy. The natural fine aggregate has the least effect on the compressive strength of concrete with iron tailing sand. The proposed model provides a new idea for the optimal design of the mix ratio of iron tailing sand concrete.

Key words： iron tailing sands concrete compressive strength prediction stepwise regression support vector regression entropy weight-TOPSIS

Published: 25 December 2025 Online: 2025-12-17

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TU528

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	Articles by authors
	FAN Minghui
	LI Wei
	JIAO Yuhao
	REN Wenyuan
	LUO Tao
	ZHAO Dingxiaoye

Cite this article:

FAN Minghui,LI Wei,JIAO Yuhao, et al. Compressive Strength Prediction of Iron Tailing Sand Concrete Based on SR-SSA-SVR[J]. Materials Reports, 2025, 39(24): 24120042-8.

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https://www.mater-rep.com/EN/10.11896/cldb.24120042 OR https://www.mater-rep.com/EN/Y2025/V39/I24/24120042

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