Compressive Strength Prediction of Iron Tailing Sand Concrete Based on SR-SSA-SVR
FAN Minghui1, LI Wei1, JIAO Yuhao1, REN Wenyuan1,*, LUO Tao1,2, ZHAO Dingxiaoye1
1 College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, Shaanxi, China 2 Shanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University, Xi’an 710123, China
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.
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2025, Vol. 39 
