Prediction of Concrete Compressive Strength Based on the BP Neural Network Optimized by Random Forest and ISSA. (27th August 2022)

• Record Type: Journal Article
• Authors: Chen, Gang; Zhu, Donglin; Wang, Xiao; Zhou, Changjun; Chen, Xiangyu
• Other Names: Chen Miaochao Academic Editor.
• Is Part Of: Journal of function spaces. Volume 2022(2022)
• Publication Date: 2022-08-27
• Subjects: Function spaces -- Periodicals; 515.7305
• DOI: 10.1155/2022/8799429
• Languages: English
• ISSNs: 2314-8896

Abstract

Abstract : In modern engineering construction, the compressive strength of concrete determines the safety of engineering structure. BP neural network (BPNN) tends to converge to different local minimum points, and the prediction accuracy is not high in the prediction of the compressive strength of concrete. Therefore, a prediction model based on the BPNN optimized by improved sparrow search algorithm (ISSA) and random forest (RF) is proposed to enhance the generalization ability and prediction accuracy of BPNN for compressive strength of concrete. In terms of algorithm improvement, three improvements are proposed for SSA: Latin hypercube sampling is introduced to initialize the location of sparrows and increase the diversity of sparrows; the somersault foraging strategy is used to enrich the optimal position of producers; and combining with the cyclone foraging mechanism, the position updating process of the scroungers is optimized to obtain a better foraging position. In terms of performance evaluation of the algorithm, the ablation experiment verifies that the three improved strategies have improved effects in SSA, and the performance of ISSA on the CEC2017 benchmark function is better than other peers. In terms of predictive index screening, the important features are selected as the input variables of the model by random forest. The prediction results show that compared with the RF-BPNN model and models optimized by other algorithms, RF-ISSA-BPNN model has the lowest prediction error, and the expected value fits the real value better.