A Comparative Study on Compressive Strength Model of Recycled BrickAggregate Concrete Based on PSO-BP and GA-BP Neural Networks
HUANG Wei1,2,3, ZHOU Lang2, GE Pei2, YANG Tao2
1 State Key Laboratory of Green Building in Western China, Xi’an University of Architecture & Technology, Xi’an 710055, China 2 School of Civil Engineering, Xi’an University of Architecture & Technology, Xi’an 710055, China 3 Key Laboratory of Structural Engineering and Earthquake Resistance of Ministry of Education, Xi’an University of Architecture & Technology, Xi’an 710055, China
Abstract: Two hybrid algorithm artificial neural network models (PSO-BP and GA-BP) are used to predict the compressive strength of recycled brick aggregate concrete (RBAC) with different brick aggregate replacement rates. The cement quality, water-cement ratio, replacement rate of broken ceramic tile (CT 0—5, CT 5—32.5), broken brick (CB 0—5, CB 5—32.5) and natural aggregate (NA 0—5, NA 5—32.5) of RBAC were used as input parameters of the hybrid neural network model, and the 28 day cube compressive strength was taken as output parameter. Root mean square error (RMSE), correlation coefficient (R) and average error rate were used to verify and compare the two models. The results show that both the PSO-BP model and the GA-BP model can achieve high-precision prediction and have strong generalization capabilities, In ge-neral the PSO-BP model is slightly better than the GA-BP,and both are better than BP model. At the same time, it also proves that the proposed hybrid algorithm neural network is helpful to find the best RBAC mix ratio design and improve the experimental efficiency.
黄炜, 周烺, 葛培, 杨涛. 基于PSO-BP和GA-BP神经网络再生砖骨料混凝土强度模型的对比研究[J]. 材料导报, 2021, 35(15): 15026-15030.
HUANG Wei, ZHOU Lang, GE Pei, YANG Tao. A Comparative Study on Compressive Strength Model of Recycled BrickAggregate Concrete Based on PSO-BP and GA-BP Neural Networks. Materials Reports, 2021, 35(15): 15026-15030.
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2021, Vol. 35 
