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

doi:10.11896/cldb.20070041

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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.

Key words： artificial neural network particle swarm optimization genetic algorithm recycled brick aggregate compressive strength concrete

Published: 31 August 2021

CLC number:	TU528
	TP312

Fund:National Natural Science Foundation of China (51978566), Key R & D Projects of Shaanxi Province-Key Industry Innovation Project (2020ZDLNY06-04).

About author:: Wei Huang, a professor of Xi’an University of Architecture and Technology, a master’s tutor, director of the Institute of Architectural Engineering and New Technology of Xi’an University of Architecture and Technology, deputy director of the Shaanxi Civil Engineering Society Prefabricated Building Committee, and a middle-aged and young scientific and technological innovation leader in Shaanxi Province. In recent years, he has been engaged in research on new structural systems, earthquake resistance and strength theory of engineering structures. As the project leader and main finisher, he participated in the research work of many topics such as the China Postdoctoral Fund, the National Natural Science Foundation of China, and the National “Tenth Five-Year Plan” Scientific and Technological Research. In the Journal of Building Structures, Journal of Civil Engineering, Engineering Mechanics, etc., more than 40 papers were published.
Lang Zhou, studying in Xi’an University of Architecture and Technology in September 2018, is studying for a master’s degree. Mainly engaged in research on the direction of green assembled recycled materials.

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	HUANG Wei
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HUANG Wei,ZHOU Lang,GE Pei, et al. A Comparative Study on Compressive Strength Model of Recycled BrickAggregate Concrete Based on PSO-BP and GA-BP Neural Networks[J]. Materials Reports, 2021, 35(15): 15026-15030.

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http://www.mater-rep.com/EN/10.11896/cldb.20070041 OR http://www.mater-rep.com/EN/Y2021/V35/I15/15026

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