To read the full version of this content please select one of the options below:

Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network

Jinsong Tu (West Anhui University, Liu'an, China)
Yuanzhen Liu (Taiyuan University of Technology, Taiyuan, China)
Ming Zhou (West Anhui University, Liu'an, China)
Ruixia Li (West Anhui University, Liu'an, China)

Journal of Engineering, Design and Technology

ISSN: 1726-0531

Article publication date: 12 August 2020

Issue publication date: 7 April 2021

Abstract

Purpose

This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.

Design/methodology/approach

The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform.

Findings

Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better.

Originality/value

The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

Keywords

Acknowledgements

The authors are grateful to the financial support from the National Natural Science Foundation of China (no.51678384), Scientific Research Project of the Anhui Provincial Education Department (nos. KJ2018A0414 and KJ2018A0415).

Data Availability: The data used to support the findings of this study are included within the article.

Disclosure: However, the opinions expressed in this paper are solely of the authors.

Conflicts of Interest: The authors declare that they have no conflicts of interest.

Citation

Tu, J., Liu, Y., Zhou, M. and Li, R. (2021), "Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network", Journal of Engineering, Design and Technology, Vol. 19 No. 2, pp. 412-422. https://doi.org/10.1108/JEDT-01-2020-0022

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited