This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.
The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform.
Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better.
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.
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.
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
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