Title: Study on carbonation life prediction of reinforced concrete structures based on improved support vector regression

Authors: Lei Wang; Bing Chen; Junfeng Wu

Addresses: Faculty of Engineering, Huanghe Science and Technology University, Zhengzhou, 450063, China; Henan Engineering Research Center of Acoustic Meta-Structure, Huanghe Science and Technology University, Zhengzhou, 450063, China ' Faculty of Engineering, Huanghe Science and Technology University, Zhengzhou, 450063, China; Henan Engineering Research Center of Acoustic Meta-Structure, Huanghe Science and Technology University, Zhengzhou, 450063, China ' Faculty of Engineering, Huanghe Science and Technology University, Zhengzhou, 450063, China; Henan Engineering Research Center of Acoustic Meta-Structure, Huanghe Science and Technology University, Zhengzhou, 450063, China

Abstract: The accurate prediction of the carbonation life of reinforced concrete structures can effectively prevent and avoid the risk of reinforced concrete structures. This paper proposes a carbonation life prediction method of reinforced concrete structures based on improved support vector regression. Firstly, 19 prediction model inputs are selected according to the analytic hierarchy process, mainly including cement content, water cement ratio, aggregate type, aggregate size, relative humidity, temperature, concrete vibration time, etc. Secondly, genetic algorithm is used to improve the support vector regression method. Finally, the carbonisation life prediction model of reinforced concrete structure is built and trained through the improved support vector regression, and the carbonisation life of reinforced concrete structure is predicted according to the trained model. The experimental results show that the proposed method has a smaller absolute error of only 0.222, the maximum RMSE value is only 0.336, and the fitting degree is closer to 1.

Keywords: improved support vector regression; reinforced concrete structure; carbonisation life; genetic algorithm; prediction model.

DOI: 10.1504/IJMPT.2024.143440

International Journal of Materials and Product Technology, 2024 Vol.69 No.1/2, pp.17 - 38

Received: 03 Jan 2024
Accepted: 13 May 2024
Published online: 20 Dec 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article