City Research Online

Abstract

Given the difficulty in determining the parameters of the compressive strength prediction model of self-compacting concrete and the low prediction accuracy, this study focuses on the applicability of the relevance vector machine (RVM) model constructed using various optimization techniques in predicting the strength of self-compacting concrete. The principal component analysis (PCA) is first used to reduce the dimension of the influencing factors. Then, the particle swarm optimization algorithm (PSO) is introduced into the RVM to establish a PCA–PSO–RVM collaborative optimization model, which is compared with the traditional regression model through various statistical indicators and error analysis. The results show that the collaborative optimization model prediction based on PCA–PSO–RVM performs outstandingly in all performance indicators. In the test set, the R2 of the collaborative optimization model is 0.978, MAE is 0.123, MSE is 0.021, and RMSE is 0.150. The evaluation of quantitative indicators verifies that the collaborative optimization model is feasible and advanced in predicting the strength of self-compacting concrete. This study also provides a reference for the research on durability, rheological properties, and other material predictions of self-compacting concrete.

Publication Type:	Article
Additional Information:	© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher Keywords:	Machine learning, Relevance vector machine, Principal component analysis, Particle swarm optimization, Self-compacting concrete, Strength prediction
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) T Technology > TH Building construction
Departments:	School of Science & Technology School of Science & Technology > Department of Engineering
SWORD Depositor:	Symplectic Administrator

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