Optimal boosting method of HPC concrete compressive and tensile strength prediction

• Published in: Structural concrete : journal of the FIB Vol. 25; no. 1; pp. 283 - 302
• Main Author: Chao, Xu
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01-02-2024; Wiley Subscription Services, Inc
• Subjects: adaptive boosting; Admixtures; Algorithms; Complexity; Compressive strength; Concrete properties; Fly ash; gannet optimization algorithm; HPC compressive and tensile strengths; Machine learning; Mechanical properties; Nonlinearity; Optimization; starling murmuration optimization; Tensile strength; termite queen algorithm
• ISSN: 1464-4177; 1751-7648
• DOI: 10.1002/suco.202300078

The evaluation of fly ash (FA) and micro‐silica (MS) effects on the mechanical properties of concrete at various ages prompts the search for efficient factors in forecasting the compressive strength (CS) and tensile strength (TS), which are extendable for future investigation as well as practical use. The main aim of this article is to overcome the complexity caused by nonlinearity, which is rooted in the relationship between input variables and outputs. Also, the nonlinearity is getting worse through the existence of admixtures. The Adaptive Boosting (ADA) approach was utilized in the related study as a hybrid model to provide an optimal relation between the ingredients and the mechanical strengths for evaluating the parameters most useful in predicting the CS and TS of high‐performance concrete. Furthermore, model boosting is applied using the starling murmuration optimization (SMO), termite queen algorithm (TQO), and gannet optimization algorithm (GOA) algorithms to reach the highest convergence. As a result of assessing the related models, the ADA‐SMO obtained a coefficient correlation of 0.9981 and 0.9825 for the predictions of CS and TS, respectively. Considering the adaptive framework provided to have the best compatibility with the complex physics of the problem, the ADA‐SMO earned the highest level of accuracy in estimating CS and TS among other hybrid models and was introduced as a trustable model in the prediction of strength properties.