A comparative study on predicting the rapid chloride permeability of self‐compacting concrete using meta‐heuristic algorithm and artificial intelligence techniques

Obtaining a trustworthy approach to forecast the chloride penetration into self‐compacting concrete via rapid test may lead to frugality in cost, time, and energy to provide a durable mix design. Different single and hybrid regression methods are developed to predict the results of rapid chloride pe...

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Vydáno v:Structural concrete : journal of the FIB Ročník 23; číslo 2; s. 753 - 774
Hlavní autoři: Yuan, Jia, Zhao, Ming, Esmaeili‐Falak, Mahzad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01.04.2022
Témata:
fly ash
meta‐heuristic algorithm
prediction
rapid chloride permeability
regression methods
silica fume
ISSN:1464-4177, 1751-7648
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Shrnutí:Obtaining a trustworthy approach to forecast the chloride penetration into self‐compacting concrete via rapid test may lead to frugality in cost, time, and energy to provide a durable mix design. Different single and hybrid regression methods are developed to predict the results of rapid chloride penetration tests in the present study. Cement content, fly ash, and silica fume replacement percent with cement, temperature and fine and coarse aggregates are considered as input variables. All predicted values using expanded models have a good agreement with experimentally measured results. Evaluating the accuracy and precision of single and hybrid optimized models by five statistical performance criteria (R2, root mean square error, mean absolute error, mean absolute percentage error, and performance index) illustrates that the hybrid support vector regression with optimization algorithm is a high‐accurate promising model for predicting the results of a rapid chloride penetration test.
Bibliografie:Discussion on this paper must be submitted within two months of the print publication. The discussion will then be published in print, along with the authors’ closure, if any, approximately nine months after the print publication.
ISSN:1464-4177
1751-7648
DOI:10.1002/suco.202100682