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Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups
The reinforced concrete (RC) member’s shear strength estimation has been experimentally studied in most cases due to its nonlinear behavior. Many empirical equations have been derived from the experimental data; however, even those adopted in the construction codes do not thoroughly and accurately d...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269491/ https://www.ncbi.nlm.nih.gov/pubmed/34206496 http://dx.doi.org/10.3390/ma14133471 |
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author | Koo, Seungbum Shin, Dongik Kim, Changhyuk |
author_facet | Koo, Seungbum Shin, Dongik Kim, Changhyuk |
author_sort | Koo, Seungbum |
collection | PubMed |
description | The reinforced concrete (RC) member’s shear strength estimation has been experimentally studied in most cases due to its nonlinear behavior. Many empirical equations have been derived from the experimental data; however, even those adopted in the construction codes do not thoroughly and accurately describe their shear behavior. Theoretically explained equations, on the other hand, are aligned with the experiment; however, they are complicated to use in practice. As shear behavior research is data-driven, the machine learning technique is applicable. Herein, an artificial neural network (ANN) algorithm is trained with 776 experiment results collected from available publications. The raw data is preprocessed by principal component analysis (PCA) before the application of the ANN technique. The predictions of the trained algorithm using ANN with PCA are compared to those of formulae adopted in a few existing building codes. Finally, a parametric study is conducted, and the significance of each variable to the strength of RC members is analyzed. |
format | Online Article Text |
id | pubmed-8269491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82694912021-07-10 Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups Koo, Seungbum Shin, Dongik Kim, Changhyuk Materials (Basel) Article The reinforced concrete (RC) member’s shear strength estimation has been experimentally studied in most cases due to its nonlinear behavior. Many empirical equations have been derived from the experimental data; however, even those adopted in the construction codes do not thoroughly and accurately describe their shear behavior. Theoretically explained equations, on the other hand, are aligned with the experiment; however, they are complicated to use in practice. As shear behavior research is data-driven, the machine learning technique is applicable. Herein, an artificial neural network (ANN) algorithm is trained with 776 experiment results collected from available publications. The raw data is preprocessed by principal component analysis (PCA) before the application of the ANN technique. The predictions of the trained algorithm using ANN with PCA are compared to those of formulae adopted in a few existing building codes. Finally, a parametric study is conducted, and the significance of each variable to the strength of RC members is analyzed. MDPI 2021-06-22 /pmc/articles/PMC8269491/ /pubmed/34206496 http://dx.doi.org/10.3390/ma14133471 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Koo, Seungbum Shin, Dongik Kim, Changhyuk Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups |
title | Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups |
title_full | Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups |
title_fullStr | Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups |
title_full_unstemmed | Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups |
title_short | Application of Principal Component Analysis Approach to Predict Shear Strength of Reinforced Concrete Beams with Stirrups |
title_sort | application of principal component analysis approach to predict shear strength of reinforced concrete beams with stirrups |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269491/ https://www.ncbi.nlm.nih.gov/pubmed/34206496 http://dx.doi.org/10.3390/ma14133471 |
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