Staff View: Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes

Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes

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Bibliographic Details
Main Author:	Ho, Nang Xuan
Other Authors:	Le, Tien-Thinh
Format:	Article
Language:	English
Published:	Measurement 2021
Subjects:	Variability propagation Regression machine-learning models Circular concrete-filled steel tubes Support vector machine
Online Access:	https://www.sciencedirect.com/science/article/abs/pii/S0263224121002165?via%3Dihub#! https://dlib.phenikaa-uni.edu.vn/handle/PNK/1772 https://doi.org/10.1016/j.measurement.2021.109198
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spelling	oai:localhost:PNK-17722022-08-17T05:54:41Z Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes Ho, Nang Xuan Le, Tien-Thinh Variability propagation Regression machine-learning models Circular concrete-filled steel tubes Support vector machine Q1 This study investigates the performance and robustness of regression machine-learning models in the presence of variability in the experimental database. The main objective of this work is to predict the ultimate load of circular concrete-filled steel tubes. The simulations were designed by combining size of the learning dataset, random realizations and prediction models. The variability (i.e. probability density function of each variable) is propagated to the output response through the regression machine-learning models. Results show that such variability must be considered when training and testing regression machine-learning models. The performance and robustness of the prediction models are presented and discussed. Based on the most robust and efficient model, a prediction equation is proposed for practical use. After conducting a comparison investigation, the performance of the proposed equation is found superior to one of current models. Finally, the proposed equation is implemented in Excel and appended to this paper. 2021-06-15T04:43:04Z 2021-06-15T04:43:04Z 2021 Article Working Paper https://www.sciencedirect.com/science/article/abs/pii/S0263224121002165?via%3Dihub#! https://dlib.phenikaa-uni.edu.vn/handle/PNK/1772 https://doi.org/10.1016/j.measurement.2021.109198 en Measurement
institution	Digital Phenikaa
collection	Digital Phenikaa
language	English
topic	Variability propagation Regression machine-learning models Circular concrete-filled steel tubes Support vector machine
spellingShingle	Variability propagation Regression machine-learning models Circular concrete-filled steel tubes Support vector machine Ho, Nang Xuan Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes
description	Q1
author2	Le, Tien-Thinh
author_facet	Le, Tien-Thinh Ho, Nang Xuan
format	Article
author	Ho, Nang Xuan
author_sort	Ho, Nang Xuan
title	Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes
title_short	Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes
title_full	Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes
title_fullStr	Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes
title_full_unstemmed	Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes
title_sort	effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes
publisher	Measurement
publishDate	2021
url	https://www.sciencedirect.com/science/article/abs/pii/S0263224121002165?via%3Dihub#! https://dlib.phenikaa-uni.edu.vn/handle/PNK/1772 https://doi.org/10.1016/j.measurement.2021.109198
_version_	1751856251773386752
score	8.887929

Effects of variability in experimental database on machine-learning-based prediction of ultimate load of circular concrete-filled steel tubes

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