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Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models

The current research aims to investigate the parameters’ effect on the confinement coefficient, Ks, forecast using machine learning. Because various parameters affect the Ks, a new computational model has been developed to investigate this issue. Six parameters are among the effective parameters bas...

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Main Authors: Cheng, Gege, Lai, Sai Hin, A. Rashid, Ahmad Safuan, Ulrikh, Dmitrii Vladimirovich, Wang, Bin
Format: Article
Language:English
Published: MDPI 2023
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/107239/1/AhmadSafuanA2023_InvestigatingtheEffectofParametersonConfinement.pdf
http://eprints.utm.my/107239/
http://dx.doi.org/10.3390/su15010199
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spelling my.utm.1072392024-09-01T06:20:57Z http://eprints.utm.my/107239/ Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models Cheng, Gege Lai, Sai Hin A. Rashid, Ahmad Safuan Ulrikh, Dmitrii Vladimirovich Wang, Bin TA Engineering (General). Civil engineering (General) The current research aims to investigate the parameters’ effect on the confinement coefficient, Ks, forecast using machine learning. Because various parameters affect the Ks, a new computational model has been developed to investigate this issue. Six parameters are among the effective parameters based on previous research. Therefore, according to the dimensions of the variables in the problem, a supply–demand-based optimization (SDO) model was developed. The performance of this model is directly dependent on its main parameters, such as market size and iteration. Then, to compare the performance of the SDO model, classical models, including particle swarm size (PSO), imperialism competitive algorithm (ICA), and genetic algorithm (GA), were used. Finally, the best-developed model used different parameters to check the uncertainty obtained. For the test results, the new SDO-ANFIS model was able to obtain values of 0.9449 and 0.134 for the coefficient of determination (R2), and root mean square error (RMSE), which performed better than other models. Due to the different relationships between the parameters, different designed conditions were considered and developed based on the hybrid model and, finally, the number of longitudinal bars and diameter of lateral ties were obtained as the strongest and weakest parameters based on the developed model for this study. MDPI 2023-01 Article PeerReviewed application/pdf en http://eprints.utm.my/107239/1/AhmadSafuanA2023_InvestigatingtheEffectofParametersonConfinement.pdf Cheng, Gege and Lai, Sai Hin and A. Rashid, Ahmad Safuan and Ulrikh, Dmitrii Vladimirovich and Wang, Bin (2023) Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models. Sustainability (Switzerland), 15 (1). pp. 1-20. ISSN 2071-1050 http://dx.doi.org/10.3390/su15010199 DOI:10.3390/su15010199
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Cheng, Gege
Lai, Sai Hin
A. Rashid, Ahmad Safuan
Ulrikh, Dmitrii Vladimirovich
Wang, Bin
Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models
description The current research aims to investigate the parameters’ effect on the confinement coefficient, Ks, forecast using machine learning. Because various parameters affect the Ks, a new computational model has been developed to investigate this issue. Six parameters are among the effective parameters based on previous research. Therefore, according to the dimensions of the variables in the problem, a supply–demand-based optimization (SDO) model was developed. The performance of this model is directly dependent on its main parameters, such as market size and iteration. Then, to compare the performance of the SDO model, classical models, including particle swarm size (PSO), imperialism competitive algorithm (ICA), and genetic algorithm (GA), were used. Finally, the best-developed model used different parameters to check the uncertainty obtained. For the test results, the new SDO-ANFIS model was able to obtain values of 0.9449 and 0.134 for the coefficient of determination (R2), and root mean square error (RMSE), which performed better than other models. Due to the different relationships between the parameters, different designed conditions were considered and developed based on the hybrid model and, finally, the number of longitudinal bars and diameter of lateral ties were obtained as the strongest and weakest parameters based on the developed model for this study.
format Article
author Cheng, Gege
Lai, Sai Hin
A. Rashid, Ahmad Safuan
Ulrikh, Dmitrii Vladimirovich
Wang, Bin
author_facet Cheng, Gege
Lai, Sai Hin
A. Rashid, Ahmad Safuan
Ulrikh, Dmitrii Vladimirovich
Wang, Bin
author_sort Cheng, Gege
title Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models
title_short Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models
title_full Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models
title_fullStr Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models
title_full_unstemmed Investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models
title_sort investigating the effect of parameters on confinement coefficient of reinforced concrete using development of learning machine models
publisher MDPI
publishDate 2023
url http://eprints.utm.my/107239/1/AhmadSafuanA2023_InvestigatingtheEffectofParametersonConfinement.pdf
http://eprints.utm.my/107239/
http://dx.doi.org/10.3390/su15010199
_version_ 1809136646072827904
score 13.211869

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