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Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength

Compressive strength is the most essential mechanical characterization for concrete due to its crucial role in stating the design standards. Therefore, early, and accurate evaluation of concrete compressive strength minimizes efforts, costs, and time. In this study, we investigate the ability of art...

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Main Authors: Hameed, Mohammed Majeed, AlOmar, Mohamed Khalid, Baniya, Wajdi Jaber, AlSaadi, Mohammed Abdulhakim
Format: Article
Published: Springer 2021
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/35743/
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spelling my.um.eprints.357432023-11-08T11:03:29Z http://eprints.um.edu.my/35743/ Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength Hameed, Mohammed Majeed AlOmar, Mohamed Khalid Baniya, Wajdi Jaber AlSaadi, Mohammed Abdulhakim TA Engineering (General). Civil engineering (General) Compressive strength is the most essential mechanical characterization for concrete due to its crucial role in stating the design standards. Therefore, early, and accurate evaluation of concrete compressive strength minimizes efforts, costs, and time. In this study, we investigate the ability of artificial neural network (ANN) incorporated with principal component analyses (PCA) and cross-validation (CV) techniques to forecast the high-performance concrete (HPC) compression strength. The obtained results from the ANN-CVPCA model showed a good agreement between predicted and actual values. The proposed model provides high accuracy prediction of HPC compressive strength. It also provided a higher correlation coefficient (0.96) and a lower value of mean absolute error (3.43mpa), root mean square error (4.64mpa) and normalized root mean square error (0.13). Moreover, a sensitivity analysis was carried out to identify the most influential parameters and the simulated results showed that the superplasticizer, blast furnace slag, and cement parameters respectively have great effects on the compressive strength of HPC. The performance of the ANN-CVPCA model compared with other models published in previous studies and achieved the desired superiority and more stable predictions due to the existence of PCA and CV which play a significant role in increasing the generalization ability as well as avoiding redundant data and reducing the uncertainty in modeling outcomes. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. Springer 2021-09 Article PeerReviewed Hameed, Mohammed Majeed and AlOmar, Mohamed Khalid and Baniya, Wajdi Jaber and AlSaadi, Mohammed Abdulhakim (2021) Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength. Asian Journal of Civil Engineering, 22 (6). pp. 1019-1031. ISSN 1563-0854, DOI https://doi.org/10.1007/s42107-021-00362-3 <https://doi.org/10.1007/s42107-021-00362-3>. 10.1007/s42107-021-00362-3
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Hameed, Mohammed Majeed
AlOmar, Mohamed Khalid
Baniya, Wajdi Jaber
AlSaadi, Mohammed Abdulhakim
Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength
description Compressive strength is the most essential mechanical characterization for concrete due to its crucial role in stating the design standards. Therefore, early, and accurate evaluation of concrete compressive strength minimizes efforts, costs, and time. In this study, we investigate the ability of artificial neural network (ANN) incorporated with principal component analyses (PCA) and cross-validation (CV) techniques to forecast the high-performance concrete (HPC) compression strength. The obtained results from the ANN-CVPCA model showed a good agreement between predicted and actual values. The proposed model provides high accuracy prediction of HPC compressive strength. It also provided a higher correlation coefficient (0.96) and a lower value of mean absolute error (3.43mpa), root mean square error (4.64mpa) and normalized root mean square error (0.13). Moreover, a sensitivity analysis was carried out to identify the most influential parameters and the simulated results showed that the superplasticizer, blast furnace slag, and cement parameters respectively have great effects on the compressive strength of HPC. The performance of the ANN-CVPCA model compared with other models published in previous studies and achieved the desired superiority and more stable predictions due to the existence of PCA and CV which play a significant role in increasing the generalization ability as well as avoiding redundant data and reducing the uncertainty in modeling outcomes. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
format Article
author Hameed, Mohammed Majeed
AlOmar, Mohamed Khalid
Baniya, Wajdi Jaber
AlSaadi, Mohammed Abdulhakim
author_facet Hameed, Mohammed Majeed
AlOmar, Mohamed Khalid
Baniya, Wajdi Jaber
AlSaadi, Mohammed Abdulhakim
author_sort Hameed, Mohammed Majeed
title Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength
title_short Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength
title_full Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength
title_fullStr Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength
title_full_unstemmed Incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength
title_sort incorporation of artificial neural network with principal component analysis and cross-validation technique to predict high-performance concrete compressive strength
publisher Springer
publishDate 2021
url http://eprints.um.edu.my/35743/
_version_ 1783876631603970048
score 13.223943

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