ICA-LightGBM Algorithm for Predicting Compressive Strength of Geo-Polymer Concrete

Wang, Q; Qi, J; Hosseini, S; Rasekh, H; Huang, J

ICA-LightGBM Algorithm for Predicting Compressive Strength of Geo-Polymer Concrete

Wang, Q Qi, J Hosseini, S Rasekh, H

Huang, J

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Buildings, 2023, 13, (9), pp. 2278
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Wang, Q
dc.contributor.author	Qi, J
dc.contributor.author	Hosseini, S
dc.contributor.author	Rasekh, H https://orcid.org/0000-0003-2064-0753
dc.contributor.author	Huang, J
dc.date.accessioned	2024-05-08T06:20:23Z
dc.date.available	2024-05-08T06:20:23Z
dc.date.issued	2023-09-01
dc.identifier.citation	Buildings, 2023, 13, (9), pp. 2278
dc.identifier.issn	2075-5309
dc.identifier.issn	2075-5309
dc.identifier.uri	http://hdl.handle.net/10453/178742
dc.description.abstract	The main goal of the present study is to investigate the capability of hybridizing the imperialist competitive algorithm (ICA) with an intelligent, robust, and data-driven technique named the light gradient boosting machine (LightGBM) to estimate the compressive strength of geo-polymer concrete (CSGCo). The hyper-parameters of the LightGBM algorithm have been optimized based on ICA and its accuracy improved. The obtained results from the proposed hybrid ICA-LightGBM are compared with the traditional LightGBM model as well as four different topologies of artificial neural networks (ANN) comprising a multi-layer perceptron neural network (MLP), radial basis function (RBF), generalized feed-forward neural network (GFFNN), and Bayesian regularized neural network (BRNN). The results of these models were compared based on three evaluation indices of R2, RMSE, and VAF for providing an objective evaluation of the performance and capability of the predictive models. Concerning the outcomes, the ICA-LightGBM with the R2 of (0.9871 and 0.9805), RMSE of (0.4703 and 1.3137), and VAF of (98.5773 and 98.0397) for training and testing phases, respectively, was a superior predictor to estimate the CSGCo compared to the LightGBM with the R2 of (0.9488 and 0.9478), RMSE of (0.9532 and 2.1631), and VAF of (94.3613 and 94.5173); the MLP with the R2 of (0.9067 and 0.8959), RMSE of (1.3093 and 3.3648), and VAF of (88.9888 and 84.9125); the RBF with the R2 of (0.8694 and 0.8055), RMSE of (1.4703 and 5.0309), and VAF of (86.3122 and 66.1888); the BRNN with the R2 of (0.9212 and 0.9107), RMSE of (1.1510 and 2.6569), and VAF of (91.4168 and 90.5854); and the GFFNN with the R2 of (0.9144 and 0.8925), RMSE of (1.1525 and 2.9415), and VAF of (91.4092 and 88.9088). Hence, the proposed ICA-LightGBM algorithm can be efficiently used in anticipating the CSGCo.
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Buildings
dc.relation.isbasedon	10.3390/buildings13092278
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1201 Architecture, 1202 Building, 1203 Design Practice and Management
dc.subject.classification	3301 Architecture
dc.subject.classification	3302 Building
dc.subject.classification	4005 Civil engineering
dc.title	ICA-LightGBM Algorithm for Predicting Compressive Strength of Geo-Polymer Concrete
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	1201 Architecture
utslib.for	1202 Building
utslib.for	1203 Design Practice and Management
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-05-08T06:20:21Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	9

Abstract:

The main goal of the present study is to investigate the capability of hybridizing the imperialist competitive algorithm (ICA) with an intelligent, robust, and data-driven technique named the light gradient boosting machine (LightGBM) to estimate the compressive strength of geo-polymer concrete (CSGCo). The hyper-parameters of the LightGBM algorithm have been optimized based on ICA and its accuracy improved. The obtained results from the proposed hybrid ICA-LightGBM are compared with the traditional LightGBM model as well as four different topologies of artificial neural networks (ANN) comprising a multi-layer perceptron neural network (MLP), radial basis function (RBF), generalized feed-forward neural network (GFFNN), and Bayesian regularized neural network (BRNN). The results of these models were compared based on three evaluation indices of R2, RMSE, and VAF for providing an objective evaluation of the performance and capability of the predictive models. Concerning the outcomes, the ICA-LightGBM with the R2 of (0.9871 and 0.9805), RMSE of (0.4703 and 1.3137), and VAF of (98.5773 and 98.0397) for training and testing phases, respectively, was a superior predictor to estimate the CSGCo compared to the LightGBM with the R2 of (0.9488 and 0.9478), RMSE of (0.9532 and 2.1631), and VAF of (94.3613 and 94.5173); the MLP with the R2 of (0.9067 and 0.8959), RMSE of (1.3093 and 3.3648), and VAF of (88.9888 and 84.9125); the RBF with the R2 of (0.8694 and 0.8055), RMSE of (1.4703 and 5.0309), and VAF of (86.3122 and 66.1888); the BRNN with the R2 of (0.9212 and 0.9107), RMSE of (1.1510 and 2.6569), and VAF of (91.4168 and 90.5854); and the GFFNN with the R2 of (0.9144 and 0.8925), RMSE of (1.1525 and 2.9415), and VAF of (91.4092 and 88.9088). Hence, the proposed ICA-LightGBM algorithm can be efficiently used in anticipating the CSGCo.

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http://hdl.handle.net/10453/178742