On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, nam...

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Autores principales:	Duong Kien Trong, Binh Thai Pham, Fazal E. Jalal, Mudassir Iqbal, Panayiotis C. Roussis, Anna Mamou, Maria Ferentinou, Dung Quang Vu, Nguyen Duc Dam, Quoc Anh Tran, Panagiotis G. Asteris
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	California Bearing Ratio modulus of subgrade reaction elastic modulus metaheuristic algorithms Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/59ee155596294df6b4582f9338ca8190
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spelling	oai:doaj.org-article:59ee155596294df6b4582f9338ca81902021-11-11T18:05:06ZOn Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils10.3390/ma142165161996-1944https://doaj.org/article/59ee155596294df6b4582f9338ca81902021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/21/6516https://doaj.org/toc/1996-1944The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique.Duong Kien TrongBinh Thai PhamFazal E. JalalMudassir IqbalPanayiotis C. RoussisAnna MamouMaria FerentinouDung Quang VuNguyen Duc DamQuoc Anh TranPanagiotis G. AsterisMDPI AGarticleCalifornia Bearing Ratiomodulus of subgrade reactionelastic modulusmetaheuristic algorithmsTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6516, p 6516 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	California Bearing Ratio modulus of subgrade reaction elastic modulus metaheuristic algorithms Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	California Bearing Ratio modulus of subgrade reaction elastic modulus metaheuristic algorithms Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Duong Kien Trong Binh Thai Pham Fazal E. Jalal Mudassir Iqbal Panayiotis C. Roussis Anna Mamou Maria Ferentinou Dung Quang Vu Nguyen Duc Dam Quoc Anh Tran Panagiotis G. Asteris On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils
description	The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique.
format	article
author	Duong Kien Trong Binh Thai Pham Fazal E. Jalal Mudassir Iqbal Panayiotis C. Roussis Anna Mamou Maria Ferentinou Dung Quang Vu Nguyen Duc Dam Quoc Anh Tran Panagiotis G. Asteris
author_facet	Duong Kien Trong Binh Thai Pham Fazal E. Jalal Mudassir Iqbal Panayiotis C. Roussis Anna Mamou Maria Ferentinou Dung Quang Vu Nguyen Duc Dam Quoc Anh Tran Panagiotis G. Asteris
author_sort	Duong Kien Trong
title	On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils
title_short	On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils
title_full	On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils
title_fullStr	On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils
title_full_unstemmed	On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils
title_sort	on random subspace optimization-based hybrid computing models predicting the california bearing ratio of soils
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/59ee155596294df6b4582f9338ca8190
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On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

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