Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study

Volumetric stability and erodibility are important soil properties influenced by moisture through raindrops and eventual runoff and the rise in water tables during wet seasons. Compacted subgrade materials made of clay respond to water ingress through swelling and shrinking in turn during drying and...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Kennedy C. Onyelowe, Ahmed M. Ebid, Light I. Nwobia
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Cleaner & green materials Genetic programming (GP) Artificial neural network (ANN) Evolutionary polynomial regression (EPR) Nanotextured agro-waste ashes Erodibility Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/e8dad07b234c44f0a3c86e0c5f95f42c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e8dad07b234c44f0a3c86e0c5f95f42c
record_format	dspace
spelling	oai:doaj.org-article:e8dad07b234c44f0a3c86e0c5f95f42c2021-12-01T05:07:02ZPredictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study2772-397610.1016/j.clema.2021.100006https://doaj.org/article/e8dad07b234c44f0a3c86e0c5f95f42c2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S277239762100006Xhttps://doaj.org/toc/2772-3976Volumetric stability and erodibility are important soil properties influenced by moisture through raindrops and eventual runoff and the rise in water tables during wet seasons. Compacted subgrade materials made of clay respond to water ingress through swelling and shrinking in turn during drying and this poses a problem for foundation structures. Supplementary cementitious materials have been used to treat soils, in a cleaner procedure to improve the mechanical properties and to overcome undesirable behavior during changes in seasons. However, design and construction of foundation structures exposed to these problems become necessary and common, which requires constant visits to the laboratory and equipment needs. In order to overcome this, machine learning-based predictive models have been proposed in this work for the estimation of durability (Sv) via loss of strength on immersion technique and erodibility (Er) of agro-based ashes. Genetic programming (GP) (six levels of complexity), artificial neural network (ANN) (sigmoid activation function), evolutionary polynomial regression (EPR) (GA optimized PLR method) techniques have been used to conduct this intelligent prediction exercise. The performance of the models was conducted using the sum of squared errors (SSE) and coefficient of determination (R2) indices. The results show that EPR’s Er and Sv prediction with SSE of 5.1% and 2.7% respectively and R2 of 97.2% and 92.9% respectively outclassed GP and ANN. However, both GP and ANN showed minimal error and acceptable R2 above 0.85, which showed their ability to predict with good performance accuracy.Kennedy C. OnyeloweAhmed M. EbidLight I. NwobiaElsevierarticleCleaner & green materialsGenetic programming (GP)Artificial neural network (ANN)Evolutionary polynomial regression (EPR)Nanotextured agro-waste ashesErodibilityMaterials of engineering and construction. Mechanics of materialsTA401-492ENCleaner Materials, Vol 1, Iss , Pp 100006- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cleaner & green materials Genetic programming (GP) Artificial neural network (ANN) Evolutionary polynomial regression (EPR) Nanotextured agro-waste ashes Erodibility Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Cleaner & green materials Genetic programming (GP) Artificial neural network (ANN) Evolutionary polynomial regression (EPR) Nanotextured agro-waste ashes Erodibility Materials of engineering and construction. Mechanics of materials TA401-492 Kennedy C. Onyelowe Ahmed M. Ebid Light I. Nwobia Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study
description	Volumetric stability and erodibility are important soil properties influenced by moisture through raindrops and eventual runoff and the rise in water tables during wet seasons. Compacted subgrade materials made of clay respond to water ingress through swelling and shrinking in turn during drying and this poses a problem for foundation structures. Supplementary cementitious materials have been used to treat soils, in a cleaner procedure to improve the mechanical properties and to overcome undesirable behavior during changes in seasons. However, design and construction of foundation structures exposed to these problems become necessary and common, which requires constant visits to the laboratory and equipment needs. In order to overcome this, machine learning-based predictive models have been proposed in this work for the estimation of durability (Sv) via loss of strength on immersion technique and erodibility (Er) of agro-based ashes. Genetic programming (GP) (six levels of complexity), artificial neural network (ANN) (sigmoid activation function), evolutionary polynomial regression (EPR) (GA optimized PLR method) techniques have been used to conduct this intelligent prediction exercise. The performance of the models was conducted using the sum of squared errors (SSE) and coefficient of determination (R2) indices. The results show that EPR’s Er and Sv prediction with SSE of 5.1% and 2.7% respectively and R2 of 97.2% and 92.9% respectively outclassed GP and ANN. However, both GP and ANN showed minimal error and acceptable R2 above 0.85, which showed their ability to predict with good performance accuracy.
format	article
author	Kennedy C. Onyelowe Ahmed M. Ebid Light I. Nwobia
author_facet	Kennedy C. Onyelowe Ahmed M. Ebid Light I. Nwobia
author_sort	Kennedy C. Onyelowe
title	Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study
title_short	Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study
title_full	Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study
title_fullStr	Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study
title_full_unstemmed	Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study
title_sort	predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; gp, ann and epr performance study
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/e8dad07b234c44f0a3c86e0c5f95f42c
work_keys_str_mv	AT kennedyconyelowe predictivemodelsofvolumetricstabilitydurabilityanderodibilityoflateriticsoiltreatedwithdifferentnanotexturedbioasheswithapplicationoflossofstrengthonimmersiongpannandeprperformancestudy AT ahmedmebid predictivemodelsofvolumetricstabilitydurabilityanderodibilityoflateriticsoiltreatedwithdifferentnanotexturedbioasheswithapplicationoflossofstrengthonimmersiongpannandeprperformancestudy AT lightinwobia predictivemodelsofvolumetricstabilitydurabilityanderodibilityoflateriticsoiltreatedwithdifferentnanotexturedbioasheswithapplicationoflossofstrengthonimmersiongpannandeprperformancestudy
_version_	1718405575279116288

Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study

Ejemplares similares