A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN),...

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Autores principales:	Binh Thai Pham, Manh Duc Nguyen, Nadhir Al-Ansari, Quoc Anh Tran, Lanh Si Ho, Hiep Van Le, Indra Prakash
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/f3fd6398612e4faaa6c78da25976c565
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spelling	oai:doaj.org-article:f3fd6398612e4faaa6c78da25976c5652021-11-29T00:56:31ZA Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil1563-514710.1155/2021/7631493https://doaj.org/article/f3fd6398612e4faaa6c78da25976c5652021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7631493https://doaj.org/toc/1563-5147Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil.Binh Thai PhamManh Duc NguyenNadhir Al-AnsariQuoc Anh TranLanh Si HoHiep Van LeIndra PrakashHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Binh Thai Pham Manh Duc Nguyen Nadhir Al-Ansari Quoc Anh Tran Lanh Si Ho Hiep Van Le Indra Prakash A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil
description	Determination of the permeability coefficient (K) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient (K) of soil (10−9 cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio (e), specific density (g/cm3), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient (R) were used to evaluate and compare performance of the models. The results show that all the three models performed well (R > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R = 0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient (K) of the soil.
format	article
author	Binh Thai Pham Manh Duc Nguyen Nadhir Al-Ansari Quoc Anh Tran Lanh Si Ho Hiep Van Le Indra Prakash
author_facet	Binh Thai Pham Manh Duc Nguyen Nadhir Al-Ansari Quoc Anh Tran Lanh Si Ho Hiep Van Le Indra Prakash
author_sort	Binh Thai Pham
title	A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil
title_short	A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil
title_full	A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil
title_fullStr	A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil
title_full_unstemmed	A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil
title_sort	comparative study of soft computing models for prediction of permeability coefficient of soil
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/f3fd6398612e4faaa6c78da25976c565
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A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

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