Modeling of soil mechanical resistance using intelligent methods

Abstract: In recent years, novel techniques such as intelligent techniques are being employed for developing predictive models to estimate parameters that are difficult to measure. For instance, determining soil mechanical resistance is difficult, particularly in fine-textured soils and during warm...

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Autores principales:	Hosseini,Mehdi, Movahedi Naeini,Seyed Ali Reza, Dehghani,Amir Ahmad, Zeraatpisheh,Mojtaba
Lenguaje:	English
Publicado:	Chilean Society of Soil Science / Sociedad Chilena de la Ciencia del Suelo 2018
Materias:	Farm management root growth soil mechanical modeling soil properties
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-95162018000400939
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spelling	oai:scielo:S0718-951620180004009392019-02-05Modeling of soil mechanical resistance using intelligent methodsHosseini,MehdiMovahedi Naeini,Seyed Ali RezaDehghani,Amir AhmadZeraatpisheh,Mojtaba Farm management root growth soil mechanical modeling soil properties Abstract: In recent years, novel techniques such as intelligent techniques are being employed for developing predictive models to estimate parameters that are difficult to measure. For instance, determining soil mechanical resistance is difficult, particularly in fine-textured soils and during warm seasons. In this research, we used statistical algorithms, adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy inference systems (FIS) in order to predict soil mechanical resistance and compared them with traditional statistical models such as multiple regression (MR). To achieve this goal, bulk density, volumetric soil water content (as predictors) and soil mechanical resistance (as target variable) were used at 0-25 cm depth with sample size equals 200. The results showed that intelligent methods are appropriate tools for minimizing the uncertainties in soil engineering projects. The ANFIS model predicted soil mechanical resistance more accurately than the other models with R2 = 0.93 and RMSE= 299.41. Also, the use of intelligent methods not only provided new approaches and methodologies to estimate soil mechanical resistance, but also minimized the potential inconsistency of correlations.info:eu-repo/semantics/openAccessChilean Society of Soil Science / Sociedad Chilena de la Ciencia del SueloJournal of soil science and plant nutrition v.18 n.4 20182018-12-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-95162018000400939en10.4067/S0718-95162018005002702
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Farm management root growth soil mechanical modeling soil properties
spellingShingle	Farm management root growth soil mechanical modeling soil properties Hosseini,Mehdi Movahedi Naeini,Seyed Ali Reza Dehghani,Amir Ahmad Zeraatpisheh,Mojtaba Modeling of soil mechanical resistance using intelligent methods
description	Abstract: In recent years, novel techniques such as intelligent techniques are being employed for developing predictive models to estimate parameters that are difficult to measure. For instance, determining soil mechanical resistance is difficult, particularly in fine-textured soils and during warm seasons. In this research, we used statistical algorithms, adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANNs) and fuzzy inference systems (FIS) in order to predict soil mechanical resistance and compared them with traditional statistical models such as multiple regression (MR). To achieve this goal, bulk density, volumetric soil water content (as predictors) and soil mechanical resistance (as target variable) were used at 0-25 cm depth with sample size equals 200. The results showed that intelligent methods are appropriate tools for minimizing the uncertainties in soil engineering projects. The ANFIS model predicted soil mechanical resistance more accurately than the other models with R2 = 0.93 and RMSE= 299.41. Also, the use of intelligent methods not only provided new approaches and methodologies to estimate soil mechanical resistance, but also minimized the potential inconsistency of correlations.
author	Hosseini,Mehdi Movahedi Naeini,Seyed Ali Reza Dehghani,Amir Ahmad Zeraatpisheh,Mojtaba
author_facet	Hosseini,Mehdi Movahedi Naeini,Seyed Ali Reza Dehghani,Amir Ahmad Zeraatpisheh,Mojtaba
author_sort	Hosseini,Mehdi
title	Modeling of soil mechanical resistance using intelligent methods
title_short	Modeling of soil mechanical resistance using intelligent methods
title_full	Modeling of soil mechanical resistance using intelligent methods
title_fullStr	Modeling of soil mechanical resistance using intelligent methods
title_full_unstemmed	Modeling of soil mechanical resistance using intelligent methods
title_sort	modeling of soil mechanical resistance using intelligent methods
publisher	Chilean Society of Soil Science / Sociedad Chilena de la Ciencia del Suelo
publishDate	2018
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-95162018000400939
work_keys_str_mv	AT hosseinimehdi modelingofsoilmechanicalresistanceusingintelligentmethods AT movahedinaeiniseyedalireza modelingofsoilmechanicalresistanceusingintelligentmethods AT dehghaniamirahmad modelingofsoilmechanicalresistanceusingintelligentmethods AT zeraatpishehmojtaba modelingofsoilmechanicalresistanceusingintelligentmethods
_version_	1714206587861598208

Modeling of soil mechanical resistance using intelligent methods

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