Application of Genetic Algorithms for the Estimation of Hydraulic Conductivity

In the study described here model calibration was performed employing the inverse analysis using genetic algorithms (GA). The objective of analysis is to determine value of the coefficient of hydraulic conductivity, k. The commonly used method for the determination of coefficient of hydraulic conduc...

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Main Authors: Bartlewska-Urban M., Strzelecki T.
Format: Article
Language:English
Published: Sciendo 2018-08-01
Series:Studia Geotechnica et Mechanica
Subjects:
Online Access:https://doi.org/10.2478/sgem-2018-0013
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spelling doaj-d5aa4a402c36402d8acafddb66c0017f2021-09-05T14:01:52ZengSciendoStudia Geotechnica et Mechanica2083-831X2018-08-0140214014610.2478/sgem-2018-0013sgem-2018-0013Application of Genetic Algorithms for the Estimation of Hydraulic ConductivityBartlewska-Urban M.0Strzelecki T.1Wrocław University of Technology, Faculty of Geoengineering, Mining and Geology, Wrocław, PolandWrocław University of Technology, Faculty of Technology And Natural Sciences, Wrocław, PolandIn the study described here model calibration was performed employing the inverse analysis using genetic algorithms (GA). The objective of analysis is to determine value of the coefficient of hydraulic conductivity, k. The commonly used method for the determination of coefficient of hydraulic conductivity based on Terzaghi consolidation leads to an underestimation of the value of k as the Terzaghi model does not take into account the deformation of soil skeleton. Here, an alternative methodology based on genetic algorithms is presented for the determination of the basic parameters of Biot consolidation model. It has been demonstrated that genetic algorithms are a highly effective tool enabling automatic calibration based on simple rules. The values of the coefficient of hydraulic conductivity obtained with GA are of at least one order smaller than values obtained with the Terzaghi model.https://doi.org/10.2478/sgem-2018-0013genetic algorithmshydraulic conductivitybiot consolidation model
collection DOAJ
language English
format Article
sources DOAJ
author Bartlewska-Urban M.
Strzelecki T.
spellingShingle Bartlewska-Urban M.
Strzelecki T.
Application of Genetic Algorithms for the Estimation of Hydraulic Conductivity
Studia Geotechnica et Mechanica
genetic algorithms
hydraulic conductivity
biot consolidation model
author_facet Bartlewska-Urban M.
Strzelecki T.
author_sort Bartlewska-Urban M.
title Application of Genetic Algorithms for the Estimation of Hydraulic Conductivity
title_short Application of Genetic Algorithms for the Estimation of Hydraulic Conductivity
title_full Application of Genetic Algorithms for the Estimation of Hydraulic Conductivity
title_fullStr Application of Genetic Algorithms for the Estimation of Hydraulic Conductivity
title_full_unstemmed Application of Genetic Algorithms for the Estimation of Hydraulic Conductivity
title_sort application of genetic algorithms for the estimation of hydraulic conductivity
publisher Sciendo
series Studia Geotechnica et Mechanica
issn 2083-831X
publishDate 2018-08-01
description In the study described here model calibration was performed employing the inverse analysis using genetic algorithms (GA). The objective of analysis is to determine value of the coefficient of hydraulic conductivity, k. The commonly used method for the determination of coefficient of hydraulic conductivity based on Terzaghi consolidation leads to an underestimation of the value of k as the Terzaghi model does not take into account the deformation of soil skeleton. Here, an alternative methodology based on genetic algorithms is presented for the determination of the basic parameters of Biot consolidation model. It has been demonstrated that genetic algorithms are a highly effective tool enabling automatic calibration based on simple rules. The values of the coefficient of hydraulic conductivity obtained with GA are of at least one order smaller than values obtained with the Terzaghi model.
topic genetic algorithms
hydraulic conductivity
biot consolidation model
url https://doi.org/10.2478/sgem-2018-0013
work_keys_str_mv AT bartlewskaurbanm applicationofgeneticalgorithmsfortheestimationofhydraulicconductivity
AT strzeleckit applicationofgeneticalgorithmsfortheestimationofhydraulicconductivity
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