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A unified classification model for modeling of seismic liquefaction potential of soil based on CPT
The evaluation of liquefaction potential of soil due to an earthquake is an important step in geosciences. This article examines the capability of Minimax Probability Machine (MPM) for the prediction of seismic liquefaction potential of soil based on the Cone Penetration Test (CPT) data. The dataset...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4506965/ https://www.ncbi.nlm.nih.gov/pubmed/26199749 http://dx.doi.org/10.1016/j.jare.2014.02.002 |
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author | Samui, Pijush Hariharan, R. |
author_facet | Samui, Pijush Hariharan, R. |
author_sort | Samui, Pijush |
collection | PubMed |
description | The evaluation of liquefaction potential of soil due to an earthquake is an important step in geosciences. This article examines the capability of Minimax Probability Machine (MPM) for the prediction of seismic liquefaction potential of soil based on the Cone Penetration Test (CPT) data. The dataset has been taken from Chi–Chi earthquake. MPM is developed based on the use of hyperplanes. It has been adopted as a classification tool. This article uses two models (MODEL I and MODEL II). MODEL I employs Cone Resistance (q(c)) and Cyclic Stress Ratio (CSR) as input variables. q(c) and Peak Ground Acceleration (PGA) have been taken as inputs for MODEL II. The developed MPM gives 100% accuracy. The results show that the developed MPM can predict liquefaction potential of soil based on q(c) and PGA. |
format | Online Article Text |
id | pubmed-4506965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-45069652015-07-21 A unified classification model for modeling of seismic liquefaction potential of soil based on CPT Samui, Pijush Hariharan, R. J Adv Res Original Article The evaluation of liquefaction potential of soil due to an earthquake is an important step in geosciences. This article examines the capability of Minimax Probability Machine (MPM) for the prediction of seismic liquefaction potential of soil based on the Cone Penetration Test (CPT) data. The dataset has been taken from Chi–Chi earthquake. MPM is developed based on the use of hyperplanes. It has been adopted as a classification tool. This article uses two models (MODEL I and MODEL II). MODEL I employs Cone Resistance (q(c)) and Cyclic Stress Ratio (CSR) as input variables. q(c) and Peak Ground Acceleration (PGA) have been taken as inputs for MODEL II. The developed MPM gives 100% accuracy. The results show that the developed MPM can predict liquefaction potential of soil based on q(c) and PGA. Elsevier 2015-07 2014-02-14 /pmc/articles/PMC4506965/ /pubmed/26199749 http://dx.doi.org/10.1016/j.jare.2014.02.002 Text en © 2014 Production and hosting by Elsevier B.V. on behalf of Cairo University. http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). |
spellingShingle | Original Article Samui, Pijush Hariharan, R. A unified classification model for modeling of seismic liquefaction potential of soil based on CPT |
title | A unified classification model for modeling of seismic liquefaction potential of soil based on CPT |
title_full | A unified classification model for modeling of seismic liquefaction potential of soil based on CPT |
title_fullStr | A unified classification model for modeling of seismic liquefaction potential of soil based on CPT |
title_full_unstemmed | A unified classification model for modeling of seismic liquefaction potential of soil based on CPT |
title_short | A unified classification model for modeling of seismic liquefaction potential of soil based on CPT |
title_sort | unified classification model for modeling of seismic liquefaction potential of soil based on cpt |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4506965/ https://www.ncbi.nlm.nih.gov/pubmed/26199749 http://dx.doi.org/10.1016/j.jare.2014.02.002 |
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