Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network

This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships betw...

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Autores principales:	Mahmood Ahmad, Feezan Ahmad, Jiandong Huang, Muhammad Junaid Iqbal, Muhammad Safdar, Nima Pirhadi
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	bayesian belief network cone penetration test liquefaction potential interpretive structural modeling sensitivity analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a0a0673c5c944e6aa4776a55b92d6bfa
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spelling	oai:doaj.org-article:a0a0673c5c944e6aa4776a55b92d6bfa2021-11-29T05:52:53ZProbabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network10.3934/mbe.20214541551-0018https://doaj.org/article/a0a0673c5c944e6aa4776a55b92d6bfa2021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021454?viewType=HTMLhttps://doaj.org/toc/1551-0018This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.Mahmood AhmadFeezan AhmadJiandong HuangMuhammad Junaid IqbalMuhammad Safdar Nima PirhadiAIMS Pressarticlebayesian belief networkcone penetration testliquefaction potentialinterpretive structural modelingsensitivity analysisBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9233-9252 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bayesian belief network cone penetration test liquefaction potential interpretive structural modeling sensitivity analysis Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	bayesian belief network cone penetration test liquefaction potential interpretive structural modeling sensitivity analysis Biotechnology TP248.13-248.65 Mathematics QA1-939 Mahmood Ahmad Feezan Ahmad Jiandong Huang Muhammad Junaid Iqbal Muhammad Safdar Nima Pirhadi Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network
description	This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict cone penetration test (CPT)-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naive Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.
format	article
author	Mahmood Ahmad Feezan Ahmad Jiandong Huang Muhammad Junaid Iqbal Muhammad Safdar Nima Pirhadi
author_facet	Mahmood Ahmad Feezan Ahmad Jiandong Huang Muhammad Junaid Iqbal Muhammad Safdar Nima Pirhadi
author_sort	Mahmood Ahmad
title	Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network
title_short	Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network
title_full	Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network
title_fullStr	Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network
title_full_unstemmed	Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network
title_sort	probabilistic evaluation of cpt-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/a0a0673c5c944e6aa4776a55b92d6bfa
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Probabilistic evaluation of CPT-based seismic soil liquefaction potential: towards the integration of interpretive structural modeling and bayesian belief network

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