Soil Liquefaction Assessment by CPT and VS Data and Incomplete-Fuzzy C-Means Clustering

Assessing soil liquefaction potential is a crucial consideration in the seismic design of structures and their seismic stability. The complex nonlinear behavior of the liquefiable soils and the non-deterministic nature of earthquakes make the liquefaction analysis vague. Accordingly, researchers hav...

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Vydané v:	Geotechnical and geological engineering Ročník 42; číslo 3; s. 2205 - 2220
Hlavní autori:	Mohammadikish, Saeideh, Ashayeri, Iman, Biglari, Mahnoosh, Yarmohamadi, Amir
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.05.2024 Springer Nature B.V
Predmet:	Algorithms Civil Engineering Clustering Cone penetration tests Earth and Environmental Science Earth Sciences Earthquakes Geotechnical Engineering & Applied Earth Sciences Hydrogeology Liquefaction Machine learning Original Paper S waves Seismic activity Seismic design Seismic stability Seismic velocities Shear wave velocities Soil Soils Terrestrial Pollution Waste Management/Waste Technology Wave velocity Incomplete data ) Cone penetration test (CPT) Fuzzy c-means clustering Liquefaction Shear wave velocity test (V
ISSN:	0960-3182, 1573-1529
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Abstract	Assessing soil liquefaction potential is a crucial consideration in the seismic design of structures and their seismic stability. The complex nonlinear behavior of the liquefiable soils and the non-deterministic nature of earthquakes make the liquefaction analysis vague. Accordingly, researchers have progressively focused on employing machine learning and mathematical algorithms to address the complexities and uncertainties of evaluating soil liquefaction potential. This paper investigates the performance of fuzzy c-means clustering of incomplete data for assessing liquefaction potential based on cone penetration test (CPT) and shear wave velocity (V s ) field data. The research was conducted using two approaches: (1) whole data strategy; (2) partial distance strategy. The used database contains 786 CPT and 846 V s records, with specified liquefaction conditions in past earthquake events. We compared the effectiveness and success of this method with traditional deterministic and probabilistic liquefaction evaluation approaches. It was found that the fuzzy c-means clustering model had a comparable predictive ability with other methods and would be reliable when assessing the liquefaction possibility.
AbstractList	Assessing soil liquefaction potential is a crucial consideration in the seismic design of structures and their seismic stability. The complex nonlinear behavior of the liquefiable soils and the non-deterministic nature of earthquakes make the liquefaction analysis vague. Accordingly, researchers have progressively focused on employing machine learning and mathematical algorithms to address the complexities and uncertainties of evaluating soil liquefaction potential. This paper investigates the performance of fuzzy c-means clustering of incomplete data for assessing liquefaction potential based on cone penetration test (CPT) and shear wave velocity (V s ) field data. The research was conducted using two approaches: (1) whole data strategy; (2) partial distance strategy. The used database contains 786 CPT and 846 V s records, with specified liquefaction conditions in past earthquake events. We compared the effectiveness and success of this method with traditional deterministic and probabilistic liquefaction evaluation approaches. It was found that the fuzzy c-means clustering model had a comparable predictive ability with other methods and would be reliable when assessing the liquefaction possibility. Assessing soil liquefaction potential is a crucial consideration in the seismic design of structures and their seismic stability. The complex nonlinear behavior of the liquefiable soils and the non-deterministic nature of earthquakes make the liquefaction analysis vague. Accordingly, researchers have progressively focused on employing machine learning and mathematical algorithms to address the complexities and uncertainties of evaluating soil liquefaction potential. This paper investigates the performance of fuzzy c-means clustering of incomplete data for assessing liquefaction potential based on cone penetration test (CPT) and shear wave velocity (Vs) field data. The research was conducted using two approaches: (1) whole data strategy; (2) partial distance strategy. The used database contains 786 CPT and 846 Vs records, with specified liquefaction conditions in past earthquake events. We compared the effectiveness and success of this method with traditional deterministic and probabilistic liquefaction evaluation approaches. It was found that the fuzzy c-means clustering model had a comparable predictive ability with other methods and would be reliable when assessing the liquefaction possibility.
Author	Biglari, Mahnoosh Mohammadikish, Saeideh Ashayeri, Iman Yarmohamadi, Amir
Author_xml	– sequence: 1 givenname: Saeideh orcidid: 0000-0003-2859-6929 surname: Mohammadikish fullname: Mohammadikish, Saeideh email: mohammadikishs@yahoo.com organization: Civil Engineering Department, Faculty of Engineering, Razi University – sequence: 2 givenname: Iman surname: Ashayeri fullname: Ashayeri, Iman organization: Civil Engineering Department, Faculty of Engineering, Razi University – sequence: 3 givenname: Mahnoosh surname: Biglari fullname: Biglari, Mahnoosh organization: Civil Engineering Department, Faculty of Engineering, Razi University – sequence: 4 givenname: Amir surname: Yarmohamadi fullname: Yarmohamadi, Amir organization: International Institute of Earthquake Engineering and Seismology
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Keywords	Incomplete data ) Cone penetration test (CPT) Fuzzy c-means clustering Liquefaction Shear wave velocity test (V
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SubjectTerms	Algorithms Civil Engineering Clustering Cone penetration tests Earth and Environmental Science Earth Sciences Earthquakes Geotechnical Engineering & Applied Earth Sciences Hydrogeology Liquefaction Machine learning Original Paper S waves Seismic activity Seismic design Seismic stability Seismic velocities Shear wave velocities Soil Soils Terrestrial Pollution Waste Management/Waste Technology Wave velocity
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Title	Soil Liquefaction Assessment by CPT and VS Data and Incomplete-Fuzzy C-Means Clustering
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