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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:0960-3182
1573-1529
DOI:10.1007/s10706-023-02669-1