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A platform for research: civil engineering, architecture and urbanism
Developing Vs-NSPT Prediction Models Using Bayesian Framework
https://orcid.org/http://orcid.org/0000-0002-6134-4638
https://orcid.org/http://orcid.org/0000-0001-9863-1801
https://orcid.org/http://orcid.org/0000-0001-9685-5641
https://orcid.org/http://orcid.org/0009-0002-1848-5109
https://orcid.org/http://orcid.org/0000-0002-1760-9838
In earthquake engineering, shear wave velocity (Vs) is an effective parameter for quantifying the ground’s effects due to shaking. The determination of Vs is usually done by costly and time-consuming geophysical testing; accordingly, previous research endeavors focused on developing empirical relationships between Vs. and other soil geotechnical properties like Standard Penetration Test (SPT) blow count (NSPT), depth, and vertical effective stress. However, previous models might be biased for the data from regions of these models, and most of them do not account for uncertainty. Consequently, this research aims to develop a reliable Vs-NSPT correlation relationship using the Bayesian hierarchical model approach. For that reason, a comprehensive dataset of 321 Vs-NSPT data pairs was compiled from different locations to develop a region-specific correlation model; after that, the models were validated using a different dataset of 174 data pairs from the literature. It was concluded that the developed models are less biased toward outliers in the data across different regions, relatively more accurate, and explicitly quantify uncertainty in the developed relationships, providing a more reliable approach for Vs-NSPT correlation.
Developing Vs-NSPT Prediction Models Using Bayesian Framework
https://orcid.org/http://orcid.org/0000-0002-6134-4638
https://orcid.org/http://orcid.org/0000-0001-9863-1801
https://orcid.org/http://orcid.org/0000-0001-9685-5641
https://orcid.org/http://orcid.org/0009-0002-1848-5109
https://orcid.org/http://orcid.org/0000-0002-1760-9838
In earthquake engineering, shear wave velocity (Vs) is an effective parameter for quantifying the ground’s effects due to shaking. The determination of Vs is usually done by costly and time-consuming geophysical testing; accordingly, previous research endeavors focused on developing empirical relationships between Vs. and other soil geotechnical properties like Standard Penetration Test (SPT) blow count (NSPT), depth, and vertical effective stress. However, previous models might be biased for the data from regions of these models, and most of them do not account for uncertainty. Consequently, this research aims to develop a reliable Vs-NSPT correlation relationship using the Bayesian hierarchical model approach. For that reason, a comprehensive dataset of 321 Vs-NSPT data pairs was compiled from different locations to develop a region-specific correlation model; after that, the models were validated using a different dataset of 174 data pairs from the literature. It was concluded that the developed models are less biased toward outliers in the data across different regions, relatively more accurate, and explicitly quantify uncertainty in the developed relationships, providing a more reliable approach for Vs-NSPT correlation.
Developing Vs-NSPT Prediction Models Using Bayesian Framework
https://orcid.org/http://orcid.org/0000-0002-6134-4638
https://orcid.org/http://orcid.org/0000-0001-9863-1801
https://orcid.org/http://orcid.org/0000-0001-9685-5641
https://orcid.org/http://orcid.org/0009-0002-1848-5109
https://orcid.org/http://orcid.org/0000-0002-1760-9838
In earthquake engineering, shear wave velocity (Vs) is an effective parameter for quantifying the ground’s effects due to shaking. The determination of Vs is usually done by costly and time-consuming geophysical testing; accordingly, previous research endeavors focused on developing empirical relationships between Vs. and other soil geotechnical properties like Standard Penetration Test (SPT) blow count (NSPT), depth, and vertical effective stress. However, previous models might be biased for the data from regions of these models, and most of them do not account for uncertainty. Consequently, this research aims to develop a reliable Vs-NSPT correlation relationship using the Bayesian hierarchical model approach. For that reason, a comprehensive dataset of 321 Vs-NSPT data pairs was compiled from different locations to develop a region-specific correlation model; after that, the models were validated using a different dataset of 174 data pairs from the literature. It was concluded that the developed models are less biased toward outliers in the data across different regions, relatively more accurate, and explicitly quantify uncertainty in the developed relationships, providing a more reliable approach for Vs-NSPT correlation.
Developing Vs-NSPT Prediction Models Using Bayesian Framework
Transp. Infrastruct. Geotech.
Al-Jeznawi, Duaa (author) / Sadik, Laith (author) / Al-Janabi, Musab A. Q. (author) / Alzabeebee, Saif (author) / Hajjat, Jumanah (author) / Keawsawasvong, Suraparb (author)
Transportation Infrastructure Geotechnology ; 11 ; 1777-1798
2024-08-01
22 pages
Article (Journal)
Electronic Resource
English
Developing Vs-NSPT Prediction Models Using Bayesian Framework
| Springer Verlag | 2024
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