Proceedings of the Rocscience International Conference (RIC 2023)

Developing SPT-CPT Correlation Models Using Hierarchical Bayesian Approach

Authors
Sara Khoshnevisan1, *, Laith Sadik1
1University of Cincinnati, Cincinnati, OH, USA
*Corresponding author. Email: sara.khoshnevisan@uc.edu
Corresponding Author
Sara Khoshnevisan
Available Online 8 November 2023.
DOI
10.2991/978-94-6463-258-3_61How to use a DOI?
Keywords
Hierarchical Bayesian Modeling; Standard Penetration Test; Cone Penetration Test
Abstract

In geotechnical practice, engineers often perform only one type of in-situ testing. However, under some circumstances, there might be a need for different testing for additional analysis. Having a correlation model in such cases eliminates the need for performing additional testing and thus, saving time and the associated costs. Over years, many researchers have developed models between different in-situ testing methods, most of which are based on regression analysis using data from different regions. However, these developed models do not account for the potential spatial variability between the regions data is taken from. Moreover, the applicability of these models is questionable in a new region; especially where no data or limited data is available. In addition, most of the developed models do not account for uncertainties. In this paper, the Hierarchical Bayesian Modeling approach is adopted to develop region-specific correlation models between two popular in-situ testing methods: The Standard Penetration Test and Cone Penetration Test. 220 high-quality data pairs of N1,60 cs and qt1N,cs from six regions in Taiwan are used for illustration purposes. The developed model is validated using a new region that was not used for model development. Two N1,60 cs - qt1N,cs existing correlation models are also adopted for comparison purposes. The models developed using the Hierarchical Bayesian Modeling approach are shown to perform better compared to these existing correlation models. In addition, the hierarchical proposed approach shows a strong prediction capability and high reliability in regions with limited or no data. With the proposed approach, region-specific N1,60 cs - qt1N,cs correlation models can be developed for regions with limited or limited data.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Rocscience International Conference (RIC 2023)
Series
Atlantis Highlights in Engineering
Publication Date
8 November 2023
ISBN
978-94-6463-258-3
ISSN
2589-4943
DOI
10.2991/978-94-6463-258-3_61How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Sara Khoshnevisan
AU  - Laith Sadik
PY  - 2023
DA  - 2023/11/08
TI  - Developing SPT-CPT Correlation Models Using Hierarchical Bayesian Approach
BT  - Proceedings of the Rocscience International Conference  (RIC 2023)
PB  - Atlantis Press
SP  - 655
EP  - 665
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-258-3_61
DO  - 10.2991/978-94-6463-258-3_61
ID  - Khoshnevisan2023
ER  -