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Developing Vs-NSPT Prediction Models Using Bayesian Framework

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Abstract

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.

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Data Availability

The data used in the paper are available upon request from the corresponding author.

Abbreviations

Vs:

Shear wave velocity

Vs30 :

Shear wave velocity in the top 30 m

SPT:

Standard penetration test

NSPT, N:

SPT blow

z :

Depth

st:

Type of soil

a and b :

Site-dependent coefficients

R 2 :

R-squared is the coefficient of determination

NPHI:

Neutron porosity

RHOB:

Density

GR:

Gamma

MLP:

Multi-layer perceptron

ELM:

Extreme learning machine

MASW:

Multichannel analysis of surface wave

SCPTs:

Seismic cone penetration tests

ASTM:

American Society for Testing and Materials

HBM:

Hierarchical Bayesian model

ε:

Model error

σε :

Standard deviation

μa :

Mean and standard deviation

R :

Number of regions in the observed data points

θ :

Set of the random variables

N :

Normal-gamma distribution

IG:

Inverse-gamma distribution

MCMC:

Markov chain Monte Carlo

PBM:

Pooled Bayesian model

RMSD:

Root mean squared deviation

yi :

True data value

ŷi :

Predicted value

\(\mathrm{ln}\) v s :

Specific model predicts

U :

Undisturbed sample

D :

Disturbed sample

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Authors and Affiliations

  1. Department of Civil Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq

    Duaa Al-Jeznawi & Musab A. Q. Al-Janabi

  2. Department of Civil and Architectural Engineering and Construction Management, University of Cincinnati, Cincinnati, USA

    Laith Sadik

  3. Department of Roads and Transport Engineering, University of Al-Qadisiyah, Al-Qadisiyah, Iraq

    Saif Alzabeebee

  4. Department of Mechanical, Environmental, and Civil Engineering, Tarleton State University |Rellis| The Texas A & M University System, College Station, Stephenville, TX, USA

    Jumanah Hajjat

  5. Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand

    Suraparb Keawsawasvong

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  1. Duaa Al-Jeznawi
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Contributions

DA-J: data collection; methodology; writing original draft.

LS: data collection; methodology; writing original draft.

MAQA-J: data collection; methodology; revising the draft.

SA: data collection; methodology; revising the draft.

JH: data collection; methodology; revising the draft.

SK: data collection; methodology; revising the draft.

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Correspondence to Duaa Al-Jeznawi.

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Al-Jeznawi, D., Sadik, L., Al-Janabi, M.A.Q. et al. Developing Vs-NSPT Prediction Models Using Bayesian Framework. Transp. Infrastruct. Geotech. (2023). https://doi.org/10.1007/s40515-023-00353-8

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