Human-Centric Intelligent Systems

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Volume 1, Issue 3-4, December 2021, Pages 98 - 104

Application of Logistic Regression Based on Maximum Likelihood Estimation to Predict Seismic Soil Liquefaction Occurrence

Authors
Idriss Jairi1, Yu Fang1, 2, *, Nima Pirhadi3
1School of Computer Science, Southwest Petroleum University, Chengdu, China
2School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
3School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu, China
*Corresponding author. Email: fangyu@swpu.edu.cn
Corresponding Author
Yu Fang
Received 13 September 2021, Accepted 5 December 2021, Available Online 11 December 2021.
DOI
10.2991/hcis.k.211207.001How to use a DOI?
Keywords
Probability of liquefaction; logistic regression; classification; maximum likelihood estimation; cone penetration test
Abstract

Seismic soil liquefaction is one of the considerable challenges and disastrous sides of earthquakes that can generally happen in loose to medium saturated sandy soils. The in-situ cone penetration test (CPT) is a widely used index for evaluating the liquefaction characteristics of soils from different sites all over the world. To deal with the uncertainties of the models and the parameters on evaluating the liquefaction, a mathematical probabilistic model is applied via logistic regression, and the comprehensive CPT results are used to develop a model to predict the probability of liquefaction (PL). The new equation to assess the liquefaction occurrence is based on two important features from the expanded CPT dataset. The maximum likelihood estimation (MLE) method is applied to compute the model parameters by maximizing a likelihood function. In addition to that, the sampling bias is applied in the likelihood function via using the weighting factors. Five curve classifiers are plotted for different PL values and ranked using two evaluation metrics. Then, based on these metrics the optimal curve is selected and compared to a well-known deterministic model to validate it. This study also highlights the importance of the recall evaluation metric in the liquefaction occurrence evaluation. The experiment results indicate that the proposed method is outperform existing methods and presents the state-of-the-art in terms of probabilistic models.

Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Issue
Journal
Human-Centric Intelligent Systems
Volume-Issue
1 - 3-4
Pages
98 - 104
Publication Date
2021/12/11
ISSN (Online)
2667-1336
DOI
10.2991/hcis.k.211207.001How to use a DOI?
Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Idriss Jairi
AU  - Yu Fang
AU  - Nima Pirhadi
PY  - 2021
DA  - 2021/12/11
TI  - Application of Logistic Regression Based on Maximum Likelihood Estimation to Predict Seismic Soil Liquefaction Occurrence
JO  - Human-Centric Intelligent Systems
SP  - 98
EP  - 104
VL  - 1
IS  - 3-4
SN  - 2667-1336
UR  - https://doi.org/10.2991/hcis.k.211207.001
DO  - 10.2991/hcis.k.211207.001
ID  - Jairi2021
ER  -