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Prediction of liquefaction-induced lateral displacements using Gaussian process regression

Abstract: During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-term development. This research pro...

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Main Authors: Ahmad, Mahmood, Amjad, Maaz, Al-Mansob, Ramez, Kami ´nski, Paweł, Olczak, Piotr, Khan, Beenish Jehan, Alguno, Arnold C., ,
Format: Article
Language:English
English
Published: MDPI 2022
Subjects:
TA401 Materials of engineering and construction
TA705 Engineering geology. Rock mechanics. Soil mechanics
Online Access:http://irep.iium.edu.my/96808/7/96808_Prediction%20of%20liquefaction-induced%20lateral%20displacements.pdf
http://irep.iium.edu.my/96808/13/96808_%20Prediction%20of%20Liquefaction_Scopus.pdf
http://irep.iium.edu.my/96808/
https://doi.org/10.3390/app12041977
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spelling my.iium.irep.968082022-03-03T00:26:59Z http://irep.iium.edu.my/96808/ Prediction of liquefaction-induced lateral displacements using Gaussian process regression Ahmad, Mahmood Amjad, Maaz Al-Mansob, Ramez Kami ´nski, Paweł Olczak, Piotr Khan, Beenish Jehan Alguno, Arnold C. , TA401 Materials of engineering and construction TA705 Engineering geology. Rock mechanics. Soil mechanics Abstract: During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-term development. This research proposes a Gaussian Process Regression (GPR) model based on 247 post liquefaction in-situ free face ground conditions case studies for analyzing liquefaction-induced lateral displacement. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination, coefficient of correlation, Nash–Sutcliffe efficiency coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data. The developed GPR model predictive ability is compared to that of three other known models—evolutionary polynomial regression, artificial neural network, and multi-layer regression available in the literature. The results show that the GPR model can accurately learn complicated nonlinear relationships between lateral displacement and its influencing factors. A sensitivity analysis is also presented in this study to assess the effects of input parameters on lateral displacement. MDPI 2022-02-14 Article PeerReviewed application/pdf en http://irep.iium.edu.my/96808/7/96808_Prediction%20of%20liquefaction-induced%20lateral%20displacements.pdf application/pdf en http://irep.iium.edu.my/96808/13/96808_%20Prediction%20of%20Liquefaction_Scopus.pdf Ahmad, Mahmood and Amjad, Maaz and Al-Mansob, Ramez and Kami ´nski, Paweł and Olczak, Piotr and Khan, Beenish Jehan and Alguno, Arnold C. and UNSPECIFIED (2022) Prediction of liquefaction-induced lateral displacements using Gaussian process regression. Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression. pp. 1-17. E-ISSN 2076-3417 https://doi.org/10.3390/app12041977 10.3390/app12041977
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TA401 Materials of engineering and construction
TA705 Engineering geology. Rock mechanics. Soil mechanics
spellingShingle TA401 Materials of engineering and construction
TA705 Engineering geology. Rock mechanics. Soil mechanics
Ahmad, Mahmood
Amjad, Maaz
Al-Mansob, Ramez
Kami ´nski, Paweł
Olczak, Piotr
Khan, Beenish Jehan
Alguno, Arnold C.
,
Prediction of liquefaction-induced lateral displacements using Gaussian process regression
description Abstract: During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-term development. This research proposes a Gaussian Process Regression (GPR) model based on 247 post liquefaction in-situ free face ground conditions case studies for analyzing liquefaction-induced lateral displacement. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination, coefficient of correlation, Nash–Sutcliffe efficiency coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data. The developed GPR model predictive ability is compared to that of three other known models—evolutionary polynomial regression, artificial neural network, and multi-layer regression available in the literature. The results show that the GPR model can accurately learn complicated nonlinear relationships between lateral displacement and its influencing factors. A sensitivity analysis is also presented in this study to assess the effects of input parameters on lateral displacement.
format Article
author Ahmad, Mahmood
Amjad, Maaz
Al-Mansob, Ramez
Kami ´nski, Paweł
Olczak, Piotr
Khan, Beenish Jehan
Alguno, Arnold C.
,
author_facet Ahmad, Mahmood
Amjad, Maaz
Al-Mansob, Ramez
Kami ´nski, Paweł
Olczak, Piotr
Khan, Beenish Jehan
Alguno, Arnold C.
,
author_sort Ahmad, Mahmood
title Prediction of liquefaction-induced lateral displacements using Gaussian process regression
title_short Prediction of liquefaction-induced lateral displacements using Gaussian process regression
title_full Prediction of liquefaction-induced lateral displacements using Gaussian process regression
title_fullStr Prediction of liquefaction-induced lateral displacements using Gaussian process regression
title_full_unstemmed Prediction of liquefaction-induced lateral displacements using Gaussian process regression
title_sort prediction of liquefaction-induced lateral displacements using gaussian process regression
publisher MDPI
publishDate 2022
url http://irep.iium.edu.my/96808/7/96808_Prediction%20of%20liquefaction-induced%20lateral%20displacements.pdf
http://irep.iium.edu.my/96808/13/96808_%20Prediction%20of%20Liquefaction_Scopus.pdf
http://irep.iium.edu.my/96808/
https://doi.org/10.3390/app12041977
_version_ 1726791269281169408
score 13.211869

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