IIUM Repository

Prediction of liquefaction-induced lateral displacements using Gaussian process regression

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

[img] PDF - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
[img] PDF (SCOPUS) - Supplemental Material
Restricted to Registered users only

Download (588kB) | Request a copy

Abstract

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.

Item Type: Article (Journal)
Uncontrolled Keywords: Keywords: lateral displacement; liquefaction; Gaussian process regression; sensitivity analysis; machine learning
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA401 Materials of engineering and construction
T Technology > TA Engineering (General). Civil engineering (General) > TA705 Engineering geology. Rock mechanics. Soil mechanics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Civil Engineering
Depositing User: Dr. Ramez Al-Mansob
Date Deposited: 18 Feb 2022 16:46
Last Modified: 03 Mar 2022 08:26
URI: http://irep.iium.edu.my/id/eprint/96808

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year