Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression

Mahmood Ahmad,Arnold Alguno,Piotr Olczak,Paweł Kamiński,Ramez Al-Mansob,Maaz Amjad,Beenish Khan

doi:10.3390/app12041977

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.

Highlights

Loss of life and property remains an unavoidable consequence of major earthquakes.Studies of the consequences of major earthquakes have attempted to analyze the damage and make recommendations for reducing loss in the event of future earthquakes throughout history [1,2,3]
Various approaches have been presented to estimate the magnitude of lateral displacement to date, and from the technical perspective, they can be classified as: (1) numerical analysis based on finite element or finite difference approaches (e.g., Finn et al [5], Liao et al [6] and Arulanandan et al [7], (2) simplified analytical methods, e.g., Newmark [8], Towhata et al [9], and Kokusho and Fujita [10], (3) empirical methods based on either laboratory testing set or analytical methods of lateral spreading case history records (e.g., Hamada et al [11] and Youd et al [12]) and (4) machine learning approaches (e.g., Wang and Rahman [13])
The trend line for Gaussian process regression (GPR) in training and testing phases has been drawn by comparing the observed regression in Figure 2 scatter plot, and the GPR

Summary

Introduction

Loss of life and property remains an unavoidable consequence of major earthquakes.Studies of the consequences of major earthquakes have attempted to analyze the damage and make recommendations for reducing loss in the event of future earthquakes throughout history [1,2,3]. Various approaches have been presented to estimate the magnitude of lateral displacement to date, and from the technical perspective, they can be classified as: (1) numerical analysis based on finite element or finite difference approaches (e.g., Finn et al [5], Liao et al [6] and Arulanandan et al [7], (2) simplified analytical methods, e.g., Newmark [8], Towhata et al [9], and Kokusho and Fujita [10], (3) empirical methods based on either laboratory testing set or analytical methods of lateral spreading case history records (e.g., Hamada et al [11] and Youd et al [12]) and (4) machine learning approaches (e.g., Wang and Rahman [13]). These different approaches are reviewed with particular emphasis on empirical models and soft computing techniques

Methods

Findings

Discussion

Conclusion