NDLI: Prediction of lateral spread displacement: data-driven approaches

Content Provider	Springer Nature Link
Author	Liu, Zheng Tesfamariam, Solomon
Copyright Year	2012
Abstract	Site seismic hazard (SSH) is an integral component of seismic risk assessment of engineered structures. The SSH encompasses the effect of ground shaking, landslide, and liquefaction. Discernment of liquefaction and lateral spreading vulnerability is a complex and nonlinear procedure that is influenced by model and parameter uncertainty. In this study, nine different data-driven models were investigated to predict the lateral spread displacement over a free-face and ground-slope conditions. These models include: multivariate adaptive regression splines, generalized additive model, neural networks, generalized linear model, robust regression, regression tree, support vector machine, projection pursuit, and random forest. The results demonstrate efficacy of the proposed models for lateral spreading estimation and in general, the random forest showed a better prediction. Sensitivity analysis is also performed to identify parameters that contribute to the model variability.
Starting Page	1431
Ending Page	1454
Page Count	24
File Format	PDF
ISSN	1570761X
Journal	Bulletin of Earthquake Engineering
Volume Number	10
Issue Number	5
e-ISSN	15731456
Language	English
Publisher	Springer Netherlands
Publisher Date	2012-07-24
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Lateral spread displacements Data-driven model Non-parametric regression Machine learning Geophysics/Geodesy Structural Geology Hydrogeology Civil Engineering Geotechnical Engineering & Applied Earth Sciences Environmental Engineering/Biotechnology
Content Type	Text
Resource Type	Article
Subject	Building and Construction Geophysics Geotechnical Engineering and Engineering Geology Civil and Structural Engineering

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