Evaluation of liquefaction potential based on CPT data using random forest

V. Kohestani (), M. Hassanlourad () and A. Ardakani ()

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2015, vol. 79, issue 2, 1079-1089

Abstract: The prediction of liquefaction potential of soil due to an earthquake is an essential task in civil engineering. In this paper, random forest (RF) method is introduced and investigated for the prediction of seismic liquefaction potential of soil based on the cone penetration test data. RF has been proposed on the basis of classification and regression trees with “ensemble learning” strategy. The RF models were developed and validated on a relatively large dataset comprising 226 field records of liquefaction performance and cone penetration test measurements. The database contains the information about depth of potentially liquefiable soil layer (D), cone tip resistance ( $$q_{\text{c}}$$ q c ), sleeve friction ratio ( $$R_{\text{f}}$$ R f ), effective vertical stress ( $$\sigma_{0}^{\prime }$$ σ 0 ′ ), total vertical stress ( $$\sigma_{0}$$ σ 0 ), maximum horizontal ground surface acceleration ( $$\alpha_{\hbox{max} }$$ α max ) and earthquake magnitude ( $$M_{\text{w}}$$ M w ). Two RF models (Model I and Model II) are developed for predicting the occurrence and non-occurrence of liquefaction on the basis of combination of above input parameters. The results of RF models have been compared with the available artificial neural network (ANN) and support vector machine (SVM) models. It is shown that the proposed RF models provide more accurate results than the ANN and SVM models proposed in the literature. The developed RF provides a viable tool for civil engineers to determine the liquefaction potential of soil. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: Liquefaction; Cone penetration test; Artificial intelligence; Random forest (RF) (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1007/s11069-015-1893-5 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:79:y:2015:i:2:p:1079-1089

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-015-1893-5

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().