Prediction of potential seismic damage using classification and regression trees: a case study on earthquake damage databases from Turkey

Fatma Yerlikaya-Özkurt () and Aysegul Askan ()
Additional contact information
Fatma Yerlikaya-Özkurt: Atılım University
Aysegul Askan: Middle East Technical University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2020, vol. 103, issue 3, No 25, 3163-3180

Abstract: Abstract Seismic damage estimation is an important key ingredient of seismic loss modeling, risk mitigation and disaster management. It is a problem involving inherent uncertainties and complexities. Thus, it is important to employ robust approaches which will handle the problem accurately. In this study, classification and regression tree approach is applied on damage data sets collected from reinforced concrete frame buildings after major previous earthquakes in Turkey. Four damage states ranging from None to Severe are used, while five structural parameters are employed as damage identifiers. For validation, results of classification analyses are compared against observed damage states. Results in terms of well-known classification performance measures indicate that when the size of the database is larger, the correct classification rates are higher. Performance measures computed for Test data set indicate similar success to that of Train data set. The approach is found to be effective in classifying randomly selected damage data.

Keywords: Earthquakes; Seismic damage; Classification and regression tree; Damage prediction (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-020-04125-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:103:y:2020:i:3:d:10.1007_s11069-020-04125-2

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

DOI: 10.1007/s11069-020-04125-2

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 ().