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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmad N, Ali Q, Crowley H, Pinho R (2014) Earthquake loss estimation of residential buildings in pakistan. Nat Hazards 73(3):1889–1955
Askan A (2002) Stochastic methods for the estimation of potential seismic damage. Thesis, Middle East Technical University, Ankara, M.Sc
Askan A, Yucemen MS (2010) Probabilistic methods for the estimation of potential seismic damage: application to reinforced concrete buildings in turkey. Struct Saf 32(4):262–271
Askan A, Sisman FN, Ugurhan B (2013) Stochastic strong ground motion simulations in sparsely-monitored regions: a validation and sensitivity study on the 13 March 1992 erzincan (Turkey) earthquake. Soil Dyn Earthq Eng 55:170–181
Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth International Group, Belmont
Grünthal G, Thieken A, Schwarz J, Radtke K, Smolka A, Merz B (2006) Comparative risk assessments for the city of cologne-storms, floods, earthquakes. Nat Hazards 38(1–2):21–44
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer, Berlin
Holzer TL, Barka AA, Carver D, Çelebi M, Cranswick E, Dawson T, Dieterich JH, Ellsworth WL, Fumal T, Gross JL, Langridge R, Lettis WR, Meremonte M, Mueller C, Olsen RS, Ozel O, Parsons T, Phan LT, Rockwell T, Safak E, Stein RS, Stenner H, Toda S, Toprak S (2000) Implications for earthquake risk reduction in the united states from the kocaeli, turkey earthquake of august 17, 1999. United States Geological Survey (USGS) Circular 1193:64
James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning: with applications in R. Springer, Berlin
Karimzadeh S, Askan A, Erberik MA, Yakut A (2018) Seismic damage assessment based on regional synthetic ground motion dataset: a case study for Erzincan, Turkey. Nat Hazards 92(3):1371–1397
Lang DH, Molina-Palacios S, Lindholm CD (2008) Towards near-real-time damage estimation using a CSM-based tool for seismic risk assessment. J Earthq Eng 12(S2):199–210
Malinowska A (2014) Classification and regression tree theory application for assessment of building damage caused by surface deformation. Nat Hazards 73(2):317–334
Therneau T, Atkinson B, Ripley B (2018) rpart: Recursive partitioning and regression trees. r package version 4.1-13. https://CRAN.R-project.org/package=rpart
Ünal B, Askan A, Selcuk-Kestel AS (2017) Simulation of large earthquakes and its implications on earthquake insurance rates: a case study in Bursa region (Turkey). Nat Hazards 85(1):215–236
Whitman RV, Anagnos T, Kircher CA, Lagorio HJ, Lawson RS, Schneider P (1997) Development of a national earthquake loss estimation methodology. Earthq Spectra 13(4):643–661
Zobin VM, Ventura-Ramírez JF, Gutiérrez-Andrade CL, Cruz LH, Santibáñez-Ibáñez S, (2006) The mw 7.4 Colima, Mexico, earthquake of 21 January, (2003) the observed damage matrix in colima city and its comparison with the damage probability matrix. Nat Hazards 38(3):391–410
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yerlikaya-Özkurt, F., Askan, A. Prediction of potential seismic damage using classification and regression trees: a case study on earthquake damage databases from Turkey. Nat Hazards 103, 3163–3180 (2020). https://doi.org/10.1007/s11069-020-04125-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-020-04125-2