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A decision tree-based damage estimation approach for preliminary seismic assessment of reinforced concrete buildings

Authors

Ayşe Elif Özsoy Özbay Civil Engineering Department, Maltepe University, İstanbul (Türkiye)

DOI:

https://doi.org/10.7764/RDLC.22.1.5

Keywords:

Seismic vulnerability assessment, Decision trees, Reinforced concrete buildings, Earthquake risk estimation, Seismic risk prioritization

Abstract

This study aims to introduce an earthquake-induced damage classification approach for seismic vulnerability assessment of reinforced concrete buildings. Through the use of the damage data collected from post-earthquake inspections after the 2003 Bingöl Earthquake in Turkey, two models were constructed by the decision tree classification technique considering nine building-specific features as the estimation variables in the analysis. The first model was developed for the prediction of the observed damage states of the buildings, whereas the second one concerning the life safety level assessment, was proposed for distinguishing the extremely vulnerable buildings for seismic prioritization. In the validation process, the leave-one-out cross validation technique was adopted to deal with the small sample size of the building inventory. Among the estimation variables, the priority index and the existence of short columns were found to have the highest importance in classification. Results have revealed that the proposed model for life safety level assessment was capable of discriminating the cluster of severely damaged and collapsed buildings from the entire database with an accuracy of 70.59%. Hence, the damage classification approach adopted in this study has the potential for improving effective tools for seismic risk assessment of the existing buildings.

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Published

2023-04-30 — Updated on 2023-05-02

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How to Cite

Özsoy Özbay, A. E. (2023). A decision tree-based damage estimation approach for preliminary seismic assessment of reinforced concrete buildings. Revista De La Construcción. Journal of Construction, 22(1), 5–15. https://doi.org/10.7764/RDLC.22.1.5 (Original work published April 30, 2023)