Estimation of economic seismic loss of steel moment-frame buildings using a machine learning algorithm
- Authors
- Hwang, Seong-Hoon; Mangalathu, Sujith; Shin, Jinwon; Jeon, Jong-Su
- Issue Date
- Mar-2022
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Expected annual losses; Machine learning algorithm; Structural-modeling-related uncertainty; Steel moment-frame buildings; Seismic risk
- Citation
- ENGINEERING STRUCTURES, v.254, pp.1 - 14
- Indexed
- SCIE
SCOPUS
- Journal Title
- ENGINEERING STRUCTURES
- Volume
- 254
- Start Page
- 1
- End Page
- 14
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/139360
- DOI
- 10.1016/j.engstruct.2022.113877
- ISSN
- 0141-0296
- Abstract
- In this study, the effect of modeling-related uncertainties on the expected annual losses of modern code-compliant steel moment-frame buildings is analyzed. Probabilistic structural models are initially employed to account for all the critical sources of uncertainty. Then, these structural models are used to develop machine-learning-based prediction models to estimate the expected annual losses and the associated economic contributors; the developed machine-learning-based prediction models exhibit an excellent performance in the prediction of the economic seismic losses of steel frame buildings. The effect of structural-modeling-related uncertainties on each loss contributor is also evaluated; the effect of uncertain modeling parameters is observed to be more pronounced on loss contributors such as demolition and structural collapse losses that are controlled primarily by ground motions with a low probability of earthquake occurrence.
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