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Stripe-based fragility analysis of multispan concrete bridge classes using machine learning techniques

Title
Stripe-based fragility analysis of multispan concrete bridge classes using machine learning techniques
Author
전종수
Keywords
bridge-specific fragility; machine learning; multispan bridges; regional risk assessment
Issue Date
2019-09
Publisher
WILEY
Citation
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS, v. 48, no. 11, Page. 1238-1255
Abstract
A framework for the generation of bridge-specific fragility curves utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and expensive resimulation. The methodology does not place any assumptions on the demand model of various components and helps to identify the relative importance of each uncertain variable in their seismic demand model. The methodology is demonstrated through the case study of a multispan concrete bridge class in California. Geometric, material, and structural uncertainties are accounted for in the generation of bridge numerical models and their fragility curves. It is also noted that the traditional lognormality assumption on the demand model leads to unrealistic fragility estimates. Fragility results obtained by the proposed methodology can be deployed in a risk assessment platform such as HAZUS for regional loss estimation.
URI
https://onlinelibrary.wiley.com/doi/full/10.1002/eqe.3183https://repository.hanyang.ac.kr/handle/20.500.11754/154085
ISSN
0098-8847; 1096-9845
DOI
10.1002/eqe.3183
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > CIVIL AND ENVIRONMENTAL ENGINEERING(건설환경공학과) > Articles
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