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

Summary 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...

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Vydáno v:Earthquake engineering & structural dynamics Ročník 48; číslo 11; s. 1238 - 1255
Hlavní autoři: Mangalathu, Sujith, Jeon, Jong‐Su
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.09.2019
Témata:
Artificial intelligence
bridge‐specific fragility
Computer applications
Concrete bridges
Forests
Fragility
Learning algorithms
Machine learning
Mathematical models
Methodology
multispan bridges
Numerical models
regional risk assessment
Risk assessment
ISSN:0098-8847, 1096-9845
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Shrnutí:Summary 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.
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ISSN:0098-8847
1096-9845
DOI:10.1002/eqe.3183