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Stripe‐based fragility analysis of multispan concrete bridge classes using machine learning techniques
Author(s) -
Mangalathu Sujith,
Jeon JongSu
Publication year - 2019
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.3183
Subject(s) - fragility , bridge (graph theory) , variable (mathematics) , computer science , structural engineering , set (abstract data type) , engineering , mathematics , medicine , mathematical analysis , chemistry , programming language
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.