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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| Published in | Earthquake engineering & structural dynamics Vol. 48; no. 11; pp. 1238 - 1255 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
01.09.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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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| AbstractList | 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. 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. |
| Author | Mangalathu, Sujith Jeon, Jong‐Su |
| Author_xml | – sequence: 1 givenname: Sujith orcidid: 0000-0001-8435-3919 surname: Mangalathu fullname: Mangalathu, Sujith organization: University of California – sequence: 2 givenname: Jong‐Su orcidid: 0000-0001-6657-7265 surname: Jeon fullname: Jeon, Jong‐Su email: jongsujeon@hanyang.ac.kr organization: Hanyang University |
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| Snippet | Summary
A framework for the generation of bridge‐specific fragility curves utilizing the capabilities of machine learning and stripe‐based approach is... A framework for the generation of bridge‐specific fragility curves utilizing the capabilities of machine learning and stripe‐based approach is presented in... |
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| SubjectTerms | 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 |
| Title | Stripe‐based fragility analysis of multispan concrete bridge classes using machine learning techniques |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Feqe.3183 https://www.proquest.com/docview/2268252035 |
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