期刊
ENGINEERING STRUCTURES
卷 162, 期 -, 页码 166-176出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2018.01.053
关键词
Bridge skew; Concrete box-girder bridges with seat abutments; Artificial neural network; Multi-dimensional fragility curves; Regional risk assessment
资金
- Basic Research Program in Science and Engineering through the National Research Foundation of Korea - Ministry of Education, South Korea [NRF-2016R1D1A1B03933842]
- National Research Foundation of Korea - Korean government (MSIP) [NRF-2016R1A2A1A05005499]
Recent researches are directed towards the regional seismic risk assessment of structures based on a bridge inventory analysis. The framework for traditional regional risk assessments consists of grouping the bridge classes and generating fragility relationships for each bridge class. However, identifying the bridge attributes that dictate the statistically different performances of bridges is often challenging. These attributes also vary depending on the demand parameter under consideration. This paper suggests a multi-parameter fragility methodology using artificial neural network to generate bridge-specific fragility curves without grouping the bridge classes. The proposed methodology helps identify the relative importance of each uncertain parameter on the fragility curves. Results from the case study of skewed box-girder bridges reveal that the ground motion intensity measure, span length, and column longitudinal reinforcement ratio have a significant influence on the seismic fragility of this bridge class.
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