|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|138535||2018||11 صفحه PDF||سفارش دهید||7116 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Engineering Structures, Volume 162, 1 May 2018, Pages 166-176
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.