钢筋混凝土框架状态依赖性脆弱性案例研究

Karim Aljawhari, F. Freddi, C. Galasso
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引用次数: 3

摘要

本文研究了主余震序列对欧式钢筋混凝土(RC)抗弯矩框架(MRFs)数值易损性和易损性关系的影响。为了便于说明,我们选择了一个四层、四隔板的非延性RC MRF。该索引构建代表了地中海地区一个典型的漏洞类别。研究了砌体填料对结构抗震性能的影响。为案例研究框架建立了一个先进的数值非线性模型,然后通过“序列云”方法,使用真实和人工主余震序列进行非线性动态分析。获得的地震需求估计允许仅在遭受主震时为未损坏的框架生成易损性函数。此外,导出了主震破坏框架在随后遭受余震时的状态相关脆性函数。损害-损失模型,特别根据意大利震后数据校准,用于推导本案例研究结构的脆弱性函数。初步研究结果表明,在一系列地震动强度下,框架经历了严重的破坏状态和高损失,余震造成的破坏明显增加。最后提出了一种生成矢量值主余震易损性关系的尝试。建议的漏洞面可以更容易地实现到一个依赖时间的风险评估框架中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
STATE-DEPENDENT VULNERABILITY OF CASE-STUDY REINFORCED CONCRETE FRAMES
This study investigates the effect of mainshock-aftershock sequences on numerical fragility and vulnerability relationships of European reinforced concrete (RC) moment-resisting frames (MRFs). A four-story, four-bay nonductile RC MRF is selected for illustrative purposes. This index building is representative of a typical vulnerability class in the Mediterranean region. The influence of the masonry infills on seismic performance is also investigated. An advanced numerical nonlinear model is developed for the case-study frame and then assessed through nonlinear dynamic analysis using both real and artificial mainshock-aftershock sequences, via a ‘sequential cloud’ approach. The obtained seismic demand estimates allow to generate fragility functions for the undamaged frame when subjected to mainshocks only. Moreover, statedependent fragility functions are derived for the mainshock-damaged frame when subsequently subjected to aftershocks. Damage-to-loss models, specifically calibrated on Italian post-earthquake data, are used to derive vulnerability functions for this case-study structure. Preliminary results from the study show that the frame experiences severe damages states and high losses for a range of ground-motion shaking intensities, with a clear damage increase due to aftershocks. An attempt to generate vector-valued mainshock-aftershock vulnerability relationships is finally presented. The proposed vulnerability surfaces can be more easily implemented into a time-dependent risk assessment framework.
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