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Probabilistic approach to assess URM walls with openings using discrete rigid block analysis (D-RBA)
Journal of Building Engineering ( IF 6.4 ) Pub Date : 2022-09-24 , DOI: 10.1016/j.jobe.2022.105269
Bora Pulatsu, Semih Gonen, Fulvio Parisi, Ece Erdogmus, Kagan Tuncay, Marco Francesco Funari, Paulo B. Lourenço

This study aims to improve our current understanding of the seismic assessment of load-bearing unreinforced masonry (URM) systems by proposing a probabilistic computational modeling framework using the discrete element method (DEM). The main objective is to predict the structural behavior and capacity of URM walls with openings subjected to lateral loading, considering uncertainties in material properties. The proposed modeling strategy represents masonry as an assembly of rigid blocks interacting along their boundaries by adopting the point-contact hypothesis. Fracture energy-based softening contact models are implemented into a commercial discrete element code (3DEC) to better simulate both the pre- and post-peak behavior of masonry. The results highlight the influence of material properties on the force capacity, displacement capacity (drift limits), and collapse mechanisms of walls with openings. Based on the applied non-spatial probabilistic analyses, the most commonly observed failure mechanisms are further assessed using a simplified macro-block formulation. As a result, practical, yet necessary, inferences are made, providing valuable contributions. Furthermore, the validated discontinuum analysis framework is demonstrated as an accurate structural analysis strategy and a useful approach to simulating the potential collapse mechanism of load-bearing URM structures.



中文翻译:

使用离散刚性块分析 (D-RBA) 评估具有开口的 URM 墙的概率方法

本研究旨在通过提出使用离散元法 (DEM) 的概率计算建模框架来提高我们目前对承重无钢筋砌体 (URM) 系统的地震评估的理解。主要目标是预测 URM 墙的结构行为和承载能力,其中开口承受横向载荷,考虑到材料特性的不确定性。所提出的建模策略将砌体表示为通过采用点接触假设沿其边界相互作用的刚性块的组合。基于断裂能的软化接触模型被实施到商业离散单元代码 (3DEC) 中,以更好地模拟砌体的峰值前和峰值后行为。结果突出了材料特性对具有开口的墙壁的受力能力、位移能力(漂移极限)和倒塌机制的影响。基于应用的非空间概率分析,使用简化的宏块公式进一步评估最常见的失效机制。结果,做出了实用但必要的推论,提供了有价值的贡献。此外,

更新日期:2022-09-24
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