Vibration Transmission across Seismically Damaged Beam-to-Column Junctions of Reinforced Concrete Using Statistical Energy Analysis

IF 1.9 Q3 ENGINEERING, MECHANICAL Vibration Pub Date : 2023-02-02 DOI:10.3390/vibration6010011
M. Filippoupolitis, C. Hopkins
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Abstract

To detect human survivors trapped in buildings after earthquakes by using structure-borne sound it is necessary to have knowledge of vibration transmission in collapsed and fragmented reinforced-concrete buildings. In this paper, statistical energy analysis (SEA) is considered for modelling vibration transmission in seismically damaged, reinforced concrete, beam-to-column junctions where the connection between the beam and the column is made only via the steel reinforcement. An ensemble of 30 randomly damaged beam-to-column junctions was generated using a Monte Carlo simulation with FEM. Experimental SEA (ESEA) is then considered with two or three subsystems to determine the coupling loss factors (CLFs) between the beam and the column with either bending modes or the combination of all mode types. It is shown that bending modes dominate the dynamic response and that the uncertainty of predicting the CLFs using FEM with ESEA is sufficiently low that it should be feasible to estimate the coupling even when the exact angle between the beam and the column is unknown. In addition, the use of two rather than three subsystems for the junction significantly decreases the number of negative coupling loss factors with ESEA. An initial analysis of the results in this paper was presented at the 50th International Congress and Exposition on Noise Control Engineering.
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基于统计能量分析的地震损伤钢筋混凝土梁柱节点的振动传递
为了利用结构声探测地震后被困在建筑物中的人类幸存者,有必要了解倒塌和破碎的钢筋混凝土建筑物中的振动传递。在本文中,考虑了统计能量分析(SEA)来模拟地震损伤的钢筋混凝土梁柱节点中的振动传递,其中梁和柱之间的连接仅通过钢筋实现。使用有限元蒙特卡罗模拟生成了30个随机损伤的梁柱节点的集合。然后考虑具有两个或三个子系统的实验SEA(ESEA),以确定具有弯曲模式或所有模式类型组合的梁和柱之间的耦合损耗因子(CLF)。结果表明,弯曲模式主导了动力响应,并且使用有限元法和ESEA预测CLF的不确定性足够低,即使在梁和柱之间的确切角度未知的情况下,估计耦合也是可行的。此外,结使用两个子系统而不是三个子系统显著减少了ESEA的负耦合损耗因子的数量。在第50届国际噪声控制工程大会和博览会上对本文的结果进行了初步分析。
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CiteScore
3.20
自引率
0.00%
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0
审稿时长
10 weeks
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