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System Identification and Vibration-Based Damage Detection in a Concrete Shear Wall System

机译:混凝土剪力墙系统的系统识别和基于振动的损伤检测

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Structural Health Monitoring (SHM) based on the vibration of structures has been very attractive subject for researchers in different fields such as: civil, aeronautical and mechanical engineering. System Identification (SI) and Vibration based Damage Identification (VBDI) are two main parts of SHM. A full-scale seven-story reinforced concrete (RC) wall building tested in October 2005 and January 2006 at the University of California at San Diego (UCSD) has been considered here as a case study. The building was excited through four historical California ground motions. The RC wall experienced different levels of damage, progressively under increasing intensity of ground motions. At different levels of damage, the building was subjected to ambient vibration tests and low-amplitude White Gaussian Noise (WGN) base excitation. In this study, the response of the structure to ambient vibration tests was used to identify damage using VBDD methods. The frequency domain decomposition method (FDD) is used here to identify the modal parameters of the building. Damage changes the modal properties (frequency, mode shape and damping) by reducing the stiffness. Therefore, changes in the vibration characteristics of the structure can be used to identify location and severity of damage. A mode shape curvature-based method is used to detect and localize damage. Also a data-driven technique based on Neural Networks has been developed to identify the damage in the structure. The results show a close correlation with the structural damage observed in the experimental study.
机译:基于结构振动的结构健康监测(SHM)已成为土木,航空和机械工程等不同领域的研究人员非常感兴趣的主题。系统识别(SI)和基于振动的损坏识别(VBDI)是SHM的两个主要部分。在此,作为案例研究,已考虑分别于2005年10月和2006年1月在加利福尼亚大学圣地亚哥分校(UCSD)进行测试的全尺寸七层钢筋混凝土(RC)墙建筑。这座建筑因加利福尼亚州的四次历史性地震动而激动不已。 RC墙在地面运动强度增加的情况下逐渐受到不同程度的破坏。在不同程度的破坏下,该建筑物经受了环境振动测试和低振幅白高斯噪声(WGN)的基础激励。在这项研究中,该结构对环境振动测试的响应被用于使用VBDD方法识别损坏。此处使用频域分解方法(FDD)来识别建筑物的模态参数。损伤通过降低刚度来改变模态特性(频率,模态形状和阻尼)。因此,结构振动特性的变化可用于识别损坏的位置和严重程度。基于模式形状曲率的方法用于检测和定位损坏。还开发了一种基于神经网络的数据驱动技术来识别结构中的损坏。结果表明与实验研究中观察到的结构损伤密切相关。

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