希尔伯特-黄变换
水准点(测量)
人工神经网络
计算机科学
加速度
信号处理
信号(编程语言)
结构健康监测
非线性系统
希尔伯特变换
振动
人工智能
结构工程
模式识别(心理学)
工程类
光谱密度
声学
数字信号处理
地质学
物理
程序设计语言
滤波器(信号处理)
计算机硬件
电信
经典力学
量子力学
计算机视觉
大地测量学
作者
Seyed Bahram Beheshti Aval,Vahid Ahmadian,Mohammad Maldar,Ehsan Darvishan
标识
DOI:10.1177/1369433219886079
摘要
This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide the possibility for side-by-side comparison. First, three signal processing techniques including empirical mode decomposition, Hilbert vibration decomposition, and local mean decomposition, categorized as instantaneous time–frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Time-varying single degree of freedom and multiple degree of freedom models are used since real vibration signals are nonstationary and nonlinear in nature. Based on the results, empirical mode decomposition has proved to outperform than the others. Second, empirical mode decomposition is used to extract the acceleration response of the sensors. Next, a two-stage artificial neural network is used to classify damage patterns. The first artificial neural network identifies location and severity of damage and the second one calculates the severity of damage for the entire structure. IASC–ASCE benchmark problem is used to validate the proposed procedure. By taking advantage of signal processing and artificial intelligence techniques, damage detection of structures was successfully carried out in three levels including damage occurrence, damage severity, and the location of damage.
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