支持向量机
加速度计
主成分分析
结构健康监测
计算机科学
加速度
结构工程
噪音(视频)
信号(编程语言)
工程类
模式识别(心理学)
人工智能
物理
图像(数学)
程序设计语言
操作系统
经典力学
作者
Anahita Bolourani,Maryam Bitaraf,Ala Nekouvaght Tak
出处
期刊:Structures
[Elsevier]
日期:2021-07-21
卷期号:33: 4501-4513
被引量:21
标识
DOI:10.1016/j.istruc.2021.07.032
摘要
Harbor caissons are regarded as one of the most critical infrastructures, and any disruption in their operation would have dramatic economic consequences. The need for a health monitoring system for these structures is thus evident. The primary aim of this study is to develop a structural health monitoring (SHM) system to effectively detect damage in harbor caissons. To this end, through performing dynamic analysis, acceleration signals are extracted from the locations in the model corresponding to the presumed accelerometer's placement in an actual structure. Three levels of white Gaussian noises are added to the original signal to simulate the ambient noise. Using the acquired signals, the damage-sensitive features in both frequency and time domains are determined in the structure. In this study, leveraging Principal Component Analysis (PCA) and Support Vector Machine (SVM), the damage-sensitive features associated with the damaged and undamaged structure are reduced in dimensionality and classified. Hence, the introduced system can assess the state of the structure based on the input signal from the accelerometer and detect possible damage and its severity. The effectiveness of the proposed system in detecting damage is shown using the numerical model of a real harbor caisson.
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