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Fractal dimension-based methodology for detecting and quantifying the severity of fatigue cracks in a four-story structure

机译:基于分形维数的方法,用于检测和量化四层结构中疲劳裂纹的严重性

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Civil structures (CS) frequently suffer damage during their service lives. The accumulation of these damages can cause a weakness of the structure and if these damages are not early detected, they can produce a collapse of it. Cracks produced by fatigue in the material are common deterioration situations that can be found in CS. In recent years, a concept named Structural Health Monitoring (SHM) is used for assessing the condition of the structures, with the purpose of generating a monitoring system capable of identifying damages in an early stage in order to avoid economical and human losses. One of steps more important in SHM scheme is the signal processing technique, which has to be capable of estimating features that allow assessing the condition of the civil structure. Hence, the purpose of this paper is to introduce a Fractal Dimension based methodology in order to identify and quantify cracks in a four-story structure subjected to forced excitations. The fractal value of a of time-series signals are calculated for three different FD methods: Katz's FD (KFD), Box Dimension (BD), and Higuchi's FD (HFD). The proposed FD methodology demonstrate to be effective for monitoring the structure condition.
机译:土木结构(CS)在使用寿命期间经常遭受损坏。这些损坏的积累会导致结构的脆弱,如果不及早发现这些损坏,可能会导致结构崩溃。由材料疲劳引起的裂纹是CS中常见的劣化情况。近年来,一种名为“结构健康监视”(SHM)的概念用于评估结构的状况,目的是生成一个能够在早期阶段识别损坏的监视系统,以避免经济和人员损失。在SHM方案中,更重要的步骤之一是信号处理技术,该技术必须能够估计允许评估土木结构状况的特征。因此,本文的目的是介绍一种基于分形维数的方法,以识别和量化受强迫激励的四层结构中的裂缝。针对三种不同的FD方法计算时间序列信号a的分形值:Katz的FD(KFD),Box Dimension(BD)和Higuchi的FD(HFD)。所提出的FD方法论证明对监测结构状况是有效的。

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