Browsing by Author "Fajri, Haikal"

Now showing 1 - 2 of 2

Application of Multifractal Detrended Fluctuation Analysis for Structural Health Monitoring
(2015-01-01)
Fajri, Haikal
;
Lin, Tzu-Kang
;
交大名義發表
;
National Chiao Tung University
This paper presents an application of multifractal detrended fluctuation analysis (MF-DFA) for structural health monitoring (SHM). To demonstrate the performance of proposed SHM algorithm for detecting and locating the damage, structure response of seven-story scale-down benchmark measured from ambient vibration was evaluated. It was found that damage condition due to stiffness change was effectively detected by using multifractal spectrum peak value of a single sensor under different damage categories. Moreover, the damage location was discovered after analyzing the signals of the entire floors under the same damage categories by using MF-DFA analysis. The multifractal spectrums of damage cases were subsequently compared with the healthy structure to distinctly assess the damage location. The result revealed that the damage location was correctly predicted as the shifted spectrum trend of the proposed SHM-based system. As it offers an alternative choice of the traditional SHM system with a set of initial reference, and only ambient vibration signal is required, the proposed SHM system is a promising tool in the practical implementation.
Damage detection of structures with detrended fluctuation and detrended crosscorrelation analyses
(2017-03-01)
Lin, Tzu-Kang
;
Fajri, Haikal
;
土木工程學系
;
Department of Civil Engineering
Recently, fractal analysis has shown its potential for damage detection and assessment in fields such as biomedical and mechanical engineering. For its practicability in interpreting irregular, complex, and disordered phenomena, a structural health monitoring (SHM) system based on detrended fluctuation analysis (DFA) and detrended cross-correlation analysis (DCCA) is proposed. First, damage conditions can be swiftly detected by evaluating ambient vibration signals measured from a structure through DFA. Damage locations can then be determined by analyzing the cross correlation of signals of different floors by applying DCCA. A damage index is also proposed based on multi-scale DCCA curves to improve the damage location accuracy. To verify the performance of the proposed SHM system, a four-story numerical model was used to simulate various damage conditions with different noise levels. Furthermore, an experimental verification was conducted on a seven-story benchmark structure to assess the potential damage. The results revealed that the DFA method could detect the damage conditions satisfactorily, and damage locations can be identified through the DCCA method with an accuracy of 75%. Moreover, damage locations can be correctly assessed by the damage index method with an improved accuracy of 87.5%. The proposed SHM system has promising application in practical implementations.