Journal ArticleDOI
A Data-Driven Approach to Structural Health Monitoring of Bridge Structures Based on the Discrete Model and FFT-Deep Learning
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TLDR
This research investigates changes in the mechanical properties of complex structures using a combination of the discrete model, Fast Fourier Transform (FFT) analysis and deep learning, and applies deep learning in the noise reduction process for the original data.Abstract:
In this paper, we investigate changes in the mechanical properties of complex structures using a combination of the discrete model, Fast Fourier Transform (FFT) analysis and deep learning. The first idea from this research utilizes the discrete model from a perspective that is different from the finite element method (FEM) of previous works. As the method in this paper only models the mechanical properties of structures with finite degrees of freedom instead of dividing them into smaller elements, it reduces error in evaluation and produces more realistic results compared to the FEM model. Another advantage is how it allows the research to survey both parameters that affect the mechanical properties of structures—the overall stiffness (K) and the damping coefficient (c)—during vibration, while previous researches focus only on one of these two parameters. The second idea is to use FFT analysis to increase the sensitivity of the signal received during vibration. FFT analysis simplifies calculations, thereby reducing the effect of noise or errors. The sensitivity achieved in FFT analysis increases by 25% compared to traditional Fourier Transform (FT) analysis; moreover, the error in FFT analysis compared to experimental results is quite small, less than 2%. This shows that FFT is a suitable method to identify sensitive characteristics in evaluating changes in the mechanical properties. When FFT is combined with the discrete model, results are much better than those of several existing approaches. For the last idea, the manuscript applies deep learning (FFT-deep learning) in the noise reduction process for the original data. This makes the results much more accurate than in previous studies. The results of this research are shown through the monitoring of spans of the Saigon Bridge—the biggest and most important bridge in Ho Chi Minh City, Vietnam—during the past 11 years. The correspondence between the theoretically obtained result and the experimental one at the Saigon Bridge suggests a new area for development in evaluating and forecasting structural changes in the future.read more
Citations
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Statistical analysis of online strain response and its application in fatigue assessment of a long-span steel bridge
TL;DR: The analytical results show that the modified model is practical for reliable evaluation of the service life of existing bridges under random traffic loading.
Journal ArticleDOI
A deep learning platform for evaluating energy loss parameter in engineering structures
TL;DR: This study improves the model of Hooke’s traditional linear mechanical change using a nonlinear model to truly express the nature of structures in reality and adds to the current framework regarding changes in the mechanical properties of a material according to the Kelvin-Voigt model.
Journal ArticleDOI
Separation of the Structure Signal by the Maximal Overlap Discrete Wavelet Transform and Fast Fourier Transform
TL;DR: In this article, the maximal overlap discrete wavelet transform (MODWT) method combined with the method of fast Fourier transform (FFT) is used to separate the synthetic vibration signal into a structure's discrete vibration and other extraneous vibrations.
Journal ArticleDOI
Long-term structural health monitoring for bridge based on back propagation neural network and long and short-term memory
TL;DR: In this paper , an in-site structural health monitoring (SHM) system is designed and deployed on the Caohekou Bridge in China with a series of sensors installed, providing continuous real-time data for 4 years.
Journal ArticleDOI
Self-Powered Self-Contained Wireless Vibration Synchronous Sensor for Fault Detection
TL;DR: A fully integrated vibration-powered energy harvester sensor system that is interfaced with a custom-developed fault detection app and employs an FFT algorithm on the transmitted data and notifies the user when a threshold vibration level is reached.
References
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Journal ArticleDOI
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An application of Genetic Algorithms to identify damage in elastic structures
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TL;DR: In this article, a technique for the detection of macroscopic structural damage in elastic structures is described, where the location and quantification of the extent of the damage are performed with genetic algorithms implemented by using the residual force method which is based on conventional modal analysis theory.
Journal ArticleDOI
Wavelet-based crack identification of bridge beam from operational deflection time history
Xinqun Zhu,Siu-Seong Law +1 more
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