Digital Image Correlation Techniques for NDE and SHM

Niezrecki, Christopher; Baqersad, Javad; Sabato, Alessandrao

doi:10.1007/978-3-319-30050-4_47-1

Cited by 33 publications

(9 citation statements)

References 153 publications

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“…In recent years, novel paradigms have been advanced to measure structural responses. For example, visionbased systems have been developed for measuring displacements in large-scale structures, analyzing mechanical stressstrain fields, and extracting modal parameters using sequences of images captured by cameras over time [4][5][6][7] . The displacement, stress-strain field, and modal parameters extracted from the cameras have been successfully used as damage indicators in various SHM applications 8,9 .…”

Section: Introductionmentioning

confidence: 99%

Time-inferred autoencoder for learning and adapting changes in the dynamic system

Kulkarni

Sabato

2023

Health Monitoring of Structural and Biological Systems XVII

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Recent machine learning (ML) algorithms have resulted in new paradigms for extracting spatio-temporal characteristics (STCs), such as frequency spectra of critical infrastructures and performing structural health monitoring. However, the accuracy of the STCs extracted using ML is affected by any noise in the data used to train and test the ML algorithms. While noise reduction methods have been successfully proposed, they are application-specific, and none consider the dynamics of the targeted system. Hence, a novel framework named time-inferred autoencoder (TIA) is proposed. The TIA is based on a long, short-term memory (LSTM) neural network to learn the dynamics of the system and a maximum correntropy loss function for noise removal. The robustness of the TIA is validated by collecting a video of an undamaged beam for training the framework and learning the structure’s STCs. Later, the capability of the trained TIA to adapt to changes in the system’s dynamics and reconstruct the STCs is validated by recording noise-corrupted videos of a beam in three different damaged configurations. Results of laboratory tests showed that the TIA reconstructs the natural frequencies of the structure with an error of less than 1%. If further developed, the proposed framework can be used as a structural dynamics tool given its robustness and capability to adapt to changes and noise in the system.

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Section: Introductionmentioning

confidence: 99%

Time-inferred autoencoder for learning and adapting changes in the dynamic system

Kulkarni

Sabato

2023

Health Monitoring of Structural and Biological Systems XVII

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“…19 In the next few years, however, developments in imaging technology can aid in the detection and diagnosis of skin cancer without the need for a skin biopsy. With the advances in optical techniques, researchers have started to use digital image correlation (DIC) and photogrammetry for structural health monitoring 20 and nonintrusive vibration measurements 21 for mechanical components. [22][23][24][25][26][27][28][29][30][31][32][33][34] The optical techniques have also been used for full-field deformations in the biomechanical field.…”

Section: Introductionmentioning

confidence: 99%

Vibration analysis of healthy skin: toward a noninvasive skin diagnosis methodology

et al. 2019

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Vibration analysis of healthy skin: toward a noninvasive skin diagnosis methodology,"Abstract. Several noninvasive imaging techniques have been developed to monitor the health of skin and enhance the diagnosis of skin diseases. Among them, skin elastography is a popular technique used to measure the elasticity of the skin. A change in the elasticity of the skin can influence its natural frequencies and mode shapes. We propose a technique to use the resonant frequencies and mode shapes of the skin to monitor its health. Our study demonstrates how the resonant frequencies and mode shapes of skin can be obtained using numerical and experimental analysis. In our study, natural frequencies and mode shapes are obtained via two methods: (1) finite element analysis: an eigensolution is performed on a finite element model of normal skin, including stratum corneum, epidermis, dermis, and subcutaneous layers and (2) digital image correlation (DIC): several in-vivo measurements have been performed using DIC. The experimental results show a correlation between the DIC and FE results suggesting a noninvasive method to obtain vibration properties of the skin. This method can be further examined to be eventually used as a method to differentiate healthy skin from diseased skin. Prevention, early diagnosis, and treatment are critical in helping to reduce the incidence, morbidity, and mortality associated with skin cancer; thus, making the current study significant and important in the field of skin biomechanics. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Recently in the past decade, several studies and field applications explored the possibility of using non-contact visionbased systems for larger applications such as bridge monitoring and system identifications (e.g., Lee and Shinozuka, 2006;Yoneyama et al, 2007;Chiang et al, 2011;Peddle et al, 2011;Nonis et al, 2013;Murray et al, 2014;Ribeiro et al, 2014;Ye et al, 2015;Pan et al, 2016;Niezrecki et al, 2018). The related works of measuring 3D displacement of bridge structures could be found in Santos et al (2012) where 3D displacements of the bridge deck were measured.…”

Section: Introductionmentioning

confidence: 99%

“…The related works of measuring 3D displacement of bridge structures could be found in Santos et al (2012) where 3D displacements of the bridge deck were measured. Another relevant study is Nonis et al (2013), and more recent work from the same group Niezrecki et al (2018), where 3D-DIC was used for periodic and non-destructive inspection of concrete bridges to locate non-visible cracks in concrete, quantify spalling, and measure bridge deformation. However, most of the studies either focused on demonstrating the concept or had other limitations in the application.…”

Section: Introductionmentioning

confidence: 99%

System Identification of Large-Scale Bridges Using Target-Tracking Digital Image Correlation

Ngeljaratan

Moustafa

2019

Front. Built Environ.

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This paper characterizes the extensive research activities conducted in the Earthquake Engineering Laboratory of University of Nevada, Reno, in the field of dynamic monitoring and system identification of three 1/3-scale two-span bridges. The first part of the study briefly presents the verification of target-tracking Digital Image Correlation (DIC) results as compared to conventional sensors, e.g., string potentiometers and triaxial accelerometers from one of the three bridge tests. Structural system identification is presented in the second part for the other two bridges with a focus on determining structural model parameters based on the DIC measured response data. All bridges were tested under bidirectional earthquake loading using the multiple shake table array. However, the system identification used data collected from white noise runs before and after the seismic tests. A quasi-linear response of the system was assumed because of the low intensity white noise base excitations, and the modal parameters were estimated accordingly. Using the structural vibration data recorded by target-tracking DIC at various locations on the bridges, five system identification methods were applied to analyze the modal parameters of the tested bridges. The results were used to estimate the frequency, damping ratio, and mode shapes of the bridges in two states. The initial state is before seismic testing and the end state is the damaged state after the completion of the seismic tests. The results show that the applied methods provide a reasonable estimate of the natural frequency and damping ratio of the bridge systems in the original and damaged states.

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Digital Image Correlation Techniques for NDE and SHM

Cited by 33 publications

References 153 publications

Time-inferred autoencoder for learning and adapting changes in the dynamic system

Time-inferred autoencoder for learning and adapting changes in the dynamic system

Vibration analysis of healthy skin: toward a noninvasive skin diagnosis methodology

System Identification of Large-Scale Bridges Using Target-Tracking Digital Image Correlation

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