The effect of error and regularization norms on strain and damage identification via electrical impedance tomography in piezoresistive nanocomposites

Tallman, Tyler N.; Hernandez, Julio A.

doi:10.1016/j.ndteint.2017.07.003

Cited by 26 publications

(19 citation statements)

References 38 publications

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“…(Seppänen et al 2014a). While smoothness-promoting regularization methods can certainly be used in such cases (Tallman and Hernandez 2017), improved accuracy of damage location and distribution can be gained by using sharpness-promoting regularization. For this purpose, Total Variation regularization is most commonly used.…”

Section: Regularizationmentioning

confidence: 99%

An overview of 38 least squares–based frameworks for structural damage tomography

Smyl

Bossuyt

Ahmad

et al. 2019

Structural Health Monitoring

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The ability to reliably detect damage and intercept deleterious processes, such as cracking, corrosion, and plasticity are central themes in structural health monitoring. The importance of detecting such processes early on lies in the realization that delays may decrease safety, increase long-term repair/retrofit costs, and degrade the overall user experience of civil infrastructure. Since real structures exist in more than one dimension, the detection of distributed damage processes also generally requires input data from more than one dimension. Often, however, interpretation of distributed data—alone—offers insufficient information. For this reason, engineers and researchers have become interested in stationary inverse methods, for example, utilizing distributed data from stationary or quasi-stationary measurements for tomographic imaging structures. Presently, however, there are barriers in implementing stationary inverse methods at the scale of built civil structures. Of these barriers, a lack of available straightforward inverse algorithms is at the forefront. To address this, we provide 38 least-squares frameworks encompassing single-state, two-state, and joint tomographic imaging of structural damage. These regimes are then applied to two emerging structural health monitoring imaging modalities: Electrical Resistance Tomography and Quasi-Static Elasticity Imaging. The feasibility of the regimes are then demonstrated using simulated and experimental data.

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Section: Regularizationmentioning

confidence: 99%

An overview of 38 least squares–based frameworks for structural damage tomography

Smyl

Bossuyt

Ahmad

et al. 2019

Structural Health Monitoring

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“…Some popular methods used for detecting local and/or distributed damage utilize optical [5], imagecorrelating [6], ultrasonic [7,8], capacitive [9,10], or direct-strain [11] modalities. Recently, the use of electrically-conductive sensing skins coupled with Electrical Resistance Tomography (ERT) for imaging spatially-distributed damage has been a source of much research interest [12,13,14,15,16,17]. In related works, the use of ERT to monitor moisture flow in cement-based materials, a significant contributer to environmental degradation, in two-and three-dimensions was shown in [18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Detection and reconstruction of complex structural cracking patterns with electrical imaging

Smyl

Pour-Ghaz

Seppänen

2018

NDT & E International

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The ability to detect cracks in structural elements is an integral component in the assessment of structural heath and integrity. Recently, Electrical Resistance Tomography (ERT)-based sensing skins have been shown to reliably image progressive surface damage on structural members. However, so far the approach has only been tested in cases of relatively simple crack patterns. Because the spatial resolution of ERT is generally low, it is an open question whether the ERT-based sensing skins are able to image complex structural cracking patterns. In this paper, we test the accuracy of ERT for reconstructing cracking patterns experimentally and computationally. In the computational study, we use a set of numerical simulations that model progressive cracking in a rectangular beam geometry. We also investigate the effect of image reconstruction methods on the crack pattern estimates: In addition to the contemporary image reconstruction method used in the recent sensing skin studies, we test the feasibility of a novel approach where model-based structural prior information on the cracking probability is accounted for in the image reconstruction. The results of this study indicate that ERT-based sensing skins are able to detect and reconstruct complex structural cracking patterns, especially when structural prior information is utilized in the image reconstruction.

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“…Nanocomposites have received tremendous attention from the structural health monitoring (SHM) research community because they are piezoresistive and therefore self-sensing. To date, much success has been had utilizing the piezoresistive effect to monitor damage initiation and accumulation via resistance change methods [1][2][3], strain identification [4][5][6], and in-plane damage localization via conductivity imaging techniques such as electrical impedance tomography (EIT) [7][8][9][10][11][12][13][14][15][16][17]. Efforts are even being made to discern the precise strain state which gives rise to an observed conductivity change via the process of piezoresistive inversion [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Damage Detection in Nanofiller-Modified Composites With External Circuitry via Resonant Frequency Shifts

Tallman

2018

Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; En

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Conductive nanofiller-modified composites have received a lot of attention from the structural health monitoring (SHM) research community in recent years because these materials are piezoresistive (i.e. they have deformation and damage-dependent electrical conductivity) and are therefore self-sensing. To date, the vast majority of work in this area has utilized direct current (DC) interrogation to identify and/or localize damage. While this approach has been met with much success, it is also well known that nanofiller-modified composites possess frequency-dependent electrical behavior. This behavior can be roughly modeled as a parallel resistor-capacitor circuit. However, much less work has been done to explore the potential this frequency-dependent behavior for damage detection. To this end, the work herein presented covers some preliminary results which leverage high-frequency electrical interrogation for damage detection. More specifically, carbon nanofiber (CNF)/epoxy specimens are produced and connected to an external inductor in both series and parallel configurations. Because the CNF/epoxy electrically behaves like a resistor-capacitor circuit, the inclusion of an inductor enables electrical resonance to be achieved. Changes in resonant frequency are then used for rudimentary damage detection. These preliminary results indicate that the potential of SHM via the piezoresistive effect in nanofiller-modified composites can be considerably expanded by leveraging alternating current (AC) interrogation and resonant frequency principles.

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The effect of error and regularization norms on strain and damage identification via electrical impedance tomography in piezoresistive nanocomposites

Cited by 26 publications

References 38 publications

An overview of 38 least squares–based frameworks for structural damage tomography

An overview of 38 least squares–based frameworks for structural damage tomography

Detection and reconstruction of complex structural cracking patterns with electrical imaging

Damage Detection in Nanofiller-Modified Composites With External Circuitry via Resonant Frequency Shifts

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