A Novel Methodology for Spatial Damage Detection and Imaging Using a Distributed Carbon Nanotube-Based Composite Sensor Combined with Electrical Impedance Tomography

Dai, Hongbo; Gallo, Gerard J.; Schumacher, Thomas; Thostenson, Erik T.

doi:10.1007/s10921-016-0341-0

Cited by 59 publications

(58 citation statements)

References 51 publications

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“…Examples of early applications include elasticity imaging (Bonnet and Constantinescu 2005), geometrical inverse problems using sensitivity analysis (Bonnet et al 2002;Aithal and Saigal 1995), and general inverse problems employing boundary integral equations (Nishimura 1995;Mellings and Aliabadi 1995). More recently, researchers have taken advantage of rapidly improving computational resources by solving stationary SHM inverse problems ranging from approximately 10 4 (Yang et al 2017;Gallo and Thostenson 2016;Dai et al 2016) to more than 10 5 degrees of freedom (Zalameda et al 2017;Smyl et al 2017).…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Their work was on panel of size 95 mm × 95 mm × 4 mm, and they were able to monitor circular damages of diameter as small as 3.18 mm, but the resolution was not enough to differentiate a hole of 1.59 mm from the image noise. The most recent study was done by Dai et al in which they proposed a CNT‐based composite sensor to monitor an area of 102 mm × 102 mm. Then they inflicted square holes, artificial crack (0.4 mm wide), and impact damages.…”

Section: Introductionmentioning

confidence: 99%

Self‐sensing damage in CNT infused epoxy panels with and without glass‐fibre reinforcement

Ikikardaslar

Delale

2018

Strain

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The objective of the study is to utilise a material's inherent electrical conductivity as means of damage quantification and damage location detection. After determining the percolation threshold for a carbon nanotube (CNT)‐epoxy mixture, an optimum concentration was chosen to infuse it into glass‐fabric reinforced panels to make them electrically conductive. Two different multiwalled CNT‐epoxy composites were manufactured for this study: CNT enhanced epoxy resin and glass‐fabric reinforced CNT epoxy resin. Epoxy resin‐based glass‐fabric reinforced composite panels enhanced with carbon nanotubes were manufactured with embedded electrodes and then subjected to damages. Rectangular panels of various proportions were studied. Disks made out of copper foil were affixed to surfaces of CNT epoxy panel, whereas in glass‐fabric CNT epoxy specimen, total of 64 electrodes (grid of 8 × 8) were embedded inside the composite panel under the top layer of the 10‐ply fabric. The disks acted as electrodes, enabling voltage measurements using in‐line 4‐probe technique, which minimises contact resistance between the electrodes and the material. Two different configurations of electrode network were employed to scan voltage change in the entire composite panel. The networks included evenly spaced (3 in. for inner ones) electrodes that spanned the surface of the panel. To further investigate influence of electrodes distribution, finite element simulations were used to solve the electrical potential distribution in the panel for various damage sizes and location. Predamage and postdamage voltage field was used as gauge in sensing the damage and its extent for quantification. The finite element method simulation results matched the experimental data closely. The results indicate that there is a consistent behaviour that can be correlated to the size and location of the damage. As spacing between electrodes is increased, they become less responsive to smaller damages. Forty‐eight electrodes (out of 64) were actively used and were enough to confirm that the method can be used as an alternative to electrical tomography method where fewer (boundary) electrodes per area are employed but at a higher cost of computational cost. One important aspect of this study with embedded and distributed electrodes is the fact that the method can be applied to larger panels increasing its utility in practical applications.

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A Novel Methodology for Spatial Damage Detection and Imaging Using a Distributed Carbon Nanotube-Based Composite Sensor Combined with Electrical Impedance Tomography

Cited by 59 publications

References 51 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

Self‐sensing damage in CNT infused epoxy panels with and without glass‐fibre reinforcement

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