| $ \beta_{ \text{min, }\sigma} $ | $ Q_\sigma $ | $ \beta_{ \text{min, }\lambda_i} $ i=i,2 | $ Q_{\lambda_i} $ i=i,2 | $ \beta_{ \text{min}, z_i} $ i=i,2 | $ Q_{z_i} $ i=i,2 |
| $ 0.25\sigma_* $ | $ 0.24\sigma_* $ | 0.01 | 0.0075 | 0.01 | 0.001 |
Measurement of mechanical strain on the surface of an object is important in areas such as structural health monitoring and tactile sensing applications. In this paper, the measurement of strain fields on surfaces using electrical impedance tomography (EIT) -based surface sensor, or sensing skin, is considered. Because mechanical strain generally leads to anisotropic changes in the electrical conductivity of the material, the particular focus is on anisotropic EIT imaging of the surface sensor materials. The anisotropic EIT reconstruction problem is written in the frameworks of Bayesian inference and non-linear difference (NLD) imaging, and the approach is validated with a numerical simulation study and experimentally. The simulation part of the study demonstrates the feasibility of the proposed approach for imaging anisotropic conductivity changes. Based on the simulation, in an ideal setup with low noise level, it is possible to detect features in the different components of an anisotropic conductivity change caused by mechanical strain. In the experimental part of the study, it is shown that the main features of a mechanical strain field can also be detected using EIT with real data. The experimental studies are conducted using a painted, elastic and conductive surface coating that is deformed by stretching and, for a reference, targeted with localized pressure using weights. In the former experiment, the anisotropic EIT reconstructions are qualitatively compared with strain field estimates obtained by digital image correlation (DIC) analysis applied to photographs of the surface. Despite some apparent uncertainties associated with the measurement setup, the results demonstrate that anisotropic EIT -based surface sensors can provide valuable information on mechanical strain fields.
| Citation: |
Figure 1. Left: schematic representation of the domain in the elasticity FEM simulation. The top edge of the domain was imposed a uniform displacement $ u_0 $ in the $ y $-direction, while the bottom edge was fixed in place. The edges marked in blue were assigned the condition of zero traction. Right: Finite element mesh used in the elasticity FEM simulation
Figure 4. Results of simulation studies. True, synthetic distributions of $ \sigma $, $ s_{xx} $, $ s_{yy} $ and $ s_{xy} $ (top row, from left to right), and NLD-based EIT reconstructions of the respective distributions in the smaller noise case ($ d_1 $ = 0.01, middle row) and larger noise case ($ d_1 $ = 0.1, bottom row)
Figure 7. Left: Photograph of the experimental setup in the strain experiment. The insulating rubber mat is brown and the dark square shaped area on top of it is formed by the conductive paint. Right: The region of interest (white) on which the strain was reconstructed using DIC, superimposed on the grayscale photograph of the sample
Figure 8. Plane strain experiment. DIC based reconstructions of the strain field components $ \varepsilon_{xx} $ (left column), $ \varepsilon_{yy} $ (middle column) and $ \varepsilon_{xy} $ (right column) corresponding to three values of percentual elongation of the mat: 0.1 % (top row), 1.5 % (middle row) and 3 % (bottom row). On the top row, regions of interest, each made up of a union of two parts are marked by colored squares. Each color corresponds to one region of interest: the region ROI1 is made up of the black squares, ROI2 of the magenta squares and ROI3 of the orange squares. The side length of each square was 1 cm
Figure 9. Plane strain experiment. EIT reconstructions for the anisotropic conductivity multiplier field components $ s_{xx} $ (left column), $ s_{yy} $ (middle column) and $ s_{xy} $ (right column) corresponding to three values of percentual elongation of the mat: 0.1 % (top row), 1.5 % (middle row) and 3 % (bottom row). Note the subtraction of unity from $ s_{xx} $ and $ s_{yy} $
Figure 10. Plane strain experiment. Top row: EIT reconstructions of the isotropic initial conductivity $ \sigma $ given by the NLD reconstruction approach, when the $ I_1 $ dataset corresponds to the undeformed sample, and the $ I_2 $ dataset corresponds to three different deformations of the sample: 0.1 %, 1.5 %, 3 %. Bottom row: Respective estimates of the contact impedances at the initial state ($ z_1 $) and after the deformation ($ z_2 $)
Figure 11. Left: the average values of $ s_{xx} $ (blue) and $ s_{yy} $ (red) in the region of interest $ ROI1 $ as a function of the applied elongation. The solid markers correspond to the extension phase of the experiment. Empty markers correspond to the recovery phase, in which the tension of the sample was relaxed in steps. The averaging is over the entire region of interest ROI1, made up from two parts on the left and right side of the central hole. Right: the corresponding average values for $ s_{xy} $ in the region of interest ROI1
Figure 12. Left: Average value of $ s_{xx} $ vs. average value of $ \varepsilon_{xx} $ (blue), and average value of $ s_{yy} $ vs. average value of $ \varepsilon_{yy} $ (red) within ROI1 during the strain experiment. Right: Average value of $ s_{xy} $ vs. $ \varepsilon_{xy} $ within ROI1 (black), ROI2 (magenta) and ROI3 (orange) during the strain experiment. In both figures, the solid markers correspond to the extension phase of the experiment. Empty markers correspond to the recovery phase, in which the tension of the sample was relaxed in steps
Figure 14. Contact pressure experiment. Top row: EIT reconstructions of the isotropic initial conductivity $ \sigma $ given by the NLD reconstruction approach, when the $ I_1 $ dataset corresponds to the undeformed sample, and the $ I_2 $ dataset corresponds to three different weight locations. The true positions of the weight are marked with black circles. Bottom row: Respective estimates of the contact impedances at the initial state ($ z_1 $) and after the deformation ($ z_2 $)
Table 1.
The values of
| $ \beta_{ \text{min, }\sigma} $ | $ Q_\sigma $ | $ \beta_{ \text{min, }\lambda_i} $ i=i,2 | $ Q_{\lambda_i} $ i=i,2 | $ \beta_{ \text{min}, z_i} $ i=i,2 | $ Q_{z_i} $ i=i,2 |
| $ 0.25\sigma_* $ | $ 0.24\sigma_* $ | 0.01 | 0.0075 | 0.01 | 0.001 |
| [1] |
J. F. Abascal, S. R. Arridge, D. Atkinson, R. Horesh, L. Fabrizi, M. De Lucia, L. Horesh, R. H. Bayford and D. S. Holder, Use of anisotropic modelling in electrical impedance tomography; Description of method and preliminary assessment of utility in imaging brain function in the adult human head, NeuroImage, 43 (2008), 258-268.
doi: 10.1016/j.neuroimage.2008.07.023.
|
| [2] |
J. F. Abascal, S. R. Arridge, W. R. B. Lionheart, R. H. Bayford and D. S. Holder, Validation of a finite-element solution for electrical impedance tomography in an anisotropic medium, Physiological Measurement, 28 (2007), S129-S140.
doi: 10.1088/0967-3334/28/7/S10.
|
| [3] |
J. F. Abascal, W. R. B. Lionheart, S. R. Arridge, M. Schweiger, D. Atkinson and D. S. Holder, Electrical impedance tomography in anisotropic media with known eigenvectors, Inverse Problems, 27 (2011), 065004.
doi: 10.1088/0266-5611/27/6/065004.
|
| [4] |
O. S. Al-Dahiree, M. O. Tokhi, N. H. Hadi, N. R. Hmoad, R. A. R. Ghazilla, H. J. Yap and E. A. Albaadani, Design and Shape Optimization of Strain Gauge Load Cell for Axial Force Measurement for Test Benches, Sensors, 22 (2022), 7508.
doi: 10.3390/s22197508.
|
| [5] |
K. Astala and L. Päivärinta, Calderón's inverse problem for anisotropic conductivity in the plane, Communications in Partial Differential Equations, 30 (2005), 207-224.
doi: 10.1081/PDE-200044485.
|
| [6] |
J. Blaber, B. Adair and A. Antoniou, Ncorr: Open-Source 2D digital image correlation matlab software, Experimental Mechanics, 55 (2015), 1105-1122.
doi: 10.1007/s11340-015-0009-1.
|
| [7] |
J. Cagan, J. Pelant, M. Kyncl, M. Kadlec and L. Michalcova, Damage detection in carbon fiber–reinforced polymer composite via electrical resistance tomography with Gaussian anisotropic regularization, Structural Health Monitoring, 1 (2018), 1698-1710.
doi: 10.1177/1475921718820013.
|
| [8] |
L. Chen, H. Hassan, T. N. Tallman, S. S. Huang and D. Smyl, Predicting strain and stress fields in self-sensing nanocomposites using deep learned electrical tomography, Smart Materials and Structures, 31 (2022), 045024.
doi: 10.1088/1361-665X/ac585f.
|
| [9] |
F. Clauß, M. A. Ahrens and P. Mark, A comparative evaluation of strain measurement techniques in reinforced concrete structures–A discussion of assembly, application, and accuracy, Structural Concrete, 22 (2021), 2992-3007.
doi: 10.1002/suco.202000706.
|
| [10] |
S. Cotin, M. Duprez, V. Lleras, A. Lozinski and K. Vuillemot, $\phi$-FEM: An Efficient Simulation Tool Using Simple Meshes for Problems in Structure Mechanics and Heat Transfer, Partition of Unity Methods (Wiley Series in Computational Mechanics) 1$^{st}$ edition, Wiley, 2022.
|
| [11] |
G. J. Gallo and E. T. Thostenson, Spatial damage detection in electrically anisotropic fiber-reinforced composites using carbon nanotube networks, Composite Structures, 141 (2016), 14-23.
doi: 10.1016/j.compstruct.2015.07.082.
|
| [12] |
C. Geuzaine and J.-F. Remacle, Gmsh: A three-dimensional finite element mesh generator with built-in pre- and post-processing facilities, International Journal for Numerical Methods in Engineering, 79 (2009), 1309-1331.
doi: 10.1002/nme.2579.
|
| [13] |
M. Hallaji, A. Seppänen and M. Pour-Ghaz, Electrical impedance tomography-based sensing skin for quantitative imaging of damage in concrete, Smart Materials and Structures, 23 (2014), 085001.
doi: 10.1088/0964-1726/23/8/085001.
|
| [14] |
S. J. Hamilton, M. Lassas and S. Siltanen, A direct reconstruction method for anisotropic electrical impedance tomography, Inverse Problems, 30 (2014), 075007.
doi: 10.1088/0266-5611/30/7/075007.
|
| [15] |
H. Hassan, W. A. Crossley and T. N. Tallman, Hybrid optimization schemes for solving the piezoresistive inversion problem in self-sensing materials, Smart Materials and Structures, 33 (2024), 065033.
doi: 10.1088/1361-665X/ad49ec.
|
| [16] |
H. Hassan and T. N. Tallman, A comparison of metaheuristic algorithms for solving the piezoresistive inverse problem in self-sensing materials, IEEE Sensors Journal, 21 (2020), 659-666.
doi: 10.1109/JSEN.2020.3014554.
|
| [17] |
H. Hassan and T. N. Tallman, Failure prediction in self-sensing nanocomposites via genetic algorithm-enabled piezoresistive inversion, Structural Health Monitoring, 19 (2020), 765-780.
doi: 10.1177/1475921719863062.
|
| [18] |
L. Heikkinen, T. Vilhunen, R. West and M. Vauhkonen, Simultaneous reconstruction of electrode contact impedances and internal electrical properties: Ⅱ. Laboratory experiments, Measurement Science and Technology, 13 (2002), 1855-1861.
doi: 10.1088/0957-0233/13/12/308.
|
| [19] |
J. M. Henault, M. Quiertant, S. Delepine-Lesoille, J. Salin, G. Moreau, F. Taillade and K. Benzarti, Quantitative strain measurement and crack detection in RC structures using a truly distributed fiber optic sensing system, Construction and Building Materials, 37 (2012), 916-923.
doi: 10.1016/j.conbuildmat.2012.05.029.
|
| [20] |
J. V. Herwanger, C. C. Pain, A. Binley, C. R. E. deOliveira and M. H. Worthington, Anisotropic resistivity tomography, Geophysical Journal International, 158 (2004), 409-425.
doi: 10.1111/j.1365-246X.2004.02314.x.
|
| [21] |
L. Homa, M. Sannamanni, A. J. Thomas, T. N. Tallman and J. Wertz, Enhanced damage imaging in three-dimensional composite structures via electrical impedance tomography with mixed and level set regularization, NDT and E International, 137 (2023), 102830.
|
| [22] |
J. Jauhiainen, M. Pour-Ghaz, T. Valkonen and A. Seppänen, Nonplanar sensing skins for structural health monitoring based on electrical resistance tomography, Computer-Aided Civil and Infrastructure Engineering, 36 (2021), 1488-1507.
doi: 10.1111/mice.12689.
|
| [23] |
D. Jeon and S. Yoon, Electrical Resistance Tomography (ERT) for Concrete Structure Applications: A Review, Buildings, 14 (2014), 2654.
doi: 10.3390/buildings14092654.
|
| [24] |
J. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems, Springer, 2005.
|
| [25] |
R. V. Kohn and M. Vogelius, Determining conductivity by boundary measurements Ⅱ. Interior results, Communications on Pure and Applied Mathematics, 38 (1985), 643-667.
doi: 10.1002/cpa.3160380513.
|
| [26] |
P. Kuusela and A. Seppänen, A coupled double-layer electrical impedance tomography-based sensing skin for pressure and leak detection, Sensors, 24 (2024), 4134.
doi: 10.3390/s24134134.
|
| [27] |
H. Lee, H. Cho, S.J. Kim, Y. Kim and J. Kim, Dispenser printing of piezo-resistive nanocomposite on woven elastic fabric and hysteresis compensation for skin-mountable stretch sensing, Smart Materials and Structures, 27 (2018), 025017.
doi: 10.1088/1361-665X/aaa5e3.
|
| [28] |
H. Lee, D. Kwon, H. Cho, I. Park and J. Kim, Soft nanocomposite based multipoint, multi-directional strain mapping sensor using anisotropic electrical impedance tomography, Scientific Reports, 7 (2017), 39837.
doi: 10.1038/srep39837.
|
| [29] |
H. Lee, H. Park, G. Serhat, H. Sun and K. J. Kuchenbecker, Calibrating a soft ERT-Based tactile sensor with a multiphysics model and sim-to-real transfer learning, 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, (2020), 1632-1638.
|
| [30] |
Z. Liang, B. Yin, X. Dai, J. Mo and S. Wang, Using camera calibration to apply digital image correlation outside the laboratory, Optical Engineering, 52 (2013), 123102.
doi: 10.1117/1.OE.52.12.123102.
|
| [31] |
C. Lieberman, K. Willcox and O. Ghattas, Parameter and state model reduction for large-scale statistical inverse problems, SIAM Journal on Scientific Computing, 32 (2010), 523-2542.
doi: 10.1137/090775622.
|
| [32] |
W. R. B. Lionheart, Conformal uniqueness results in anisotropic electrical impedance imaging, Inverse Problems, 13 (1997), 125-134.
doi: 10.1088/0266-5611/13/1/010.
|
| [33] |
D. Liu, V. Kolehmainen, S. Siltanen, A.-M. Laukkanen and A. Seppänen, Estimation of conductivity changes in a region of interest with electrical impedance tomography, Inverse Problems and Imaging, 9 (2015), 211-229.
doi: 10.3934/ipi.2015.9.211.
|
| [34] |
D. Liu, V. Kolehmainen, S. Siltanen and A. Seppänen, A nonlinear approach to difference imaginging EIT; assessment of the robustness in the presence of modelling errors, Inverse Problems, 31 (2015), 035012.
doi: 10.1088/0266-5611/31/3/035012.
|
| [35] |
A. Nissinen, V. Kolehmainen and J. Kaipio, Reconstruction of domain boundary and conductivity in electrical impedance tomography using the approximation error approach, International Journal for Uncertainty Quantification, 1 (2011), 203-222.
doi: 10.1615/Int.J.UncertaintyQuantification.v1.i3.20.
|
| [36] |
S. Nonn, M. Schagerl, Y. Zhao, S. Gschossmann and C. Kralovec, Application of electrical impedance tomography to an anisotropic carbon fiber-reinforced polymer composite laminate for damage localization, Composites Science and Technology, 160 (2018), 231-236.
doi: 10.1016/j.compscitech.2018.03.031.
|
| [37] |
C. C. Pain, J. V. Herwanger, J. H. Saunders, M. H. Worthington and C. R. E. deOliveira, Anisotropic resistivity inversion, Inverse Problems, 19 (2003), 1081-1111.
doi: 10.1088/0266-5611/19/5/306.
|
| [38] |
B. Pan, K. Qian, H. Xie and A. Asundi, Two-dimensional digital image correlation for inplane displacement and strain measurement: A review, Measurement Science and Technology, 20 (2009), 062001.
doi: 10.1088/0957-0233/20/6/062001.
|
| [39] |
B. Pan, L. Yu and D. Wu, High-accuracy 2D digital image correlation measurements using low-cost imaging lenses: implementation of a generalized compensation method, Measurement Science and Technology, 25 (2014), 025001.
doi: 10.1088/0957-0233/25/2/025001.
|
| [40] |
K. Park, S. Kim, H. Lee, I. Park and J. Kim, Low-hysteresis and low-interference soft tactile sensor using a conductive coated porous elastomer and a structure for interference reduction, Sensors and Actuators A, 295 (2019), 541-550.
doi: 10.1016/j.sna.2019.06.026.
|
| [41] |
K. Park, H. Park, H. Lee, S. Park and J. Kim, An ERT-based robotic Skin with sparsely distributed electrodes: Structure, fabrication, and DNN-based signal processing, 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, (2020), 1617-1624.
|
| [42] |
M. K. Pidcock, M. Kuzuoglu and K. Leblebicioglu, Analytic and semi-analytic solutions in electrical impedance tomography. I. Two-dimensional problems, Physiological Measurement, 16 (1995), 77-90.
doi: 10.1088/0967-3334/16/2/001.
|
| [43] |
M. K. Pidcock, M. Kuzuoglu and K. Leblebicioglu, Analytic and semi-analytic solutions in electrical impedance tomography. Ⅱ. Three-dimensional problems, Physiological Measurement, 16 (1995), 91-110.
doi: 10.1088/0967-3334/16/2/002.
|
| [44] |
R. Rashetnia, M. Hallaji, D. Smyl, A. Seppänen and M. Pour-Ghaz, Detection and localization of changes in two-dimensional temperature distributions by electrical resistance tomography, Smart Materials and Structures, 26 (2017), 115021.
doi: 10.1088/1361-665X/aa8f75.
|
| [45] |
H. Rocha, C. Fernandes, N. Ferreira, U. Lafont and J. P. Nunes, Damage localization on CFRP composites by electrical impedance tomography, Materials Today Communications, 32 (2022), 104164.
doi: 10.1016/j.mtcomm.2022.104164.
|
| [46] |
M. Sannamanni, J. Gao, W. W. Chen and T. N. Tallman, Damage detection in non-planar carbon fiber-reinforced polymer laminates via electrical impedance tomography with surface-mounted electrodes and directional sensitivity matrices, Composites Science and Technology, 224 (2022), 109429.
doi: 10.1016/j.compscitech.2022.109429.
|
| [47] |
A. Seppänen, M. Hallaji and M. Pour-Ghaz, A functionally layered sensing skin for the detection of corrosive elements and cracking, Structural Health Monitoring, 16 (2017), 215-224.
doi: 10.1177/1475921716670574.
|
| [48] |
D. Smyl, K.-N. Antin, D. Liu and S. Bossuyt, Coupled digital image correlation and quasi-static elasticity imaging of inhomogeneous orthotropic composite structures, Inverse Problems, 34 (2018), 124005.
doi: 10.1088/1361-6420/aae793.
|
| [49] |
D. Smyl, M. Hallaji, A. Seppänen and M. Pour-Ghaz, Quantitative electrical imaging of three-dimensional moisture flow in cement-based materials, International Journal of Heat and Mass Transfer, 103 (2016), 1348-1358.
doi: 10.1016/j.ijheatmasstransfer.2016.08.039.
|
| [50] |
D. Smyl, M. Hallaji, A. Seppänen and M. Pour-Ghaz, Three-dimensional electrical impedance tomography to monitor unsaturated moisture ingress in cement-based materials, Transport in Porous Media, 115 (2016), 101-124.
doi: 10.1007/s11242-016-0756-1.
|
| [51] |
D. Smyl, M. Pour-Ghaz and A. Seppänen, Detection and reconstruction of complex structural cracking patterns with electrical imaging, NDT and E International, 99 (2018), 123-133.
doi: 10.1016/j.ndteint.2018.06.004.
|
| [52] |
T. N. Tallman, S. Gungor and C. E. Bakis, On the inverse determination of displacements, strains, and stresses in a carbon nanofiber/polyurethane nanocomposite from conductivity data obtained via electrical impedance tomography, Journal of Intelligent Material Systems and Structures, 28 (2017), 2617-2629.
doi: 10.1177/1045389X17692053.
|
| [53] |
T. N. Tallman, L. Homa, T. Lesthaeghe, N. Schehl, M. Flores and J. Wertz, Detection of indentation damage in carbon fiber/epoxy composites via EIT during the application of bending loads, NDT and E International, 147 (2024), 103206.
|
| [54] |
T. N. Tallman and D. Smyl, Structural health and condition monitoring via electrical impedance tomography in self-sensing materials: A review, Smart Materials and Structures, 29 (2020), 123001.
doi: 10.1088/1361-665X/abb352.
|
| [55] |
Z. Tang, J. Liang, C. Guo and Y. Wang, Photogrammetry-based two-dimensional digital image correlation with nonperpendicular camera alignment, Optical Engineering, 51 (2012), 023602.
doi: 10.1117/1.OE.51.2.023602.
|
| [56] |
P. Vauhkonen, M. Vauhkonen, T. Savolainen and J. Kaipio, Three-dimensional electrical impedance tomography based on the complete electrode model, IEEE Transactions on Biomedical Engineering, 46 (1999), 1150-1160.
doi: 10.1109/10.784147.
|
| [57] |
J. Wagner, S. Gschossmann and M. Schagerl, On the capability of measuring actual strain values with electrical impedance tomography using planar silkscreen printed elastoresistive sensors, IEEE Sensors Journal, 99 (2020), 1.
|
| [58] |
N. Yasue, H. Naruse, J. Masuda, H. Kino, T. Nakamura and T. Yamaura, Concrete pipe strain measurement using optical fiber sensor, IEICE Transactions on Electronics, E83-C (2000), 468-474.
|
| [59] |
L. Yu and B. Pan, In-plane displacement and strain measurements using a camera phone and digital image correlation, Optical Engineering, 53 (2014), 054107.
doi: 10.1117/1.OE.53.5.054107.
|
| [60] |
Y. Zhao, Y. Liu, Y. Li and Q. Hao, Development and application of resistance strain force sensors, Sensors, 20 (2020), 5826.
doi: 10.3390/s20205826.
|
Left: schematic representation of the domain in the elasticity FEM simulation. The top edge of the domain was imposed a uniform displacement
The components
EIT FEM meshes used for modeling the synthetic data (left) and in inversion (right). The electrodes boundaries are marked in red and magenta dots
Results of simulation studies. True, synthetic distributions of
Simulation study; the larger noise realization case: Approximate posterior standard deviations corresponding to (from left to right)
Schematic illustrations of the plane strain experiment (left) and the contact pressure experiment (right). The numbering of the electrodes is indicated in red
Left: Photograph of the experimental setup in the strain experiment. The insulating rubber mat is brown and the dark square shaped area on top of it is formed by the conductive paint. Right: The region of interest (white) on which the strain was reconstructed using DIC, superimposed on the grayscale photograph of the sample
Plane strain experiment. DIC based reconstructions of the strain field components
Plane strain experiment. EIT reconstructions for the anisotropic conductivity multiplier field components
Plane strain experiment. Top row: EIT reconstructions of the isotropic initial conductivity
Left: the average values of
Left: Average value of
Reconstructions of the multiplier field
Contact pressure experiment. Top row: EIT reconstructions of the isotropic initial conductivity