Skip to main content
SpringerLink
Log in

Image-Based Tomography of Structures to Detect Internal Abnormalities Using Inverse Approach

  • Research paper
  • Published:
Experimental Techniques Aims and scope Submit manuscript

Abstract

Image-based techniques have been extensively deployed in the fields of condition assessment and structural mechanics to measure surface effects such as displacements or strains under loading. 3D Digital Image Correlation (3D-DIC) is a technique frequently used to quantify full-field strain measurements. This research uses 3D-DIC to detect interior anomalies of structural components, inferred from the discrepancy in constitutive properties such as elasticity modulus distribution of a three-dimensional heterogeneous/homogeneous sample using limited full-field boundary measurements. The proposed technique is an image-based tomography approach for structural identification (St-Id) to recover unseen volumetric defect distributions within the interior of a 3D heterogeneous space of a structural component based on iterative updating of unknown or uncertain model parameters. The approach leverages full-field surface deformation measurements as ground truth coupled with a finite element model updating process that leverages a novel hybridized optimization algorithm for convergence. This paper presents a case study on a series of structural test specimens with artificial damage. A computer program was created to provide an automated iterative interface between the finite element model and an optimization package. Results of the study illustrated the successful convergence of the selected objective function and the identified elasticity modulus distributions. The resulting updated model at later stages of loading was also shown to correlate well with the ground truth experimental response. The results illustrate the potential to detect subsurface defects from surface observations and to characterize internal properties of materials from their observed mechanical surface response.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Alfirević I (1995) Strength of materials. I. Tehnička knjiga. ISBN 953-172-010-X

  2. Drucker DC (1967) Introduction to mechanics of deformable solids. McGraw-Hill

  3. Timoshenko S (1976) Strength of materials, 3rd edition. Krieger Publishing Company, ISBN 0-88275-420-3

  4. ASTM International (2016) Standard test methods for tension testing of metallic materials, ASTM E8 / E8M-16a. West Conshohocken, Pennsylvania

  5. Li H, Weiss W, Medved M, Abe H, Newstead GM, Karczmar GS, Giger ML (2016) Breast density estimation from high spectral and spatial resolution. MRI J Med Imaging 3, 044507

  6. Mei Y, Goenezen S (2015) Spatially weighted objective function to solve the inverse problem in elasticity for the elastic property distribution. In: Doyle B, Miller K, Wittek A, Nielsen PMF (eds) Computational biomechanics for medicine: new approaches and new applications. Springer, New York

    Google Scholar 

  7. Mei, Y.; Goenezen, S. Parameter identification via a modified constrained minimization procedure. In Proceedings of the 24th International Congress of Theoretical and Applied Mechanics, Montreal, QC, Canada, 21–26 August 2016

  8. Mei Y, Kuznetsov S, Goenezen S. Reduced boundary sensitivity and improved contrast of the regularized inverse problem solution in elasticity. J Appl Mech 2015, 83

  9. Bayat M, Denis M, Gregory A, Mehrmohammadi M, Kumar V, Meixner D, Fazzio RT, Fatemi M, Alizad A (2017) Diagnostic features of quantitative comb-push shear elastography for breast lesion differentiation. PLoS One 12:e0172801

    Article  Google Scholar 

  10. Bayat M, Ghosh S, Alizad A, Aquino W, Fatemi M (2016) A generalized reconstruction framework for transient elastography. J Acoust Soc Am 139:2028

    Google Scholar 

  11. Wang M, Dutta D, Kim K, Brigham JC (2015) A computationally efficient approach for inverse material characterization combining Gappy POD with direct inversion. Comput Methods Appl Mech Eng 286:373–393

    Article  Google Scholar 

  12. Bay BK, Smith TS, Fyhrie DP, Saad M (1999) Digital volume correlation: three-dimensional strain mapping using X-ray tomography. Exp Mech 39(3):217–226

    Article  Google Scholar 

  13. Smith TS, Bay BK, Rashid MM (2002) Digital volume correlation including rotational degrees of freedom during minimization. Exp Mech 42(3):272–278

    Article  Google Scholar 

  14. Gates M, Lambros J, Heath MT (2011) Towards high performance digital volume correlation. Exp Mech 51(4):491–507

    Article  Google Scholar 

  15. Franck C, Hong S, Maskarinec SA, Tirrell DA, Ravichandran G (2007) Three-dimensional full-field measurements of large deformations in soft materials using confocal microscopy and digital volume correlation. Exp Mech 47(3):427–438

    Article  Google Scholar 

  16. Germaneau A, Doumalin P, Dupre J-C (2007) 3D strain field ´ measurement by correlation of volume images using scattered light: recording of images and choice of marks. Strain 43(3):207–218

    Article  Google Scholar 

  17. Roux S, Hild F, Viot P, Bernard D (2008) Three-dimensional image correlation from x-ray computed tomography of solid foam. Compos Part A-Appl S 39(8):1253–1265

    Article  Google Scholar 

  18. Yang Z et al (2017) In-situ X-ray computed tomography characterisation of 3D fracture evolution and image-based numerical homogenisation of concrete. Cem Concr Compos 75:74–83

    Article  CAS  Google Scholar 

  19. Mei Y, Goenezen S. "Non-Destructive characterization of heterogeneous solids from limited surface measurements." 24th International Congress of Theoretical and Applied Mechanics, Montreal, QC, Canada, Aug. 2016

  20. Mei Y et al (2016) Estimating the non-homogeneous elastic modulus distribution from surface deformations. Int J Solids Struct 83:73–80

    Article  Google Scholar 

  21. Mei Y, et al. "Mechanics based tomography: a preliminary feasibility study." Sensors 17.5 (2017): 1075

  22. Luo P et al (2018) Characterization of the stiffness distribution in two- and three-dimensions using boundary deformations: a preliminary study. MRS Communications 8(3):893–902

    Article  CAS  Google Scholar 

  23. Peddle J, Goudreau A, Carson E et al (2011) Bridge displacement measurement through digital image correlation. Bridge Struct 7(4):165–173

    Article  Google Scholar 

  24. Citto C, Shan IW, Willam K, Schuller MP (2011) In-place evaluation of masonry shear behavior using digital image analysis. ACI Mater J, 108(4)

  25. Murray CA, Hoag A, Hoult NA (2015) Take WA (2014) field monitoring of a bridge using digital image correlation. Proceedings of the ICE - Bridge Engineering 168(1):3–12

    Google Scholar 

  26. Vaghefi K, Ahlborn TM, Harris DK, Brooks CN (2015) Combined imaging Technologies for Concrete Bridge Deck Condition Assessment. J Perform Constr Facil 29(4):04014102

    Article  Google Scholar 

  27. Chemisky Y, Meraghni F, Bourgeois N, Cornell S, Echchorfi R, Patoor E (2015) Analysis of the deformation paths and thermomechanical parameter identification of a shape memory alloy using digital image correlation over heterogeneous tests. Int J Mech Sci 96:13–24

    Article  Google Scholar 

  28. Coppieters S, Hakoyama T, Debruyne D, Takahashi S, Kuwabara T (2018). Inverse yield locus identification of sheet metal using a complex cruciform in biaxial tension and digital image correlation. In multidisciplinary digital publishing institute proceedings (Vol. 2, no. 8, p. 382)

  29. Blaber J, Adair B, Antoniou A (2015) Ncorr: open-source 2D digital image correlation MATLAB software. Exp Mech 55(6):1105–1122

    Article  Google Scholar 

  30. Lionello G, Cristofolini L (2014) A practical approach to optimizing the preparation of speckle patterns for digital-image correlation. Meas Sci Technol 25(10):107001

    Article  Google Scholar 

  31. Dizaji MS, Alipour M, Harris D (2018) Leveraging full-field measurement from 3D digital image correlation for structural identification. Exp Mech:1–18

  32. Dizaji MS, Harris D, Alipour M, Ozbulut O. En “vision” ing a novel approach for structural health monitoring–a model for full-field structural identification using 3D–digital image correlation. The 8th international conference on structural health monitoring of intelligent infrastructure, Bridbane, Australia2017. p. 5–8

  33. Dizaji MS, Alipour M, Harris DK (2017) Leveraging vision for structural identification: a digital image correlation based approach. Springer, International Digital Imaging Correlation Society, pp 121–124

    Google Scholar 

  34. Dizaji MS, Harris DK, Alipour M, Ozbulut OE. Reframing measurement for structural health monitoring: a full-field strategy for structural identification. Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII: International Society for Optics and Photonics; 2018. p. 1059910

  35. Correlated Solutions Inc (2005) VIC-3D user manual. Correlated Solutions, Columbia

    Google Scholar 

  36. Abaqus Inc (2018) ABAQUS theory manual, version 6.6. Simulia, Providence

  37. An introduction to script writing in ABAQUS software, http://bertoldi.seas.harvard.edu/files/bertoldi/files/learnabaqusscriptinonehour.pdf

  38. Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning. Addison-Wesley, Reading

    Google Scholar 

  39. Jung DS, Kim CY (2013) Finite element model updating of a simply supported skewed PSC I-girder bridge using hybrid genetic algorithm. KSCE J Civ Eng 17(3):518–529

    Article  Google Scholar 

  40. Joghataie A, Farrokh M (2008) Dynamic analysis of non-linear frames by Prandtl neural networks. J Eng Mech 134(11):961–969

    Article  Google Scholar 

  41. Farrokh M, Shafiei Dizaji M, Joghataie A (2015) Modeling hysteretic deteriorating behavior using generalized Prandtl neural network. J Eng Mech 141(8):040150

    Article  Google Scholar 

  42. Ghouati O, Gelin JC (1997) An inverse approach for the identification of complex material behaviours. In: Sol H, Oomens CWJ (eds) Material identification using mixed numerical experimental methods. Kluwer Academic Publishers, Dordrecht, pp 93–102

    Chapter  Google Scholar 

  43. Meuwissen M, Oomens C, Baaijens F, Petterson R, Janssen J (1997) Determination of parameters in elasto-plastic models of aluminium. In: Sol H, Oomens CWJ (eds) Material identification using mixed numerical experimental methods. Kluwer Academic Publishers, Dordrecht, pp 71–80

    Chapter  Google Scholar 

  44. LeBlanc B, Niezrecki C, Avitabile P, Chen J, Sherwood J (2013) Damage detection and full surface characterization of a wind turbine blade using three-dimensional digital image correlation. Struct Health Monit 12(5–6):430–439

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Massachusetts Lowell, Lowell university Ave, Lowell, MA, 01854, USA

    M. Shafiei Dizaji

  2. Department of Engineering Systems and Environment, University of Virginia, 151 Engineer’s Way, Charlottesville, VA, 22904, USA

    M. Alipour & D.K. Harris

Authors
  1. M. Shafiei Dizaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Alipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D.K. Harris
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Shafiei Dizaji.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shafiei Dizaji, M., Alipour, M. & Harris, D. Image-Based Tomography of Structures to Detect Internal Abnormalities Using Inverse Approach. Exp Tech 46, 257–272 (2022). https://doi.org/10.1007/s40799-021-00479-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40799-021-00479-9

Keywords

Search

Navigation