Analysis of the Displacement Field of Soft Rock Samples During UCS Tests by Means of a Computer Vision Technique

  • Nunzio Luciano FazioEmail author
  • Marco Leo
  • Michele Perrotti
  • Piernicola Lollino
Original Paper


The measurement of rock sample displacement during laboratory testing is generally carried out by means of instrumental devices, which are capable of detecting average or local sample displacements, but are subjected to various error measurements; instrumental errors especially increase when localization takes place in the rock sample and macro-cracking develops. Photogrammetric techniques and, more recently, computer vision techniques based on non-contact digital image change detection propose an interesting alternative in this field, since they allow for detecting, with high precision, the visible displacement field of the rock sample external surface. This work is aimed at presenting the results of the application of an advanced computer vision technique to the assessment of the evolving displacement field of soft calcarenite samples subjected to uniaxial compression test. The corresponding results confirm that the technique is capable of detecting, with high level of accuracy, both the pre-failure displacement evolution, when continuity conditions still exist in the sample, and in the post-failure state, when large fissuring occur and a clear failure mechanism develops in the sample. A comparison between the results obtained from the technique here proposed and those resulting from a more conventional digital image correlation technique is also provided, highlighting a clear improvement in terms of accuracy of the images and capability of detecting the failure mechanisms of the samples.


Change detection Digital image Soft rock Failure Monitoring Precursory signs 



  1. Andriani GF, Walsh N (2007) The effects of wetting and drying, and marine salt crystallization on calcarenite rocks used as building material in historic monuments. In: Prikryl R, Smith BJ (eds) Building stone decay: from diagnosis to conservation. Geological Society, London, pp. 179–188 (Special Publications, v. 271) Google Scholar
  2. Andriani GF, Walsh N (2010) Petrophysical and mechanical properties of soft and porous building rocks used in Apulian monuments (south Italy). Geol Soc Lond Spec Publ 333:129–141CrossRefGoogle Scholar
  3. Birgisson B, Bloomquist D, McVay M (2009) Devices, systems, and methods for measuring and controlling compactive effort delivered to a soil by a compaction unit. US PatentGoogle Scholar
  4. Bomarito GF, Hochhalter JD, Ruggles TJ, Cannon AH (2017) Increasing accuracy and precision of digital image correlation through pattern optimization. Opt Lasers Eng 91:73–85CrossRefGoogle Scholar
  5. Chu TW, Su SF, Chen MC, Xu SSD, Hwang KS (2016) Edge enhanced SIFT for moving object detection. In: 3rd international conference on IEEE informative and cybernetics for computational social systems (ICCSS), 11–14Google Scholar
  6. Ciantia MO, Castellanza R, Di Prisco C (2015) Experimental study on the water-induced weakening of calcarenites. Rock Mech Rock Eng 48:441–461CrossRefGoogle Scholar
  7. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24:381–395 (06/1981) CrossRefGoogle Scholar
  8. Gorodetskyi O, Hütter M, Geers MG (2017) Detecting precursors of localization by strain-field analysis. Mech Mater 110:84–97CrossRefGoogle Scholar
  9. Hall SA, De Sanctis F, Viggiani G (2006) Monitoring fracture propagation in a soft rock (Neapolitan Tuff) using acoustic emissions and digital images. Pure Appl Geophys 163(10):2171–2204CrossRefGoogle Scholar
  10. Hartmann C, Wang J, Opristescu D, Volk W (2018) Implementation and evaluation of optical flow methods for two-dimensional deformation measurement in comparison to digital image correlation. Opt Lasers Eng 107:127–141CrossRefGoogle Scholar
  11. Hudson JA, Brown ET, Fairhurst C (1971) Optimizing the control of rock failure in servo-controlled laboratory tests. Rock Mech 3:217–224CrossRefGoogle Scholar
  12. ISRM (1981) Suggested methods for monitoring rock movements using inclinometers and tiltmeters. Rock characterization, testing and monitoring, ISRM suggested methods. Pergamon Press, Oxford, pp 187–199Google Scholar
  13. ISRM (1984) Suggested methods for surface monitoring of movements across discontinuities. ISRM Commission on standardization of laboratory and field tests. Int J Rock Mech Min Sci Geomech Abstr 21(5):265–276Google Scholar
  14. ISRM (2015) Suggested methods for rock characterization, testing and monitoring, 2007–2014. R. Ulusay ed., Springer, New York, p 280Google Scholar
  15. ISRM (International Society for Rock Mechanics) (1978) Suggested methods for the quantitative description of discontinuities in rock masses. Int J Rock Mech Min Sci Geomech Abstr 15:319–368CrossRefGoogle Scholar
  16. ISRM (International Society for Rock Mechanics) (1979) Suggested methods for determining the uniaxial compressive strength and deformability of rock materials. Int J Rock Mech Min Sci 16(2):135–140Google Scholar
  17. Leo M, Del Coco M, Carcagni P, Spagnolo P, Mazzeo PL, Distante C, Zecca R (2018) Automatic visual monitoring of welding procedure in stainless steel kegs. Opt Lasers Eng 104:220–231CrossRefGoogle Scholar
  18. Lingua A, Marenchino D, Francesco, Nex (2009) Performance analysis of the SIFT operator for automatic feature extraction and matching in photogrammetric applications. Sensors 9(5):3745–3766CrossRefGoogle Scholar
  19. Liu C, Yuen J, Torralba A (2011a) Sift ow: dense correspondence across scenes and its applications. IEEE Trans Pattern Anal Mach Intell 33(5):978–994CrossRefGoogle Scholar
  20. Liu C, Yuen J, Torralba A (2011b) Sift flow: Dense correspondence across scenes and its applications. IEEE Trans Pattern Anal Mach Intell 33(5):978–994CrossRefGoogle Scholar
  21. Lollino P, Andriani GF (2017) Role of brittle behaviour of soft calcarenites under low confinement: laboratory observations and numerical investigation. Rock Mech Rock Eng 50(7):1863–1882CrossRefGoogle Scholar
  22. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110CrossRefGoogle Scholar
  23. May M, Turner MJ, Morris T (2010) Scale invariant feature transform: a graphical parameter analysis. In: Proceedings of the British Machine Vision Conference (BMVC) 2010 UK postgraduate workshop. BMVA Press, UKGoogle Scholar
  24. Mazzeo PL, Leo M, Spagnolo P, Nitti M (2012) Soccer ball detection by comparing different feature extraction methodologies. Adv Artif Intell 12:1687–7470Google Scholar
  25. Mazzeo PL, Spagnolo P, Leo M, Carcagnì P, Del Coco M, Distante C (2017) Dense descriptor for visual tracking and robust update model strategy. J Ambient Intell Hum Comput. Google Scholar
  26. Meng F, Zhou H, Zhang C, Xu R, Lu J (2015) Evaluation methodology of brittleness of rock based on post-peak stress-strain curves. Rock Mech Rock Eng 48:1787–1805CrossRefGoogle Scholar
  27. Micheletti N, Lambiel C, Lane SN (2015) Investigating decadel-scale geomorphic dynamics in an alpine mountain setting. J Geophys Res Earth Surf 120:2155–2175. CrossRefGoogle Scholar
  28. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630CrossRefGoogle Scholar
  29. Munoz H, Taheri A, Chanda EK (2016) Pre-peak and post-peak rock strain characteristics during uniaxial compression by 3D digital image correlation. Rock Mech Rock Eng. Google Scholar
  30. Passieux JC, Navarro P, Périé JN, Marguet S, Ferrero JF (2014) A digital image correlation method for tracking planar motions of rigid spheres: application to medium velocity impacts. Exp Mech Soc Exp Mech 54(8):1453–1466CrossRefGoogle Scholar
  31. Passieux JC, Bugarin F, David C, Périé JN, Robert L (2015) Multiscale displacement field measurement using digital image correlation: application to the identification of elastic properties. Exp Mech 55(1):121–137CrossRefGoogle Scholar
  32. Rechenmacher AL, Abedi S, Chupin O, Orlando AD (2011) Characterization of mesoscale instabilities in localized granular shear using digital image correlation. Acta Geotech 6:205–217CrossRefGoogle Scholar
  33. Roncella R, Forlani G (2015) A fixed terrestrial photogrammetric system for landslide monitoring. In: Scaioni M (ed) Modern technologies for landslide monitoring and prediction. Springer, Berlin, Heidelberg, pp 43–65. CrossRefGoogle Scholar
  34. Roncella R, Romeo E, Barazzetti L, Gianinetto M, Scaioni M (2012) Comparative analysis of digital image correlation techniques for in-plane displacement measurements. In: 5th international congress on image and signal processing (CISP 2012). IEEE, pp 721–726Google Scholar
  35. Schmidt T, Newcombe R, Fox D (2017) Self-supervised visual descriptor learning for dense correspondence. IEEE Robot Autom Lett 2(2):420–427CrossRefGoogle Scholar
  36. Sima AA, Simon J, Buckley (2013) Optimizing SIFT for matching of short wave infrared and visible wavelength images. Remote Sens 5:2037–2056CrossRefGoogle Scholar
  37. Stanier SA, Blaber J, Take WA, White DJ (2015) Improved image-based deformation measurement for geotechnical applications. Can Geotech J 53(5):727–739CrossRefGoogle Scholar
  38. Sutton MA, Orteu JJ, Schreier H (2009) Image correlation for shape, motion and deformation measurements: basic concepts, theory and applications. Springer Science & Business Media, New YorkGoogle Scholar
  39. Viggiani G, Hall SA (2008) Full-field measurements, a new tool for laboratory experimental geomechanics. In: Burns SE, Mayne PW, Santamarina JC (eds) Proceedings of the 4th international symposium deformation characteristics geomaterials. IOS Press; 1, pp 3–26Google Scholar
  40. Wawersik WR, Fairhurst CA (1970) A study of brittle rock fracture in laboratory compression experiments. Int J Rock Mech Min Sci 7:561–575CrossRefGoogle Scholar
  41. Westoby MJ, Brasington J, Glasser NF, Hambrey MJ, Reynolds JM (2012) “Structure-from-Motion” photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 179:300 314CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CNR-IRPIBariItaly
  2. 2.CNR-ISASILecceItaly

Personalised recommendations