Advertisement

Neural Computing and Applications

, Volume 24, Issue 7–8, pp 1815–1821 | Cite as

Quantification of biofilm on flooring surface using image classification technique

  • S. A. QuadriEmail author
  • Othman Sidek
Original Article

Abstract

The deteriogenic biofilms on the outdoor-exposed surfaces of concrete structures impair the structural integrity and esthetic quality. There is a growing need for precise and reliable methods to assess the bio-deterioration in concrete structures and identify the extent of biodeposition. This study proposes statistical image analysis method and application of the neural network classification approach to quantify bio-depositories on flooring surface that is exposed to outdoor environment. The results yield percentages of various bio-depositories on the sample.

Keywords

Concrete Algae Image classification Quantification 

References

  1. 1.
    Higgins DD (1982) Removal of stains and growths from concrete. Appearance Matters 5:1–11Google Scholar
  2. 2.
    Verhoef LGW (1988) Soiling and cleaning of building facades, report of the technical committee 62 SCF, RILEM. Chapman and Hall, LondonGoogle Scholar
  3. 3.
    Wee YC, Lee KB (1980) Proliferation of algae on surfaces of buildings in Singapore. Int Biodeter Bull 16(4):113–117Google Scholar
  4. 4.
    Ryan NM (1983) Algal growth on cementitious surfaces, Pilot study of the National Institute for Physical Planning and Construction Research. In: Hopton JW, Hill EC (eds) Industrial microbiological testing, vol 23, 1st edn. Wiley-Blackwell, London, pp 55–65Google Scholar
  5. 5.
    Stoecker JG (2001) A practical manual on microbiologically influenced corrosion, 2nd ed., vol. 2, NACE International, Houston, Texas, USA, pp 4.1–4.10Google Scholar
  6. 6.
    Ribas SM, Pinheiro SMM (2007) Mitigation of concrete structures submitted to biodeterioration. MIC—an international perspective symposium, Perth, Australia, pp 1–6Google Scholar
  7. 7.
    Maruthamuthu S, Muthukumar N, Natesan M, Palaniswamy N (2008) Role of air microbes on atmospheric corrosion. Curr Sci 94(3):359–363Google Scholar
  8. 8.
    Javaherdashti R (2009) A brief review of general patterns of MIC of carbon steel and biodegradation of concrete. Istanbul Univ Fac Sci (IUFS) J 68(2):65–73Google Scholar
  9. 9.
    Suseela MR, Toppo K (2007) Algal biofilms on polythene and its possible degradation. Curr Sci 92(3):285–287Google Scholar
  10. 10.
    Gaylarde C, Morton LHG (1999) Deteriogenic biofilms on buildings and their control: a review. Biofouling 14(1):59–74CrossRefGoogle Scholar
  11. 11.
    Dubosc A, Escadeillas G, Blanc PJ (2001) Characterization of biological stains on external concrete walls and influence of concrete as underlying material. Cem Concr Res 31(11):1613–1617CrossRefGoogle Scholar
  12. 12.
    Guillitte O, Dreesen R (1995) Laboratory chamber studies and petrographical analysis as bioreceptivity assessment tools of building materials. Sci Total Environ 167:365–374CrossRefGoogle Scholar
  13. 13.
    Barberousse H, Ruot B, Yepremian C, Boulon G (2007) An assessment of facade coatings against colonization by aerial algae and cyanobacteria. Build Environ 42(7):2555–2561CrossRefGoogle Scholar
  14. 14.
    Kabir S (2010) Imaging-based detection of AAR induced map-crack damage in concrete structure. NDT&E Int 43(6):461–469CrossRefGoogle Scholar
  15. 15.
    Solomon C, Breckon T (2011) Fundamentals of digital image processing: a practical approach with examples in Matlab, 2nd ed., Chap 11. Wiley, UK, pp 297–328Google Scholar
  16. 16.
    Jovanic PB (2005) Quantification of visual information. In: Hsu J-P, Aleksandar MS (eds) Finely dispersed particles micro-, nano-, and atto-engineering, 2nd ed. CRC Press, Boca Raton, pp 341–364Google Scholar
  17. 17.
    Canty MJ (2010) Image analysis, classification and change detection in remote sensing, with algorithms for ENVI/IDL, 2nd edn. CRC Press, Boca Raton, FLGoogle Scholar
  18. 18.
    Javaherdashti R, Nikraz H, Borowitzka M, Moheimani N, Olivia M (2009) On the impact of algae on accelerating the biodeterioration/biocorrosion of reinforced concrete: a mechanistic review. Eur J Sci Res 36(3):394–406Google Scholar
  19. 19.
    Prieto B, Silva B, Lantes O (2004) Biofilm quantification on stone surfaces: comparison of various methods. Sci Total Environ 333(1):1–7CrossRefGoogle Scholar
  20. 20.
    Escadeillas G, Bertron A, Ringot E, Blanc P, Dubosc A (2008) Accelerated testing of biological stain growth on external concrete walls, Part 2: quantification of growths. Mater Struct 42(7):937–945CrossRefGoogle Scholar
  21. 21.
    Muynck WD, Ramirez AM, Belie ND, Willy W (2009) Evaluation of strategies to prevent algal fouling on white architectural and cellular concrete. Int Biodeterior Biodegrad 63(6):679–689CrossRefGoogle Scholar
  22. 22.
    Rasband WS (1997–2012) ImageJ. Online at http://rsb.info.nih.gov/ij/
  23. 23.
    Dagmar K, Torsten G, Dieter R, Axel S (2007) Quantification of microorganisms involved in cemented layer formation in sulfide mine waste tailings. Adv Mater Res 20:481–484Google Scholar
  24. 24.
    Dagmar K, Axel S (2008) Quantitative microbial community analysis of three different sulfide mine tailing dumps generating acid mine drainage. Appl Environ Microbiol 74(16):5211–5219CrossRefGoogle Scholar
  25. 25.
    Hermes research project, Handbook of Methods for Microbial Ecology, 2005 Edition, HERMES: HampshireGoogle Scholar
  26. 26.
    Katherine DM, April ZG, Robert N, Largus TA (2007) Molecular methods in biological systems. Water Environ Res 79(10):1109–1151CrossRefGoogle Scholar
  27. 27.
    Sammon NB, Harrower KM, Fabbro LD, Reed RH (2011) Three potential sources of micro fungi in a treated municipal water supply system in sub-tropical Australia. Int J Environ Res Public Health 8:713–732CrossRefGoogle Scholar
  28. 28.
    Boniecki P, Dach J, Nowakowski K (2009). Neural image analysis of maturity stage during composting of sewage sludge, In: Proceedings of international conference on digital image processing (ICDIP 2009), Thailand, pp 200–203Google Scholar
  29. 29.
    Nowakowski K, Boniecki P, Tomczak RL (2011) Identification process of corn and barley kernels damages using neural image analysis. In: Proceedings of SPIE volume of the third international conference on digital image processing (ICDIP 2011), China. p 8009, 80090CGoogle Scholar
  30. 30.
    Cerbin S, Nowakowski K, Dach J, Boniecki P, Robert Tomczak, Lewicki A (2012) Possibilities of neural image analysis implementation in monitoring of microalgae production as a substrate for biogas plant. In: Proceeding of SPIE 8334 of the fourth international conference on digital image processing (ICDIP 2012), p 83342AGoogle Scholar
  31. 31.
    Chuanwu XI, Wu J (2012) Control of biofilm formation. US patent number: 20120052052, http://www.freepatentsonline.com/y2012/0052052.html
  32. 32.
  33. 33.
    Salem A, Kabir S, Musbah A (2011) Optical image analysis based concrete damage detection. In: Proceedings of the progress in electromagnetic research symposium, Marrakesh, pp 1–4Google Scholar
  34. 34.
    Berson A (1997) Imaging structure and function in medicine and biology, In: Thompson DO, Chimenti DE (eds) Review of progress in quantitative non destructive evaluation, vol 16, Plenum Press, New YorkGoogle Scholar
  35. 35.
    Menahem F, Abraham K (1999) Introduction to pattern recognition: statistical, structural, neural, and fuzzy logic approaches. World Scientific press, UK, pp 55–68Google Scholar
  36. 36.
  37. 37.
  38. 38.
  39. 39.
    Fung T, Ledrew E (1988) The determination of optimal threshold levels for change detection using various accuracy indices. Photogr Eng Remote Sens 54(10):1449–1454Google Scholar
  40. 40.
    Karathanassi V, Iossifidis CH, Rokos D (2000) A texture-based classification method for classifying built areas according to their density. Int J Remote Sens 21(9):1807–1823CrossRefGoogle Scholar
  41. 41.
    Goncalves LM, Fonte CC, Julio EN, Caetano M (2009) Evaluation of remote sensing images classifiers with uncertainty measures. In: Rodolphe D, Helen G (eds) Spatial data quality from process to decisions. CRC Press, Boca Raton, pp 163–177Google Scholar
  42. 42.
    Hand DJ (2006) Classifier technology and the illusion of progress. Stat Sci 21(1):1–14MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Collaborative Microelectronic Design Excellence Centre (CEDEC)Universiti Sains MalaysiaPulau PinangMalaysia

Personalised recommendations