Skip to main content
Log in

Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The detection of damage at an early stage that affects the supporting element of civil structures proves to be very significant to save invaluable human life and valuable possessions. In this research work, the severity of cracks in the supporting column is assessed using a new technique. This piece of research study uses the soft computing method of fuzzy cognitive map (FCM) to model the domain experts’ knowledge and the knowledge assimilated through relevant literature to grade the severity of cracks in supporting column. The FCM grading model is further improved by using the Hebbian learning algorithms. The presented work demonstrates the classification and prediction capabilities of FCM for the respective structural health monitoring application, using two well-known and efficient FCM learning approaches viz. nonlinear Hebbian learning (NHL) and data-driven nonlinear Hebbian learning (DD-NHL). The proposed crack severity grading model classifies the cracks in supporting column into three categories, namely fine crack, moderate crack and severe crack. The proposed model uses DD-NHL algorithm. DD-NHL is trained with 70 records and tested with 30 records and gives an overall classification accuracy of 96 %. The obtained results are better compared to other popular machine learning-based classifiers. The proposed method helps even the non-experts to find the possible causes of crack and reports them to structural engineers, to start maintenance in an appropriate stage, using various crack control techniques. Also, a software tool for crack categorization was developed based on the FCM method and its learning capabilities. Thus, it is easier for the users/civil engineers to use this software to make decisions in civil engineering domain and improve their knowledge about the health of the structure.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Architectural Institute of Korea (1997) Repair and rehabilitation of concrete structures. AIK, Korea

    Google Scholar 

  2. Arthi K, Tamilarasi A, Papageorgiou EI (2011) Analyzing the performance of fuzzy cognitive maps with nonlinear Hebbian learning algorithm in predicting autistic disorder. Expert Syst Appl 38(3):1282–1292

    Article  Google Scholar 

  3. Beena P, Ganguli R (2011) Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl Soft Comput 11(1):1014–1020

    Article  Google Scholar 

  4. Chao CJ, Cheng FP (1998) Fuzzy pattern recognition model for diagnosing cracks in RC structures. J Comput Civil Eng ASCE 12(2):111–119

    Article  Google Scholar 

  5. Cohen SCM, De Castro LN (2006) Data clustering with particle swarms. IEEE congress on evolutionary computation, Vancouver, BC, Canada

  6. Central Public Works Department (2002) Handbook and Repairs and Rehabilitation of RCC Buildings. Govt. of India, New Delhi

  7. Centre for Advanced Maintainance Technology (2004) Cracks in buildings—causes and prevention. Govt. of India

  8. Farrar CR, Worden K (2007) An introduction to structural health monitoring. Philos Trans R Soc A: Math Phys Eng Sci 365(1851):303–315

    Article  Google Scholar 

  9. Groumpos P, PandStylios CD (2000) Modeling supervisory control systems using fuzzy cognitive maps. Chaos Solitons Fractals 11(1–3):329–336

    Article  MathSciNet  MATH  Google Scholar 

  10. Japanese Concrete Institute (1980) Investigation of concrete cracks and guide to repair and rehabilitation. JCI, Hiroshima

    Google Scholar 

  11. Jayashree S, Akila K, Papageorgiou EI, Papandrianos N, Vasukie A (2015) An integrated breast cancer risk assessment and risk management model based on fuzzy cognitive maps. Comput Methods Prog Bio-med 118(3):280–297

    Article  Google Scholar 

  12. Jayashree LS, Palakkal N, Papageorgiou EI, Papageorgiou K (2015) Application of fuzzy cognitive maps for coconut yield management in Malabar region of Southern India. Neural Comput Appl 26(8):1963–1978. doi:10.1007/s00521-015-1864-5

    Article  Google Scholar 

  13. Kim YM, Kim CK, Lee JC (2009) Rough set algorithm for crack category determination of reinforced concrete structures. Adv Eng Softw 40(3):202–211

    Article  MATH  Google Scholar 

  14. Kim YM, Kim CK, Hong S (2006) Fuzzy based state assessment for reinforced concrete building structures. Eng Struct 28(9):1286–1297

    Article  Google Scholar 

  15. Kim YM, Kim CK, Hong SG (2007) Fuzzy set based crack diagnosis system for reinforced concrete structures. Comput Struct 85:1828–1844

    Article  Google Scholar 

  16. Kosko B (1992) Fuzzy systems as universal approximators. In: Proceedings of IEEE international conference on fuzzy systems, San Diego, pp 1153–1162, March 1992

  17. Lee KC, Kim JS, Chung NH, Kwon SJ (2002) Fuzzy cognitive map approach to web mining inference amplification. Expert Syst Appl 22:197–211

    Article  Google Scholar 

  18. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18

    Article  Google Scholar 

  19. Nawy EG (2001) Fundamentals of high-performance concrete, 2nd edn. Wiley, New Jersey

    Google Scholar 

  20. Nilsson AH, Winter G (1985) Design of concrete structures. McGraw Hill, New York

    Google Scholar 

  21. Motlagh O, Tang SH, Ramli AR, Nakhaeinia D (2012) A FCM modeling for using a priori knowledge: application study in modeling quadruped walking. Neural Comput Appl 21(5):1007–1015

    Article  Google Scholar 

  22. Palaez E, Bowles JB (1996) Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Inf Sci 88:177–199

    Article  Google Scholar 

  23. Papageorgiou EI (2012) Learning algorithm for fuzzy cognitive maps- a review study. IEEE Trans Syst Man Cybern (SMC) Part C 42(2):150–163

    Article  Google Scholar 

  24. Papageorgiou EI, Iakovidis D (2013) Intuitionistic fuzzy cognitive maps. IEEE Trans Fuzzy Syst 21(2):342–354

    Article  Google Scholar 

  25. Papageorgiou EI, Jayashree S, Akila K, Papandrianos N (2015) A risk management model for familial breast cancer: a new application using fuzzy cognitive map method. Comput Methods Program Bio-med 122(2):123–135

    Article  Google Scholar 

  26. Papakostas GA, Polydoros AS, Koulouriotis DE, Tourassis VD (2011) Training fuzzy cognitive maps by using Hebbian learning algorithms: a comparative study. IEEE international conference on fuzzy systems (FUZZ-IEEE 2011), 27–30 June 2011, Taipei, Taiwan, pp 851–858

  27. Papageorgiou EI, Stylios CD, Groumpos PP (2003) Fuzzy cognitive map learning based on nonlinear Hebbian rule. In: Gedeon TD, Fung LCC (eds) Lecture notes in artificial intelligence, vol 2903. Springer, New York, pp 254–266

    Google Scholar 

  28. Parsopoulos KE, Papageorgiou EI, Groumpos PP, Vrahatis MN (2003) A first study of fuzzy cognitive maps learning using particle swarm optimization. In: Proceedings of the IEEE 2003 congress on evolutionary computation, pp 1440–1447

  29. Petalas EI Papageorgiou, Parsopoulos KE, Groumpos PP, Vrahatis MN (2005) Fuzzy cognitive maps learning using memetic algorithms. Lect Ser Comput Comput Sci 1:1–4

    MATH  Google Scholar 

  30. Stylios CD, Georgopoulos VC, Malandraki GA, Chouliara S (2008) Fuzzy cognitive map architecture for medical decision support systems. Appl Soft Comput 8(3):1243–1251

    Article  Google Scholar 

  31. Taber WR (1991) Knowledge processing with fuzzy cognitive maps. Expert Syst Appl 2(1):83–87

    Article  Google Scholar 

  32. Voula CG, Malandrakia GA, Stylios CD (2002) A fuzzy cognitive map approach to differential diagnosis of specific language impairment. Artif Intell Med 29(3):261–278

    Google Scholar 

  33. Wojciech S, Lukasz K, Witold Pedrycz A (2008) Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps. IEEE international conference on fuzzy systems, FUZZ-IEEE, pp 1975–1981

  34. Wojciech S, Lukasz K, Witold Pedrycz A, Reofrmat M (2005) Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst 153:371–401

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vijayalakshmi Senniappan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Senniappan, V., Subramanian, J., Papageorgiou, E.I. et al. Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures. Neural Comput & Applic 28 (Suppl 1), 107–117 (2017). https://doi.org/10.1007/s00521-016-2313-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2313-9

Keywords

Navigation