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.
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Architectural Institute of Korea (1997) Repair and rehabilitation of concrete structures. AIK, Korea
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
Beena P, Ganguli R (2011) Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl Soft Comput 11(1):1014–1020
Chao CJ, Cheng FP (1998) Fuzzy pattern recognition model for diagnosing cracks in RC structures. J Comput Civil Eng ASCE 12(2):111–119
Cohen SCM, De Castro LN (2006) Data clustering with particle swarms. IEEE congress on evolutionary computation, Vancouver, BC, Canada
Central Public Works Department (2002) Handbook and Repairs and Rehabilitation of RCC Buildings. Govt. of India, New Delhi
Centre for Advanced Maintainance Technology (2004) Cracks in buildings—causes and prevention. Govt. of India
Farrar CR, Worden K (2007) An introduction to structural health monitoring. Philos Trans R Soc A: Math Phys Eng Sci 365(1851):303–315
Groumpos P, PandStylios CD (2000) Modeling supervisory control systems using fuzzy cognitive maps. Chaos Solitons Fractals 11(1–3):329–336
Japanese Concrete Institute (1980) Investigation of concrete cracks and guide to repair and rehabilitation. JCI, Hiroshima
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
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
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
Kim YM, Kim CK, Hong S (2006) Fuzzy based state assessment for reinforced concrete building structures. Eng Struct 28(9):1286–1297
Kim YM, Kim CK, Hong SG (2007) Fuzzy set based crack diagnosis system for reinforced concrete structures. Comput Struct 85:1828–1844
Kosko B (1992) Fuzzy systems as universal approximators. In: Proceedings of IEEE international conference on fuzzy systems, San Diego, pp 1153–1162, March 1992
Lee KC, Kim JS, Chung NH, Kwon SJ (2002) Fuzzy cognitive map approach to web mining inference amplification. Expert Syst Appl 22:197–211
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
Nawy EG (2001) Fundamentals of high-performance concrete, 2nd edn. Wiley, New Jersey
Nilsson AH, Winter G (1985) Design of concrete structures. McGraw Hill, New York
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
Palaez E, Bowles JB (1996) Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Inf Sci 88:177–199
Papageorgiou EI (2012) Learning algorithm for fuzzy cognitive maps- a review study. IEEE Trans Syst Man Cybern (SMC) Part C 42(2):150–163
Papageorgiou EI, Iakovidis D (2013) Intuitionistic fuzzy cognitive maps. IEEE Trans Fuzzy Syst 21(2):342–354
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
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
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
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
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
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
Taber WR (1991) Knowledge processing with fuzzy cognitive maps. Expert Syst Appl 2(1):83–87
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
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
Wojciech S, Lukasz K, Witold Pedrycz A, Reofrmat M (2005) Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst 153:371–401
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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
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DOI: https://doi.org/10.1007/s00521-016-2313-9