Prediction of rubberized concrete properties using artificial neural network and fuzzy logic

Abstract In this study, waste automobile tyres in two different sizes were used in production of rubberized fresh concretes. Their unit weight and flow table values were determined experimentally. The values determined were also found when artificial neural networks (ANN) and fuzzy logic (FL) models were employed. According to the given rubberized concrete data, it was demonstrated that properties of fresh concrete could be determined without attempting any experiments by using ANN and FL models. During the tests similar results were observed for experimental results with those of ANN and FL models. Besides, the facts that lighter concrete might be produced using tyre as a light material and waste tyres may be recycled this way were put forth.

[1]  James A. Anderson,et al.  Cognitive and psychological computation with neural models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[3]  Shuaib H. Ahmad,et al.  Mechanical Properties of Concrete with Ground Waste Tire Rubber , 1996 .

[4]  Guoqiang Li,et al.  Development of waste tire modified concrete , 2004 .

[5]  I. Topcu Assessment of the brittleness index of rubberized concretes , 1997 .

[6]  Witold Pedrycz,et al.  Fuzzy control and fuzzy systems , 1989 .

[7]  Gokmen Tayfur,et al.  FUZZY LOGIC MODEL FOR THE PREDICTION OF CEMENT COMPRESSIVE STRENGTH , 2004 .

[8]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[9]  I. Topcu The properties of rubberized concretes , 1995 .

[10]  T. Ross Fuzzy Logic with Engineering Applications , 1994 .

[11]  A. Öztas,et al.  Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks , 2007 .

[12]  Seung-Chang Lee,et al.  Prediction of concrete strength using artificial neural networks , 2003 .

[13]  Alper Sezer,et al.  Prediction of sulfate expansion of PC mortar using adaptive neuro-fuzzy methodology , 2007 .

[14]  Turan Özturan,et al.  Properties of rubberized concretes containing silica fume , 2004 .

[15]  lker Bekir Topçu,et al.  Analysis of rubberized concrete as a composite material , 1997 .

[16]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[17]  F. Demir A new way of prediction elastic modulus of normal and high strength concrete—fuzzy logic , 2005 .

[18]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[19]  Serhan Ozdemir,et al.  The use of GA-ANNs in the modelling of compressive strength of cement mortar , 2003 .

[20]  L. Zadeh,et al.  Fuzzy Logic for the Management of Uncertainty , 1992 .

[21]  Stamatios V. Kartalopoulos,et al.  Understanding neural networks and fuzzy logic - basic concepts and applications , 1997 .

[22]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[23]  Michio Sugeno,et al.  Industrial Applications of Fuzzy Control , 1985 .

[24]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[25]  Z. Khatib,et al.  Rubberized Portland Cement Concrete , 1999 .

[26]  Mehmet Özger,et al.  Prediction of wave parameters by using fuzzy logic approach , 2007 .

[27]  Zekai Şen,et al.  Fuzzy algorithm for estimation of solar irradiation from sunshine duration , 1998 .

[28]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Piti Sukontasukkul,et al.  Properties of concrete pedestrian block mixed with crumb rubber , 2006 .

[30]  Abhijit Mukherjee,et al.  Artificial neural networks in prediction of mechanical behavior of concrete at high temperature , 1997 .

[31]  F. Martin McNeill,et al.  Fuzzy Logic: A Practical Approach , 1994 .

[32]  M. A. Bhatti,et al.  Predicting the compressive strength and slump of high strength concrete using neural network , 2006 .

[33]  Daniel W. C. Ho,et al.  Fuzzy wavelet networks for function learning , 2001, IEEE Trans. Fuzzy Syst..