A comparative study for the concrete compressive strength estimation using neural network and neuro-fuzzy modelling approaches

References

• Turgut , P. 2004 . Research into the correlation between concrete strength and UPV values . J. Nondestruct. Test. , 12 ( 12 ) : 8
   Google Scholar
• Malhotra , V.M. , ed. 1976 . Testing Hardened Concrete: Non-destructive Methods , Monograph No. 9 Detroit, MI, US : ACI .
   Google Scholar
• Leshchinsky , A.M. 12–14 June, 1991 . “ Non-destructive methods instead of specimens and cores ” . In Quality Control of Concrete Structures , Proceedings of the International Symposium Held by RILEM, CEB, RUG, BBG/GBB and NFWO/FNRS and organized by the Magnel Laboratory for Reinforced Concrete Edited by: Taerwe , L. and Lambotte , H. 377 – 386 . Belgium : State University of Ghent .
   Google Scholar
• Neville , A.M. 1995 . Properties of Concrete , Longman, UK : Addison-Wesley .
   Google Scholar
• Bungey , J.H. and Soutsos , M.N. 2001 . Reliability of partially-destructive tests to assess the strength of concrete on site . Construct. Build. Mater. , 15 : 81 – 92 .
   Google Scholar
• Ohdaira , E. and Masuzawa , N. 2000 . Water content and its effect on ultrasound propagation in concrete – the possibility of NDE . Ultrasonics , : 546 – 552 .
   PubMedGoogle Scholar
• Davis , S.G. 1977 . The effect of variations in the aggregate content of concrete columns upon the estimation of strength by the pulse-velocity method . Mag. Concr. Res. , 29 ( 98 ) : 7 – 12 .
   Google Scholar
• Popovics , S. 1998 . Strength and Related Properties of Concrete: A Quantitative Approach , New York : Wiley .
   Google Scholar
• Vassilopoulos , A.P. and Bedi , R. 2008 . Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates . Computat. Mater. Sci. , 43 : 1086 – 1093 .
   Web of Science ®Google Scholar
• Jang , J.R. , Sun , C.T. and Mizutani , E. 1997 . Neuro-fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence , Upper Saddle River, NJ, USA : Prentice-Hall .
   Google Scholar
• Haykin , S. 1998 . Neural Networks—A Comprehensive Foundation , New York : McMillan College Publishing .
   Google Scholar
• Mendel , J.M. 1995 . Fuzzy logic systems for engineering: A tutorial . Proc. IEEE , 83 ( 3 ) : 345 – 377 .
   Web of Science ®Google Scholar
• Trtnik , G. , Franci , K. and Turk , G. 2009 . Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks . Ultrasonics , 49 : 53 – 60 .
   PubMed Web of Science ®Google Scholar
• ASTM C 597-83 . 1991 . Test for Pulse Velocity through Concrete , USA : ASTM .
   Google Scholar
• BS 1881-203 . 1986 . Recommendations for Measurement of Velocity of Ultrasonic Pulses in Concrete , UK : BSI .
   Google Scholar
• Nilsen , A. and Aitcin , P. 1992 . Static modulus of elasticity of high strength concrete from pulse velocity tests . Cem. Concr. Aggr. , 14 ( 1 ) : 64 – 66 .
   Google Scholar
• Philleo , R. 1955 . Comparison of results of three methods for determining Young's modulus of elasticity of concrete . J. Am. Concr. Inst. , 51 : 461 – 469 .
   Google Scholar
• Sharma , M. and Gupta , B. 1960 . Sonic modulus as related to strength and static modulus of high strength concrete . Indian Concr. J. , 34 ( 4 ) : 139 – 141 .
   Google Scholar
• ACI 318-95 . 1995 . Building Code Requirements for Structural Concrete (ACI 318-95) and Commentary ACI 318R-95 , US : ACI .
   Google Scholar
• Mehta , P.K. and Monteiro , P.J.M. 2006 . Concrete Microstructure, Properties, and Materials , 3rd ed. , New York : McGraw-Hill .
   Google Scholar
• Whitehurst , E. 1951 . Soniscope tests concrete structures . J. Am. Concr. Inst. , 47 : 433 – 444 .
   Google Scholar
• Tharmaratnam , K. and Tan , B.S. 1990 . Attenuation of ultrasonic pulse in cement mortar . Cem. Concr. Res. , 20 : 335 – 345 .
   Web of Science ®Google Scholar
• Bungey , J.H. and Millard , S.G. 2004 . Testing of Concretes in Structures , Taylor & Francis e-Library .
   Google Scholar
• Kewalramani , M.A. and Gupta , R. 2006 . Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural networks . Automat. Construct. , 15 : 374 – 379 .
   Web of Science ®Google Scholar
• Lippman , R. 1987 . An introduction to computing with neural nets . IEEE ASSP Mag. , 4 : 4 – 22 .
   Google Scholar
• Kisi , Ö. 2008 . Constructing neural network sediment estimation models using a data-driven algorithm . Math. Comput. Simulat. , 79 : 94 – 103 .
   Google Scholar
• S. Tapkin, A recommended neural trip distribution model, Ph.D. thesis, Middle East Technical University, Ankara, 2004
   Google Scholar
• S. Tapkin, M. Tuncan, O. Arioz, A. Tuncan, and K. Ramyar, Estimation of concrete compressive strength by using ultrasonic pulse velocities and artificial neural networks, in Conference for Computer Aided Engineering and System Modeling, 11th FIGES User's Conference, Conference Proceed, September, Turkey, ISBN:975-98408-2-0, 2006
   Google Scholar
• Kartam , N. , Flood , I. and Garrett , J.H. 1997 . Artificial Neural Networks for Civil Engineers: Fundamentals and Applications , New York : ASCE .
   Google Scholar
• Rafiq , M.Y. , Bugmann , G. and Easterbrook , D.J. 2001 . Neural network design for engineering application . Comput. Struct. , 79 : 1541 – 1552 .
   Web of Science ®Google Scholar
• MathWorks Inc. 1999 . MatLab the Language of Technical Computing , Natick, MA, USA : MathWorks Inc. . Version 6
   Google Scholar
• Ashour , A.F. and Alqedra , M.A. 2005 . Concrete breakout strength of single anchors in tension using neural networks . Adv. Eng. Software , 36 : 87 – 97 .
   Web of Science ®Google Scholar
• Flood , I. and Kartam , N. 1994 . Neural network in civil engineering I: Principles and understandings . ASCE J. Comput. Civil Eng. , 8 ( 2 ) : 131 – 148 .
   Web of Science ®Google Scholar
• Rajagopalan , P.R. , Prakash , J. and Naramimhan , V. 1973 . Correlation between ultrasonic pulse velocity and strength of concrete . Indian Concr. J. , 47 ( 11 ) : 416 – 418 .
   Google Scholar
• Kisi , O. 2005 . Suspended sediment estimation using neuro-fuzzy and neural network approaches . Hydrol. Sci. J. , 50 ( 4 ) : 683 – 695 .
   Web of Science ®Google Scholar
• Shahin , M.A. , Jaksa , M.B. and Maier , H.R. 2001 . Artificial neural network applications in geotechnical engineering . Australian Geomech. , 36 ( 1 ) : 49 – 62 .
   Google Scholar
• Kumar , S. and Barai , S.V. 2010 . Neural networks modeling of shear strength of SFRC corbels without stirrups . Appl. Soft Comput. , 10 : 135 – 148 .
   Web of Science ®Google Scholar
• Zadeh , L.A. 1967 . Fuzzy sets . Informat. Control , 8 : 38 – 53 .
   Google Scholar
• Tigdemir , M. , Karasahin , M. and Sen , Z. 2002 . Investigation of fatigue behaviour of asphalt concrete pavements with fuzzy-logic approach . Int. J. Fatigue , 24 : 903 – 910 .
   Google Scholar
• Jang , J.-S.R. 1993 . ANFIS: Adaptive-network-based fuzzy inference system . IEEE Trans. Syst. Man Cybernet. , 23 ( 3 ) : 665 – 685 .
   Web of Science ®Google Scholar
• Takagi , T. and Sugeno , M. 1985 . Fuzzy identification of systems and its applications to modeling and control . IEEE Trans. Syst. Man Cybernet. , 15 : 116 – 132 .
   Web of Science ®Google Scholar
• BS 1881: Part 120: 1983 . 1983 . Method for Determination of Compressive Strength of Concrete Cores , UK : BSI .
   Google Scholar
• ASTM C 42-90 . 1992 . Standard Test Method for Obtaining and Testing Drilled Cores and Sawed Beams of Concrete , USA : ASTM .
   Google Scholar
• Rumelhart , D.E. , Hinton , G.E. and Williams , R.J. 1986 . “ Learning internal representation by error propagation ” . In Parallel Distributed Processing , Edited by: Rumelhart , D.E. and McClelland , J.L. Vol. 1 , Cambridge, MA, USA : MIT Press .
   Google Scholar
• Todd , C.P.D. and Challis , R.E. 1999 . Quantitative classification of adhesive bondlines using Lamb waves and artificial neural networks . IEEE Trans. UFFC , 46 ( 1 ) : 167 – 181 .
   Google Scholar
• Soshiroda , T. and Voraputhaporn , K. 1999 . Recommended method for earlier inspection of concrete quality by non-destructive testing . Proceedings of the Concrete durability and repair technology , : 27 – 36 . 6–10 September, Dundee, UK
   Google Scholar
• Trezos , K.G. , Georgiou , K. and Marebelias , C. 1993 . Determination of the in situ strength of concrete using the indirect methods of impact and the ultrasounds . Technika Chronika-Scientific Edition TCG , 13 : 27 – 41 .
   Google Scholar
• Bungey , J.H. 1980 . The validity of ultrasonic pulse velocity testing of in-place concrete for strength . NDT Int. , 13 ( 6 ) : 296 – 300 .
   Web of Science ®Google Scholar
• Bilgehan , M. and Turgut , P. 2010 . Artificial neural network approach to predict compressive strength of concrete through ultrasonic pulse velocity . Res. Nondestruct. Eval. , 21 (1) : 1 – 17 .
   Google Scholar