Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?

In the present study, an artificial neural network (ANN) application is introduced for estimation of damage level of reinforced concrete structures. Back-propagation learning algorithm is adopted. A typical neural network architecture is proposed and some conclusions are presented. Applicability of artificial neural network (ANN) for the assessment of earthquake related damage is investigated

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  • Adeli, H., Hung, SL., Machine learning- neural networks, genetic algorithms and fuzzy systems, John Wiley & Sons, Inc., 1995.
  • Adeli, H., Yeh, C. Perceptron learning in engineering design, Microcomputer in Civil Eng.,1989; 4: 247-56.
  • Aleksander, I., Morton, I., An introduction to neural computing, International Thomson Computer Press., 1995.
  • Civalek, Ö., The design of structures under earthquake effects by using neuro-fuzzy method., Fourth National Earthquake Engineering Conferences, 17-19 September, Ankara, :431-38. Civalek, Ö., linear and nonlinear static-dynamic analysis of plates and shells by neuro- fuzzy technique, Ms Thesis, University of Fırat, (in Turkish), Elazığ, 1998.
  • Civalek, Ö., The analysis of the rectangular plates without torsion via hybrid artificial intelligent technique, Proceedings of the Second International Symposium on Mathematical & Computational Applications, September 1-3, Azerbaijan, 1999:95-101
  • Civalek, Ö., The analysis of rectangular plates via neuro-fuzzy technique, III. National Computational Mechanic Conferences, 16-18 November, Istanbul, 1998:517-25.
  • Eberhart, R. C., and Dobbins, R. W., Neural network PC tools , Academic Press, San Diego, California,1990.
  • Fausett, L., Fundamentals of neural networks, architectures, algorithms, and applications., Prentice-Hall, Inc., New-Jersey, 1994.
  • Fu, LM, Neural Networks in Computer Intelligence., McGraw-Hill, Inc. New York.,1994.
  • Ghaboussi, J., Garrett, Jr., Wu, X., Knowledge- based modeling of material behavior with neural networks, Journal of Structural Engineering, ASCE, 1991; 117: 1, 132-53.
  • Ghaboussi, J., Lin, CC., New method of generating spectrum-compatible accelerograms using neural networks, Earthquake Eng. And Structural Dynamics, 1998; 27: 377-96.
  • Goldberg, DE., Genetic algorithms in search optimization and machine learning, Addison-Wesley, MA, 1989.
  • Hajela, P., Berke, L., Neurobiological computational models in structural analysis and design, Computers and Structures, 1991; 41(4): 657-67.
  • Hani, KB., Ghaboussi, J., Neural networks for structural control of a benchmark problem, active tendon system, Earthquake Eng. And Structural Dynamics, 1998; 27:1225-45.
  • Hertz, J., Krogh, A., Palmer, R. G., Introduction to Theory of Neural Computing, Addison – Wesley Publishing, 1991.
  • Hopfield, JJ., Neural networks and physical systems with emergent collective computational abilities., In Proceedings of National Academy of Sciences, 1982;79: 2554-58.
  • Kang, HT., Yoon, C J., Neural networks approaches to aid simple truss design problems, Microcomputers in Civil Eng., 1994; 9:211-18.
  • Kohonen, T., Content addressable memories, Springer-Verlag, New-York, 1980.
  • Kohonen, T., Associative memory: a system-theoretical approach, Spring-Verlag, New York., 1977.
  • Kohonen, T., Self-organization and associative memory., Spring-Verlag, New York., McCullogh, WS., and Pitts, W., A logical calculus of ideas imminent in nervous activity., Bull. Math. Biophysics, 1943;5: 115-33.
  • Civalek, Ö., Flexural And Axial Vibration Analysis Of Beams With Different Support Conditions Using Artificial Neural Networks, International Journal of Structural Engineering and Mechanics, 18(3), 303-314,2004.
  • Michalewicz, Z., Genetic algorithms + data structure = evolution programs, Springer, Germany, 1992.
  • Park, HS., Adeli, H., Distributed neural dynamics algorithms for optimization of large steel structures, Journal of Structural Engineering, ASCE, 1997; 123(7):880-88.
  • Civalek, Ö., Yapay Zeka-Söyleşi, Türkiye İnşaat Mühendisleri Odası-TMH, Mühendislik Haberleri, Sayı 423, 40-50, 2003.
  • Rojas, R., Neural networks, A systematic introduction., Springer, Germany,1996.
  • Ross, TJ., Fuzzy logic with engineering applications, McGraw-Hill, Inc.,1995.
  • Rumelhart, DE., Hinton, GE., and Williams, R J., Learning internal representation by error propagation. in parallel distributed processing : Explorations in the microstructures of cognition, MIT Press, Cambridge, MA., 1986.
  • Szewezyk, ZP., Hajela, P., Damage detection in structures based on feature- sensitive neural networks, J Computing Civil Eng., ASCE, 1994; 8(2):163-78.
  • Civalek, Ö., The analysis of circular plates via neuro-fuzzy technique, Journal of Eng. Science of Dokuzeylül University,1999;1(2):13-31.
  • Thomsan, WT., Dahleh, MD., Theory of vibration with applications, Prentice Hall, New Jersey, 1998.
  • Vanluchene, RD., and Roufei, S., Neural networks in structural engineering, Microcomputers in Civil Eng., 1990; 5:207-215.
  • Wu, X., Ghaboussi, J., Garrett, JH., Use of neural networks in detection of structural damage, Computers & Structures, 1992 ; 42(4): 649-59.
  • Zurada, J M., Introduction to artificial neural networks, West Publishing Com.,1992.