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

Year 2010, Volume: 2 Issue: 3, 71 - 81, 01.09.2010

Abstract

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

References

  • 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.
Year 2010, Volume: 2 Issue: 3, 71 - 81, 01.09.2010

Abstract

References

  • 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.
There are 33 citations in total.

Details

Other ID JA65JR28HS
Journal Section Articles
Authors

A. K. Baltacıoğlu This is me

B. Öztürk This is me

Ö. Civalek This is me

B. Akgöz This is me

Publication Date September 1, 2010
Published in Issue Year 2010 Volume: 2 Issue: 3

Cite

APA Baltacıoğlu, A. K., Öztürk, B., Civalek, Ö., Akgöz, B. (2010). Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?. International Journal of Engineering and Applied Sciences, 2(3), 71-81.
AMA Baltacıoğlu AK, Öztürk B, Civalek Ö, Akgöz B. Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?. IJEAS. September 2010;2(3):71-81.
Chicago Baltacıoğlu, A. K., B. Öztürk, Ö. Civalek, and B. Akgöz. “Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?”. International Journal of Engineering and Applied Sciences 2, no. 3 (September 2010): 71-81.
EndNote Baltacıoğlu AK, Öztürk B, Civalek Ö, Akgöz B (September 1, 2010) Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?. International Journal of Engineering and Applied Sciences 2 3 71–81.
IEEE A. K. Baltacıoğlu, B. Öztürk, Ö. Civalek, and B. Akgöz, “Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?”, IJEAS, vol. 2, no. 3, pp. 71–81, 2010.
ISNAD Baltacıoğlu, A. K. et al. “Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?”. International Journal of Engineering and Applied Sciences 2/3 (September 2010), 71-81.
JAMA Baltacıoğlu AK, Öztürk B, Civalek Ö, Akgöz B. Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?. IJEAS. 2010;2:71–81.
MLA Baltacıoğlu, A. K. et al. “Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?”. International Journal of Engineering and Applied Sciences, vol. 2, no. 3, 2010, pp. 71-81.
Vancouver Baltacıoğlu AK, Öztürk B, Civalek Ö, Akgöz B. Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?. IJEAS. 2010;2(3):71-8.

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