Neural Networks in the Identification Analysis of Structural Mechanics Problems

  • Zenon Waszczyszyn
  • Leonard Ziemiański
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 469)


The Chapter is related to research carried out by the authors’ teams. The Chapter contains seven Sections and list of References. Section 1 concerns basics of selected neural networks. The main attention is paid to the Back-Propagation NNs, which are mostly applied in the analysis of engineering problems. Modifications of this NN (replicator, cascade NN, Fuzzy Weight NN) and some other NNs (Radial Basis Function NN and Adaptive Neuro-Fuzzy Inference System) are discussed in short. Data preprocessing, design problems of these NNs and approximation errors are considered as well. Section 2 is related to the application of NNs for simulating trials in the Classical Monte Carlo Method. Patterns generated by an FE program are used for the NN training and testing. A great numerical efficiency of this approach is presented on an example of the reliability analysis of an elastoplastic plane frame. Section 3 deals with the identification problems of real buildings subjected to paraseismic excitations. Section 4 is related to the application of dynamic response (eigenfrequencies excited by impulse loadings or wave propagation measurements) to the parameter identification of structural elements with defects. In Section 5 the problem of FEM models updating is considered. A hybrid approach is discussed as a sequence of the application of an initially formulated FE model with control parameters, which are identified by an NN. The calibration and verification of the updated FE model is performed on the base of laboratory tests. Section 6 discusses applications of a modification of a standard NN (Fuzzy Weight NN) to the analysis of problems from experimental structural mechanics that give fuzzy results. Section 7 deals with so-called implicit modelling (i.e. model-free, data-related NNs) of physical relationships. In References, besides basic literature, also papers written by the authors and their associates are quoted3,4.


Neural Network Membership Function Hide Layer Testing Pattern Identification Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. ADINA, Theory and Modelling Guide. Adina R & D Inc. (1992).Google Scholar
  2. Broniewicz, M, and Szlendak, J. (1995). Stiffness of welded beam-to-column rectangular tube connections (in Polish). Proceedings of the 9th Intern. Conf. Metal Structures. Cracow, Poland, 1995. Vol.1.Google Scholar
  3. Ciesielski, R., Kuźniar, K., Maciag, E., and Tatara, T. (1992). Empirical formulae for fundamental natural periods of buildings with load bearing walls. Archives of Civil Engineering 38:291–299.Google Scholar
  4. Ciesielski, R., Kuzniar, K., Maciag, E., and Tatara, T. (1995). Damping of vibrations in precast buildings with bearing concrete walls. Archives of Civil Engineering 41: 329–341.Google Scholar
  5. Chaboche, J.L., and Rousslier, G. (1983). On the plastic and viscoplastic constitutive equations, Part II: Application of internal variable concept to the 316 stainless steel. Trans. ASME, Journal of Pressure Vessel Technology 105:159–164.CrossRefGoogle Scholar
  6. Eurocode 2, Design of Concrete Structures. P.1: General rules and rules for buildings, ENV 1992-1-1 (1992).Google Scholar
  7. Eurocode 3: Design of Steel Structures. ENV 1993-1-1, 6.9. Beam-to-Column Connections (1993).Google Scholar
  8. Friswell, M.I., and Motterhead, J.E. (1996). Finite Element Model Updating in Structural Mechanics. Dordrech: Kluwer Academic Publishers.Google Scholar
  9. Furtak, K. (1984). Strength of concrete subjected to multiple repeat loadings (in Polish). Archives of Civil Engineering 30:677–698.Google Scholar
  10. Furukawa T., and Yagawa, G. (1998). Implicit constitutive modelling for viscoplasticity using neural networks. International Journal for Numerical Methods in Engineering 43: 195–219.MATHCrossRefGoogle Scholar
  11. Fuzzy Logic Toolbox for Use with MATLAB. User’s Guide, Version 2.1, (2001). Natick, MA: The MathWorks Inc.Google Scholar
  12. Ghaboussi, J., and Lin, C-C. J. (1998). New method of generating spectrum compatible accelerograms using neural networks. Earthquake Engineering and Structural Dynamics 27:377–396.CrossRefGoogle Scholar
  13. Hayashi, Y., Buckley, J.J., and Czogala, E. (1993). Neural networks with fuzzy signals and weights. J. Intell Systems 7: 527–537.Google Scholar
  14. Haykin, S. (1999). Neural Networks — A Comprehensible Foundations. Upper Saddle River, NJ: Prentice-Hall. 2nd edition.Google Scholar
  15. Ishak, S.L., Liu, G.R., H.M. Shang, H.M., and Lim, S.P. (2002). Non-destructive evaluation of horizontal crack detection in beams using transverse impact. Journal of Sound and Vibrations 252:343–360.CrossRefGoogle Scholar
  16. Jakubek, M., Urbanska, A., and Waszczyszyn, Z. (2003). Application of FWNN to the analysis of experimental mechanics problems. Numerical Methods in Continuum Mechanics-Proceedings of the. 9th Conference NMCM2003, Žilina, Slovak Republic, September 9–12, 2003. CD-ROM, 12 pp.Google Scholar
  17. Jang, J-Sh.R., Sun Ch-T., and Mizutani E. (1997). Neuro-Fuzzy and Soft Computing. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
  18. Juang, C.H., Ni, S.H., and Ping, C.L. (1999). Training of artificial neural networks with the aid of fuzzy sets. Computer-Aided Civil and Infrastructures Engineerig 14:407–415.CrossRefGoogle Scholar
  19. Kaliszuk, J., Urbanska, A., and Waszczyszyn, Z. (2001). Neural analysis of concrete fatigue durability on the basis of experimental evidence. Archives of Civil Engineering 47: 327–339.Google Scholar
  20. Kaliszuk, J., and Waszczyszyn, Z. (2003). Reliability analysis of structures by neural network supported Monte Carlo methods. In: Rutkowski, L., and Kacprzyk, J., eds., Neural Networks and Soft Computing. Heidelberg, Germany: Physica-Verlag, A Springer-Verlag Co. 754–759.Google Scholar
  21. Klir, G.J., and Bo Yuan (1995). Fuzzy Sets and Fuzzy Logic — Theory and Applications. Upper Saddle River, NJ: Prentice-Hall.MATHGoogle Scholar
  22. Kuzniar, K., Maciag E., and Waszczyszyn, Z. (2000). Computation of natural fundamental periods of vibrations of medium-height prefabricated buildings by neural networks. Archives of Civil Engineering 46:515–523.Google Scholar
  23. Kuzniar, K., Maciag, E., and Waszczyszyn, Z. (2004). Computation of response spectra from mining tremors using neural networks. Solid Dynamics and Earthquake Engineering (in press).Google Scholar
  24. Kuzniar, K., and Waszczyszyn, Z. (2002). Neural analysis of vibration problems of real flat buildings and data preprocessing. Engineering Structures 24:1327–1335.CrossRefGoogle Scholar
  25. Lakota, W. (1999). Damage Detection in Beam Structures, (in Polish). Rzeszow, Poland: Ofic. Wydawn. Polit. Rzeszowskiej.Google Scholar
  26. Ligeza, W. (2002). Redistribution of Internal Forces in Braced Concrete Plate Elements (in Polish). Faculty Civil Eng., Cracow Univ. of Technology, Poland. Monograph No 277.Google Scholar
  27. Lorenz, R.F., Ben Kato, and Chen, W.F., eds. (1992). Semi-Rigid Connections in Steel Frames. New York: McGraw-Hill.Google Scholar
  28. Lyman, O. (1984). An Introduction to Statistical Methods and Data Analysis. Boston: PWS Publishers.Google Scholar
  29. Maciag, E. (1986). Experimental evaluation of changes of dynamic properties of buildings on different grounds. Earthquake Engineering and Structural Dynamics 14: 925–932.CrossRefGoogle Scholar
  30. Marek, P., Brozetti J., and Gustar, M., eds. (2001). Probabilistic Assessment of Structures using Monte Carlo Simulation. Prague, Czech Republic: TeReCo, Acad. Sci.Google Scholar
  31. Masters, Th. (1993). Practical Neural Network Recipes in C++. Academic Press.Google Scholar
  32. Muhanna, R.L., and Mullen, R.L. (2001). Uncertainty in Mechanics Problems — Interval — Based Approach. Journal of Engineering Mechanics 127: 357–556.CrossRefGoogle Scholar
  33. Miller, B., Piatkowski, G., and Ziemianski, (1999). Beam yielding load identification by neural networks. Computer Assisted Mechanics and Engineering Sciences 6: 449–467.MATHGoogle Scholar
  34. Miller, B., and Ziemianski, L. (2001). Updating of mathematical models using neural networks. Proceedings of the 2nd European Conference on Computational Mechanics ECCM-2001. Cracow, Poland June 26–29, 2001. CD-ROM, 11 pp.Google Scholar
  35. Miller, B. (2002). Updating of Mathematical Models of Structures to Physical Models. Ph.D. Thesis (in Polish). Faculty of Civil Engineering, Rzeszow University of Technology, Poland.Google Scholar
  36. Miller, B., and Ziemianski, L. (2002). Application of neural networks to the structural model updating. Proceedings of the Fifth World Congress on Computational Mechanics WCCM V, Vienna, Austria, July 7–12, 2002. CD-ROM, 10 pp.Google Scholar
  37. Neural Network Toolbox for Use with MATLAB. User’s Guide Version 3, (1998). Natick, MA: The Math Works Inc.Google Scholar
  38. Ni, S.H., Lu, P.C., and Yang, C.H. (1996). A fuzzy neural network approach to evaluation of slope failure potential. Microcomputers in Civil Engineering 11: 59–66.Google Scholar
  39. Nowak, A., and Collins, K. (2000). Reliability of Structures. McGraw-Hill, Intern. Edition / Civil Eng. Series.Google Scholar
  40. Oishi, A.K., Yamada, K., Yoshimura, S., and Yagawa, G. (1995). Quantitative nondestructive evaluation with ultrasonic method using neural networks and computational mechanics. Computational Mechanics 15: 521–523.Google Scholar
  41. Pabisek, E., Jakubek, M., and Waszczyszyn, Z. (2003). A fuzzy network for the analysis of experimental structural engineering problems. In: Rutkowski, L., and Kacprzyk, J., eds., Neural Networks and Soft Computing. Heidelberg, Germany: Physica-Verlag, A Springer-Verlag Co. 772–777.Google Scholar
  42. Paez, Th., L. (1993). Neural networks in mechanical system simulation, identification and assessment. Shock and Vibration 1: 177–199.Google Scholar
  43. Papadrakakis, M., Papadopoulos, V., and Lagaros, N.D. (1996). Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation. Computer Methods in Applied Mechanics and Engineering 136: 145–163.MATHCrossRefGoogle Scholar
  44. Piatkowski, G. (2003). Detection of Damage in Structural Elements Using Artificial Neural Networks, Ph. D. Thesis (in Polish). Faculty of Civil Engineering, Rzeszow University of Technology, Poland.Google Scholar
  45. PN-80/B-03040, Foundation and Machine Support Structures — Analysis and Design, (in Polish). Polish Standards Committee, (1980).Google Scholar
  46. Pulido, J.E., Jacobs, T.L., and Prates de Lima, E.C. (1992). Structural reliability using Monte Carlo simulation with variance reduction techniques on elastic-plastic structures. Computers & Structures 43: 419–430.CrossRefGoogle Scholar
  47. Rajaseharan, S., Febin M.F., and Ramasamy, J.V. (1996). Artificial fuzzy neural networks in civil engineering. Computers & Structures 61: 291–302.CrossRefGoogle Scholar
  48. Rojas, R. (1996). Neural Networks — A Systematic Introduction. Berlin-Heidelberg: Springer Verlag.MATHGoogle Scholar
  49. STATISTICA Neural Networks PL. Manual User’s (in Polish), (2001). Krakow, Poland: StatSoft.Google Scholar
  50. Thomson, R.B. (1983). Quantitative ultrasonic nondestructive evaluation methods. Journal of Applied Mechanics 1191–1201.Google Scholar
  51. Twomey, J.M., and Smith, A.E. (1997). Validation and verification. In: Kartam, N., Flood I., and Garrett, Jr., J.H., eds., Artificial Neural Networks for Civil Engineers: Fundamentals and Applications. New York: Publ. ASCE. 44–64.Google Scholar
  52. Urbanska, A., Kaliszuk, J., and Waszczyszyn, Z. (2000) Neural analysis of semi-rigid tube connections. Science Research Education-Proceedings of Polish-German Symposium SRE’2000, Zielona Gora, Poland, September 28–29, 2000. Vol.1, 127–134Google Scholar
  53. Urbanska, A., Kaliszuk, J., and Waszczyszyn., Z. (2001). Neural analysis of semi-rigide tube connections in steel frames (in Polish). Proceedings of the 10th International Conferenc. Metal Structures-Gdansk 2001, Gdansk, Poland, June 6–8, 2001. Vol.2, 325–334.Google Scholar
  54. Urbanska, A., and Waszczyszyn,. Z. (2003). Neural analysis of concrete shrinkage. In: Rutkowski, L., and Kacprzyk, J., eds., Neural Networks and Soft Computing. Heilderberg, Germany: Physica-Verlag, A Springer-Verlag Co.. 784–789.Google Scholar
  55. Vogel, U., (1985). Calibrating frames. Stahlbau 10: 295–301.Google Scholar
  56. Waszczyszyn, Z., ed. (1999): Neural Networks in the Analysis and Design of Structures. CISM Courses and Lectures No. 404. Wien-New York: Springer.Google Scholar
  57. Waszczyszyn, Z. (2003). Neurocomputing and finite element method. In: Burczynski, T., Fedelinski, P., and Majchrzak E., eds., Proc. 1st CEACM Conf. Computer Methods in Mechanics and 15th Intern. Conf. Compu. Meth. Mech. CMM-2003, Gliwice/Wisla, Poland, June 3–6, 2003. CD-ROM, 10 pp.Google Scholar
  58. Waszczyszyn, Z., Cichon, Cz., and Radwanska, M. (1994). Stability of Structures by Finite Element Methods. Amsterdam: Elsevier.MATHGoogle Scholar
  59. Waszczyszyn, Z., and Jakubek, M. (2003). FWNN in the neural analysis of experimental structural mechanics problems. In: Ciftcioglu, O., and Dado, E., eds., Intelligent Computing in Engineering. Delft, The Netherlands: SOON. 117–126.Google Scholar
  60. Waszczyszyn, Z., and Pabisek, E. (2002). Elastoplastic analysis of plane steel frames by a new superelement. Archives of Civil Engineering 48: 159–181.Google Scholar
  61. Waszczyszyn, Z., and Slonski, M., (2000). Analysis of some problems of experimental mechanics and biomechanis by means of the ANFIS neuro-fuzzy system. Journal of Theoretical and Applied Mechanics 38:429–445.MATHGoogle Scholar
  62. Waszczyszyn, Z., and Ziemianski, L. (2001). Neural networks in mechanics of structures and materials — new results and prospects of applications. Computers & Structures 79: 2261–2276.CrossRefGoogle Scholar
  63. Watkins, J. (1983). Fracture thoughness test for soil-cement samples in Mode II. Intern. J. Fracture 23: 135–138.CrossRefGoogle Scholar
  64. Weynand K. (1992). Sericon: Data bank on joints in building frames. Semi-Rigid Behaviour of Civil Engineering Structural Connections — COST Cl Proceedings of the First State-of-the-Art Workshop, Strasbourg, France, October 28–30, 1992, Strasbourg, France. Strasbourg: ENSAIS and Polyt. L. Pasteur. 463–473.Google Scholar
  65. Yagawa, G., and Okuda, H. (1996). Neural networks in computational mechanics. Archives of Computational Methods in Engineering, 4: 435–512.Google Scholar
  66. Yager, R.R. (1991). Modelling and Formulating Fuzzy Knowledge Base Using Neural Network. Machine Intelligence Institute, Iona College. Technical Report No. MIT-1111.Google Scholar
  67. Zell, E., ed. (1995). SNNS — Stuttgart Neural Network Simulator. User’s Manual, Version 4.1, Univ. Stuttgart, 1995.Google Scholar
  68. Ziemianski L. (1999). Neural Networks in Structural Dynamics — Selected Problems, (in Polish). Rzeszow, Poland: Ofic. Wydawn. Polit. Rzeszowskiej.Google Scholar
  69. Ziemianski, L., and Harpula, G. (1999). The use of neural networks for damage detection in eight storey frames. Engineering Applications of Neural Networks — Proceedings of the 5th International Conference, Warsaw, Poland, September 13–15, 1999, Warszawa, Poland. Torun, Poland: Wyd. A. Marszalek. 292–297.Google Scholar
  70. Ziemianski. L., and Piatkowski, G. (2000). Use of neural networks for damage detection in structural elements using wave propagation. In: B.H.V. Topping, ed., Computational Engineering using Metaphors from Nature. Edinburgh, UK: Civil-Comp Press. 25–45.Google Scholar

Copyright information

© CISM, Udine 2005

Authors and Affiliations

  • Zenon Waszczyszyn
    • 1
  • Leonard Ziemiański
    • 2
  1. 1.Institute of Computer Methods in Civil EngineeringCracow University of TechnologyPoland
  2. 2.Chair of Structural MechanicsRzeszow University of TechnologyPoland

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