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Crack and Flaw Identification in Statics and Dynamics, using Filter Algorithms and Soft Computing

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Parameter Identification of Materials and Structures

Abstract

Numerical methods for the solution of crack and flaw identification problems in two-dimensional elastomechanics are presented in this chapter. The mechanical modelling is based on boundary element techniques, with special care of appropriate crack modeling. The possibility of partially or totally closed cracks (unilateral contact effects) is taken into account by means of suitable contact mechanics’ techniques which are based on linear complementarity algorithms. The identification problem is formulated within a general framework of output error minimization (least-squares data fitting) for an appropriately parametrized mechanical model. Backpropagation neural networks and filter-driven optimization, realized by extended Kalman filter algorithms, are used for the solution of the inverse problems. For the two-dimensional examples presented here the proposed method has similar performance for classical crack and flaw identification problems. The identification using the nonlinear model of unilateral cracks is a considerably more difficult task. The methods can be extended in order to cover more general parameter identification problems.

Partial support from the German Research Foundation (DFG) and the Greek-German scientific cooperation project IKYDA 2001, is greatfully acknowledged. These notes are partially based on common research work with Prof. Rafael Gallego, Granada, Spain and Assistant Prof. Aristidis Likas, Ioannina, Greece. More details can be found in the cited original publications. The authors takes the opportunity to express their cordial thanks.

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References

  • Alessandri, C, Mallardo, V. (1999): Crack identification in two-dimensional unilateral contact mechanics with the boundary element method. Computational Mechanics, 24:100–109.

    Article  MATH  Google Scholar 

  • Alkahe, J., Rand, O., Oshman, Y. (2003): Helicopter health monitoring using an adaptive estimator. Journal of the Americal Helicopter Society, 48(3);199–210.

    Google Scholar 

  • Antes, H. (1985): A Boundary Element Procedure for Transient Wave Propagation in Two-Dimensional Isotropic Elastic Media, Finite Elements in Analysis and Design, 1:313–322.

    Article  MATH  Google Scholar 

  • Antes, H., Panagiotopoulos, P.D. (1992): The boundary integral approach to static and dynamic contact problems. Equality and inequality methods. Birkhäuser, Basel-Boston-Berlin, 1992.

    MATH  Google Scholar 

  • Bertsekas, D.P (1996): Incremental least squares methods and the extended Kalman filter. SIAM Journal on Optimization, 6(3):807–822.

    Article  MATH  MathSciNet  Google Scholar 

  • Bolzon, G., Fedele, R., Maier, G. (2002): Parameter identification of a cohesive crack model by Kalman filter. Computer Methods in Applied Mechanics and Engineering, 191(25–26):2847–2871.

    Article  MATH  Google Scholar 

  • Brammer, K., Stiffling, G. (1975): Kalman-Bucy-Filter Deterministische Beobachtung und stochastische Filterung. R. Oldenbourg Verlag.

    Google Scholar 

  • Brown, L.M., DeNale, R. (1991): Classification of ultrasonic defect signatures using an artificial neural network, Review of Progress in Qualitative Nondestructive Evaluation, 10;705–712.

    Google Scholar 

  • Brown, R.G., Hwang, P.Y.C. (1997): Introduction to Random Signals and Applied Kalman Filtering. John Wiley & Sons.

    Google Scholar 

  • Catlin, D.E. (1989): Estimation, Control, and the Discrete Kalman Filter. Springer-Verlag.

    Google Scholar 

  • Corigliano, A., Mariani, S. (2004): Parameter estimation in explicit structural dynamics: performance of the extended Kalman filter. Computer Methods in Applied Mechanics and Engineering, 193(36–38):3807–3835.

    Article  MATH  Google Scholar 

  • DomĂ­nguez, J. (1993): Boundary Elements in Dynamics, Computational Mechanics Publications, Southampton and Elsevier Applied Science, London.

    MATH  Google Scholar 

  • Engelhardt, M. (2004) Numerische Verfahren zur Identifizierung von Fehlstellen aus Randdaten, Doktorarbeit, Fakultät fĂĽr Bauingenieurwesen, Technische Universität Braunschweig, Germany.

    Google Scholar 

  • Fedelinski, P., Aliabadi, M.H., Rooke, D.P. (1994): Dynamic stress intensity factors in mixed mode. Boundary Elements XVI, pages 513–520.

    Google Scholar 

  • Gallego, R., DomĂ­nguez, J. (1996): Hypersingular BEM for transient elastodynamics. International Journal for Numerical Methods in Engineering, 39(10):1681–1705.

    Article  MATH  MathSciNet  Google Scholar 

  • Gallego, R., DomĂ­nguez. J. (1997): Solving transient dynhamic crack problems by the hypersingular boundary element method. Fatigue and Fracture of Engineering Materials and Structures, 20(5):799–812.

    Article  Google Scholar 

  • Gavarini, H., Perazzo, R.P.J., Reich, S.L., Altschuler, E., Pignotti, A. (1996): Neural network classifier of cracks in steel tubes, Insight, 38(2): 108–111.

    Google Scholar 

  • Gavarini, H., Perazzo, R.P.J., Reich, S.L., Altschuler, E., Pignotti, A. (1998): Automatic assessment of the severity of cracks in steel tubes using neural networks, Insight, 40(2):92–97.

    Google Scholar 

  • Granados, J.J., Gallego, R. (2001): Regularization of nearly hypersingular integrals in the boundary element method. Engineering Analysis with Boundary Elements 25(3): 165–184.

    Article  MATH  Google Scholar 

  • Grewal, M.S., Andrews, A.P (1993); Kalman Filtering-Theory and Practice. Prentice-Hall.

    Google Scholar 

  • Guz, A.N., Zozulya, V.V. (2002): Elastodynamic unilateral contact problems with friction for bodies with cracks. International Applied Mechanics, 38(8):895–932.

    Article  MathSciNet  Google Scholar 

  • Khandetsky, V., Antonyuk, I. (2002): Signal processing in defect detection using back-propagation neural networks. NDT & E International 35:483–488.

    Article  Google Scholar 

  • Kitahara, M., Achenbach, J.D., Guo, Q.C., Peterson, M.L., Notake, M., Takadoya, M. (1992): Neural network for crack-depth determination from ultrasonic scattering, Review in Progress in Qualitative Nondestructive Evaluation, 11:701–708.

    Google Scholar 

  • Kitahara, M., Achenbach, J.D, Guo, Q.C., Peterson, M.L., Ogi, R., Notake, M. (1991): Depth determination of surface-breaking cracks by a neural network, Review in Progress in Qualitative Nondestructive Evaluation, 10:689–696.

    Google Scholar 

  • Liang, Y.C., Hwu, C. (2001): On-line identification of holes/cracks in composite structures. Smart Materials & Structures, 10(4):599–609.

    Article  Google Scholar 

  • Likas, A., Karras, D., Lagaris, I.E. (1998): Neural network training and simulation using a multidimensional optimization system, Int. J. of Computer Mathematics, 67:33–46.

    MATH  Google Scholar 

  • Liu, Y., Liang, L.H., Jia, G.S. (2001); Kalman filter based 3D-stochastic inverse boundary element method for flaw identification and structural reliability prediction. Inverse Problems in Engineering 9(3): 199–215.

    Google Scholar 

  • Murakami, A. (2002): The role of Kalman filtering in an inverse analysis of elastoplastic material. Proceedings of the Japan Academy, Serie B-Physical and Biological Sciences, 78(8):250–255.

    MathSciNet  Google Scholar 

  • Papageorgiou, D.G., Demetropoulos, I.N., Lagaris, I.E. (1998): MERLIN-3.0 A multidimensional optimization environment, Computer Physics Communications, 109:227–249.

    Article  MATH  Google Scholar 

  • Oishi, A., Yamada, K., Yoshimura, A., Yagawa, G. (1995): Quantitative nondestructive evaluation with ultrasonic method using neural networks and computational mechanics, Computational Mechanics, 15:521–533.

    Google Scholar 

  • Portela, A., Aliabadi, M.H., Rooke D.P. (1992); The dual boundary element method: effective implementation for crack problems. International Journal for Numerical Methods in Engineering, 33(6): 1269–1287.

    Article  MATH  Google Scholar 

  • Rhim, J., Lee, S.W. (1995): A neural network approach for damage detection and identification of structures, Computational Mechanics, 16:437–443.

    Article  MATH  Google Scholar 

  • Rus, G., Gallego, R. (2002): Optimization algorithms for identification inverse problems with the boundary element method, Engineering Analysis with Boundary Elements, 26(4):315–327.

    Article  MATH  Google Scholar 

  • Rus, G., Lee, S.Y, Gallego, R. (2005): Defect identification in laminated composite structures by BEM from incomplete static data, International Journal of Solids and Structures, 42(5–6): 1743–1758.

    Article  MATH  Google Scholar 

  • Sáez, A., Gallego, R., DomĂ­nguez, J. (1995): Hypersingular quarter point boundary elements for crack problems. International Journal for Numerical Methods in Engineering, 38:1681–1701.

    Article  MATH  Google Scholar 

  • Seibold, S, Weinert, K. (1996): A time domain method for the localization of cracks in rotors. Journal of Sound and Vibration, 195(1);57–73.

    Article  Google Scholar 

  • Stavroulakis, G.E., Antes, H. (1997): Nondestructive elastostatic identification of unilateral cracks through BEM and neural networks, Computational Mechanics, 20(5):439–451.

    Article  MATH  Google Scholar 

  • Stavroulakis, G.E., Antes, H. (1998): Neural crack identification in steady state elastodynamics, Computer Methods in Applied Mechanics and Engineering, 165(1/4): 129–146.

    Article  MATH  Google Scholar 

  • Stavroulakis, G.E., Antes, H., Panagiotopoulos, P.D. (1999): Transient elastodynamics around cracks including contact and friction. Computer Methods in Applied Mechanics and Engineering, Special Issue: Computational Modeling of Contact and Friction, Eds.: J.A.C. Martins and A. Klarbring, 177(3/4):427–440.

    Google Scholar 

  • Stavroulakis, G.E. (1999): Impact-echo from a unilateral interlayer crack. LCP-BEM modelling and neural identification, Engineering Fracture Mechanics, 62(2–3): 165–184.

    Article  Google Scholar 

  • Stavroulakis, G.E., Antes, H. (2000): Unilateral crack identification. A filter-driven, iterative, boundary element approach. Journal of Global Optimization, 17(1–4):339–352.

    Article  MATH  MathSciNet  Google Scholar 

  • Stavroulakis, G.E. (2000): Inverse and crack identification problems in engineering mechanics. Kluwer Academic Publishers, Dordrecht, and Habilitation Thesis, Technical University of Braunschweig, Germany.

    Google Scholar 

  • Stavroulakis, G.E., Antes, H. (in press) Classical and soft computing techniques for crack identification problems. The challenge of unilateral cracks, ASME Applied Mechanics Reviews.

    Google Scholar 

  • Stavroulakis, G.E., Engelhardt, M., Likas, A., Gallego, R., Antes, H. (2004): Neural network assisted crack and flaw identification in transient dynamics. Journal of Theoretical and Applied Mechanics, Warsaw, Special Issue: Computational Intelligence, Ed. T. Burczynski, 42(3);629–649.

    Google Scholar 

  • Su, R.K.L., Zhu, Y., Leung, A.Y.T. (2002): Parametric quadratic programming method for elastic contact fracture analysis, International Journal of Fracture, 117:143–157.

    Article  Google Scholar 

  • Tanaka, M., Nakamura, M., Yasmagowa, K. (1991): Application of boundary element method for elastody-namics to defect shape identification. Mathematical and Computer Modeling, 15(3–5):295–302.

    Article  MATH  Google Scholar 

  • Teh, C.I., Goh, A.T.C., Jaritgam, S. (1997): Prediction of pile capacity using neural networks, ASCE Journal of Computing in Civil Engineering, 11(2): 129–138.

    Article  Google Scholar 

  • Thavasimuthu, M., Rajogopalan, C, Kalyanasundaram, P., Raj, B. (1996): Improving the evaluation sensitivity of an ultrasonic pulse echo technique using a neural network classifier, NDT and E International, 29(3): 175–179.

    Article  Google Scholar 

  • Tosaka, N., Utani, A., Takahashi, H. (1995): Unknown defect identification in elastic field by boundary element method with filtering procedure. Engineering Analysis with Boundary Elements, 15(2):207–215.

    Article  Google Scholar 

  • Tsou, P., Herman Shen, M.-H. (1994): Structural damage detection and identification using neural networks, AIAA Journal, 32(1):176–183.

    MATH  Google Scholar 

  • Udpa, L., Udpa, S.S. (1990): Eddy current defect characterization using neural networks, Materials Evaluation, 48:342–347.

    Google Scholar 

  • Williams, T.P., Cucunski, N. (1995): Neural networks for backcalculation of moduli from SASW test, ASCE Journal of Computing in Civil Engineering, 9(1): 1–8.

    Article  Google Scholar 

  • Yagawa, G., Okuda, H. (1996): Neural networks in computational mechanics, Archives of Computational Methods in Engineering, 3(4):435–512.

    Google Scholar 

  • Yoshimura, S., Matsuda, A., Yagawa, G. (1996): New regularization by transformation for neural network based inverse analyses and its application to structure identification, International Journal of Numerical Methods in Engineering, 39:3953–3968.

    Article  MATH  Google Scholar 

  • Yoshimura, S., Yagawa, G., Oishi, A., Yamada, K. (1993): Qualitative defect identification by means of neural network and computational mechanics, in 3rd Japan International SAMPE Symposium, pp. 2263–2268.

    Google Scholar 

  • Yuki, H., Homma, K. (1996): Estimation of acoustic emission source waveform of fracture using a neural network, NDT & E International, 29(1):21–25.

    Article  Google Scholar 

  • Yusa, N., Cheng, W., Chen, Z., Miya, K. (2002): Generalized neural network approach to eddy current inversion, NDT & E International, 35:609–614.

    Article  Google Scholar 

  • Zarchan, P., Musoff, H. (2000): Fundamentals of Kalman Filtering: A Practical Approach. American Institute of Aeronautics and Astronautics, Inc.

    Google Scholar 

  • Zeng, P. (1998): Neural computing in mechanics, ASME Applied Mechanics Reviews, 51(2): 173–197.

    Article  Google Scholar 

  • Zgonc, K., Achenbach, J.D. (1996): A neural network for crack sizing trained by finite element calculations, NDT & E International, 29(3): 147–155.

    Article  Google Scholar 

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Stavroulakis, G.E., Engelhardt, M., Antes, H. (2005). Crack and Flaw Identification in Statics and Dynamics, using Filter Algorithms and Soft Computing. In: MrĂłz, Z., Stavroulakis, G.E. (eds) Parameter Identification of Materials and Structures. CISM International Centre for Mechanical Sciences, vol 469. Springer, Vienna. https://doi.org/10.1007/3-211-38134-1_5

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  • DOI: https://doi.org/10.1007/3-211-38134-1_5

  • Publisher Name: Springer, Vienna

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