PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Nieparametryczna metoda identyfikacji zmian masy i sztywności konstrukcji

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
EN
Nonparametric identification of structural mass and stiffness modifications
Języki publikacji
PL
Abstrakty
PL
W niniejszej pracy zaproponowano i eksperymentalnie zweryfikowano niskoczęstotliwościową metodę identyfikacji zmian masy i sztywności konstrukcji. Zaproponowana metoda wykorzystuje nieparametryczny model konstrukcji referencyjnej w postaci zbioru jej doświadczalnie zarejestrowanych odpowiedzi. Takie podejście pozwala uniknąć kłopotliwego i podatnego na błędy etapu budowy i kalibracji parametrycznego modelu konstrukcji referencyjnej, a jednocześnie — co jest charakterystyczną cechą zaproponowanej metody — umożliwia modelowanie i identyfikację jej parametrycznie wyrażonych modyfikacji i obciążeń. W celu formalizacji metody wykorzystana została ogólna metodologia metody dystorsji wirtualnych, a główną ideą jest zastąpienie zmian parametrów masowych i sztywnościowych konstrukcji równoważnym im pseudo obciążeniem, którego wpływ na odpowiedź konstrukcji modelowany jest w sposób nieparametryczny. Takie ogólne postawienie problemu pozwoliło na opracowanie szczegółowych sformułowań w dziedzinie czasu i w dziedzinie Laplace'a, w tym problemu wprost i problemu odwrotnego. Gradient i hesjan funkcji celu zostały wyznaczone w formie analitycznej, co umożliwiło zastosowanie do rozwiązania zadania odwrotnego klasycznych gradientowych metod optymalizacyjnych pierwszego i drugiego rzędu. Podstawowe sformułowanie zostało rozszerzone na przypadek identyfikacji parametrów uderzenia idealnie niesprężystego. Weryfikację doświadczalną opracowanej metody przeprowadzono w zakresie identyfikacji zmian masy, sztywności i obciążenia dynamicznego przy wykorzystaniu przestrzennej konstrukcji 70-elementowego wspornika kratowego.
EN
This thesis proposes and experimentally verifies a low-frequency method for identification of modifications of structural mass and stiffness. The proposed method is based on a nonparametric model of the unmodified structure, which is constituted by a set of its experimentally recorded responses. Such a nonparametric approach avoids the troublesome and error-prone stage of parametric model updating, while it allows parametrically expressed structural modifications and loads to be modeled and identified. The approach was formalized by means of the general methodology of the virtual distortion method. The main idea is to model parametric modifications with the equivalent pseudo load, whose effect on the structural response is modeled nonparametrically. Such a general idea was developed into two specific formulations in time domain and in Laplace domain, including the direct and inverse problems. The gradient and the Hessian of the objective function were expressed analytically, which allowed the inverse problem to be solved by means of classical, gradient-based optimization methods of the first and second order. The basic formulations were extended to include the case of identification of an inelastic impact. In the experimental verification, a 70-element space cantilever truss was used in the tasks of identification of mass and stiffness modifications and of dynamic loads.
Rocznik
Tom
Strony
1--153
Opis fizyczny
Bibliogr. 171 poz., tab., wykr.
Twórcy
autor
  • Instytut Podstawowych Problemów Techniki, Polska Akademia Nauk
Bibliografia
  • 1. D. Adams. Health Monitoring of Structural Materials and Components. John Wiley k Sons, Ltd, 2007.
  • 2. J. Balageas, C.-P. Fritzen, and A. Giiemes. Structural Health Monitoring Systems. ISTE, 2006.
  • 3. T. Stępiński, T. Uhl, and WT. Staszewski. Advanced Structural Damage Detection: From Theory to Engineering Applications. John Wiley & Sons, Ltd, 2013.
  • 4. C.R. Farrar and K. Worden. An introduction to structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):303-315, 2007.
  • 5. K. Worden, C.R. Farrar, G. Manson, and G. Park. The fundamental axioms of structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 463(2082):1639-1664, 2007.
  • 6. J.M. Brownjohn. Structural health monitoring of civil infrastructure. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):589-622, 2007.
  • 7. H. Wenzel and D. Pichler. Ambient Vibration Monitoring. John Wiley k, Sons, Ltd, 2005.
  • 8. WT. J. Staszewski, C. Boiler, and G. R. Tomlinson, editors. Structural Health Monitoring Systems of Aerospace Structures. John Wiley & Sons, Ltd, 2004.
  • 9. A. Rytter. Vibration Based Inspection of Civil Engineering Structures. PhD thesis, Department of Building Technology and Structural Engineering, Aalborg University, Denmark, 1993.
  • 10. P. C. Chang and S. C. Liu. Recent research in nondestructive evaluation of civil infrastructures. Journal of Materials in Civil Engineering, 15(3):298-304, 2003.
  • 11. A. Żak, M. Radzieński, M. Krawczuk, and W. Ostachowicz. Damage detection strategies based on propagation of guided elastic waves. Smart Materials and Structures, 21(3):035024, 2012.
  • 12. M. Rucka and K. Wilde. Experimental study on ultrasonic monitoring of splitting failure in reinforced concrete. Journal of Nondestructive Evaluation, 32(4):372-383, 2013.
  • 13. M. Rucka and K. WTilde. Non-destructive diagnostics of concrete cantilever beam and slab by impact echo method. Diagnostyka, 3(55):63-68, 2010.
  • 14. A. Raghavan and C. E.S. Cesnik. Review of guided waves structural health monitoring. The Shock and Vibration Digest, 39(2):91—114, 2007.
  • 15. P. Kudela, M. Mieloszyk, and WT. Ostachowicz. Wave propagation based damage assessment and decision making for SUM. In 11th International Conference on Structural Safety and Reliability, ICOSSAR, pages 2383¬2388, New York, NY, June 16-20, 2013.
  • 16. Z.Q. Su, L. Ye, and Y. Lu. Guided lamb waves for identification of damage in composite structures: A review. Journal of Sound and Vibration, 295(3 5):753-780, 2006.
  • 17. R. Ghoni, M. Dollah, A. Sulaiman, and FadhilMamat Ibrahim. Defect characterization based on eddy current technique: Technical review. Advances in Mechanical Engineering, 2014:11, 2014.
  • 18. Z. Ranachowski. Emisja akustyczna w diagnostyce obiektów budowlanych. Drogi i Mosty, 2:151-173, 2012.
  • 19. M. Giordano, A.Calabro, C.Esposito, A. D'Amore, and L. Nicolais. An acoustic-emission characterization of the failure modes in polymer-composite materials. Composites Science and Technology, 58(12):1923-1928, 1998.
  • 20. S. Shahidana, R. P. Norazura, M. Bunnori, and K. M. Holford. Damage classification in reinforced concrete beam by acoustic emission signal analysis. Construction and Building Materials, 45:78-86, 2013.
  • 21. A.A. Carvalho, J.M.A. Rebelio, M.P.V. Souza, L.V.S. Sagrilo, and S.D. Soares. Reliability of non-destructive test techniques in the inspection of pipelines used in the oil industry. International Journal of Pressure Vessels and Piping, 2008.
  • 22. E. Jasinien, R. Raiutis, R. Uteris, A. Voleiis, A. Vladiauskas, D. Mitchard, and M. Amos. NDT of wind turbine blades using adapted ultrasonic and radiographic techniques. Insight, 51 (9): 477-483. 2009.
  • 23. S. Bagavathiappan, B.B. Lahiri, T. Saravanan, John Philip, and T. Jayakumar. Infrared thermography for condition monitoring - a review. Infrared Physics & Technology, 60:35-55, 2013.
  • 24. S. Gontarz and S. Radkowski. Magnetic methods in diagnosis of machines and infrastructural objects—a survey. Diagnostyka, 1:3-11, 2011.
  • 25. F.P.G. Marquez, A.M. Tobias, J.M.P. Perez, and M. Papaelias. Condition monitoring of wind turbines: techniques and methods. Renewable Energy, 46:169-178, 2012.
  • 26. H. Sohn. Effects of environmental and operational variability on structural health monitoring. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365(1851):539-560, 2007.
  • 27. Y. Xia, B. Chen, S. Weng, Y.-Q. Ni, and Y.-L. Xu. Temperature effect on vibration properties of civil structures: a literature review and case studies. Journal of Civil Structural Health Monitoring, 2(l):29-46, 2012.
  • 28. C. Liu and J. T. DeWolf. Effect of temperature on modal variability of a curved concrete bridge under ambient loads. Journal of Civil Structural Health Monitoring, 17(5): 17 12 1751. 2007.
  • 29. G.-D. Zhou and T.-H. Yi. Thermal load in large-scale bridges: A state-of-the-art review. International Journal of Distributed Sensor Networks, 2013:217983.
  • 30. H. Sohn, M. Dzwonczyk, E. G. Straser, A. S. Kiremidjian, K. H. Law, and T. Meng. An experimental study of temperature effect on modal parameters of the Alamosa Canyon bridge. Earthquake Engineering and Structural Dynamics, 28(8):879-897, 1999.
  • 31. M. Sanayei, J. Phelps, J. Sipple, E. Bell, and B. Brenner. Instrumentation, nondestructive testing, and finite-element model updating for bridge evaluation using strain measurements. Journal of Bridge Engineering, 17(1):130-138, 2012.
  • 32. P. Barr, C. Woodward, B. Najera, and M. Amin. Long-term structural health monitoring of the San Ysidro Bridge. Journal of Performance of Constructed Facilities, 20(l):14-20, 2012.
  • 33. E. Bell, P. Lefebvre, M. Sanayei, B. Brenner, J. Sipple, and J. Peddle. Objective load rating of a steel-girder bridge using structural modeling and health monitoring. Journal of Structural Engineering, 139(10):1771-1779, 2013.
  • 34. X. Cao, Y. Sugiyamac, and Y. Mitsui. Application of artificial neural networks to load identification. Computers and Structures, 69(1):63—78, 1998.
  • 35. J. S. P. Cruz and R. Salgado. Performance of vibration-based damage detection methods in bridges. Computer-Aided Civil and Infrastructure Engineering, 24(l):62-79, 2009.
  • 36. G. Hearn and R. Testa. Modal analysis for damage detection in structures. Journal of Structural Engineering, 117(10):3042-3063, 1991.
  • 37. P. Cawley and R. D. Adams. The location of defects in structures from measurements of natural frequencies. Journal of Strain Analysis for Engineering Design, 1 i(2): 19 57. 1979.
  • 38. J.-T. Kim, Y.-S. Ryu, H.-M. Cho, and N. Stubbs. Damage identification in beam-type structures: frequency-based method vs mode-shape-based method. Engineering Structures, 25(1 ):57 67. 2003.
  • 39. M. I. Friswell and J. E. T. Penny. Is damage location using vibration measurements practical? In EUROMECH 365 International Workshop: DAMAS 97, Structural Damage Assessment using Advanced Signal Processing Procedures, pages 351-362, Sheffield, UK, 1997.
  • 40. C. R. Farrar and G.H. James. System identification from ambient vibration measurements on a bridge. Journal of Sound and Vibration, 205:1-18, 1997.
  • 41. O. S. Salawu and C. Williams. Bridge assessment using forced-vibration testing. Journal of Structural Engineering, 121(2):161—173, 1995.
  • 42. L. Fryba and M. Pirner. Load tests and modal analysis of bridges. Engineering Structures, 23:102-109, 2001.
  • 43. A. Z. Khan, A. B. Stanbridge, and D. J. Ewins. Detecting damage in vibrating structures with a scanning LDV. Optics and Lasers in Engineering, 32(6):583-592, 1999.
  • 44. O. S. Salawu and C. Williams. Damage location using vibration mode shapes. In Proceedings of the 12th International Modal Analysis Conference (IMAC XII), pages 933-939, Honolulu, Hawaii, USA, 1994.
  • 45. A. K. Pandey, M. Biswas, and M. M. Samman. Damage detection from changes in curvature mode shapes. Journal of Sound and Vibration, 145(2):321-332, 1991.
  • 46. L. M. Khoo, P. R. Mantena, and P. Jadhav. Structural damage assessment using vibration modal analysis. Structural Health Monitoring, 3(2): 177 194, 2004.
  • 47. A. A. Elshafey, H. Marzouk, and M. R. Haddara. Experimental damage identification using modified mode shape difference. Journal of Marine Science and Application, 10(2):150—155, 2011.
  • 48. M. Cao, M. Radzieński, W. Xu, and W. Ostachowicz. Identification of multiple damage in beams based on robust curvature mode shapes. Mechanical Systems and Signal Processing, 46:468-480, 2014.
  • 49. C. Ratcliffe and W. J. Bagaria. A vibration technique for locating delamination in a composite beam. AIAA Journal, 36(6): 1074-1077, 1998.
  • 50. M. Battipede, R. Ruotolo, and C. Surace 2001. Damage detection of platelike structures. In Proceedings of the 4th International Conference on Damage Assessment of Structures, pages 27-34., Cardiff, Wales, UK, 2001.
  • 51. P. Cornwell, S.W. Doebling, and C.R Farrar. Application of the strain energy damage detection method to plate-like structures. Journal of Sound and Vibration, 224(2):359-374, 1999.
  • 52. W. Fan and P. Qiao. A strain energy-based damage severity correction factor method for damage identification in plate-type structures. Mechanical Systems and Signal Processing, 28:660-678, 2012.
  • 53. N. Stubbs, J. T. Kim, and C. R. Farrar. Field verification of a nondestructive damage localization and severity estimation algorithm. In Proceedings of 13th International Modal Analysis Conference (IMAC XIII), pages 210— 218, Nashville, TN, USA, 1995.
  • 54. N. Stubbs and J. T. Kim. Damage localization in structures without baseline modal parameters. AIAA Journal, 34:1644-1649, 1996.
  • 55. C. Modena, D. Sonda, and D. Zonta. Damage localization in reinforced concrete structures by using damping measurements. In Proceedings of the International conference on damage assessment of structures, DAMAS, pages 132-141, 1999.
  • 56. C. Williams and O. S. Salawu. Damping as a damage indication parameter. Proceeding of the 15th International Modal Analysis Conference (IMAC), Orlando, FL, USA, pages 1531-1536, 1997.
  • 57. G. Kawiecki. Modal damping measurement for damage detection. Smart Materials and Structures, 10(3):466-471, 2001.
  • 58. A. Deraemaeker and A. Preumont. Vibration-based damage detection using large array sensors and spatial filters. Mechanical Systems and Signal Processing, 20(7):1615-1630, 2006.
  • 59. K. Mendrok. Lokalizacja uszkodzenia z wykorzystaniem filtracji modalnej — weryfikacja eksperymentalna. Diagnostyka, 45(l):85-90, 2008.
  • 60. G. L. Slater and S. J. Shelley. Health monitoring of flexible structures using modal filter concepts. Proceedings of SPIE, 1917:997-1008, 1993.
  • 61. K. Mendrok and T. Uhl. The application of modal filters for damage detection. Smart Structures and Systems, 6(2):115—133, 2010.
  • 62. K. Mendrok and T. Uhl. Experimental verification of the damage localization procedure based on modal filtering. Structural Health Monitoring, 10(2):157-171, 2011.
  • 63. T. Uhl and K. Mendrok. Zastosowanie wektorów ritza w diagnostyce konstrukcji. Diagnostyka, 32:23-30, 2004.
  • 64. H. Sohn and K. Law. Damage diagnosis using experimental ritz vectors. Journal of Engineering Mechanics, 127(11):1184-1193, 2001.
  • 65. H. F. Lam, K. V. Yuen, and J. L. Beck. Structural health monitoring via measured ritz vectors utilizing artificial neural networks. Computer-Aided Civil and Infrastructure Engineering, 21(4):232-241, 2006.
  • 66. A.K. Pandey and M. Biswas. Experimental verification of flexibility difference method for locating damage in structures. Journal of Sound and Vibration, 184(2) :311-328, 2005.
  • 67. Q. W. Yang and J. K. Liu. Damage identification by the eigenparameter decomposition of structural flexibility change. International Journal for Numerical Methods in Engineering, 7K( !):!!! 159. 2009.
  • 68. K. C. Park, G. W. Reich, and K. F. Alvin. Structural damage detection using localized flexibilities. Journal of Intelligent Material Systems and Structures, 9(11):911-919, 1998.
  • 69. D. Bernal. Flexibility-based damage localization from stochastic realization results. Journal of Engineering Mechanics, 132(6):651-658, 2006.
  • 70. Z. Duan, G. Yan, J. Ou, and B. F. Spencer. Damage detection in ambient vibration using proportional flexibility matrix with incomplete measured DOFs. Structural Control and Health Monitoring, 14(2): 186-196, 2007.
  • 71. M. Gul and F. N. Catbas. Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications. Mechanical Systems and Signal Processing, 23(7):2192-2204, 2009.
  • 72. D. A. Tibaduiza, L. E. Mujica, and J. Rodellar. Damage classification in structural health monitoring using principal component analysis and self-organizing maps. Structural Control and Health Monitoring, 20(10):1303-1316, 2013.
  • 73. M. Rucka and K. Wilde. Application of continuous wavelet transform in vibration based damage detection method for beams and plates. Journal of Sound and Vibration, 297(3-5) :536-550, 2006.
  • 74. M. Rucka. Damage detection in beams using wavelet transform on higher vibration modes. Journal of Theoretical and Applied Mechanics, 49(2):399-417, 2011.
  • 75. S. W. Doebling, C. R. Farrar, M. B. Prime, and D. W. Shevitz. Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review. Technical Report LA-13070-MS, Los Alamos National Laboratory, Los Alamos, N.M., 1996.
  • 76. S. W. Doebling, C. R. Farrar, and M. B. Prime. A summary review of vibration-based damage identification methods. The Shock and Vibration Digest, 30(2):91-105, March 1998.
  • 77. P. C. Chang, A. Flatau, and S. C. Liu. Review paper: Health monitoring of civil infrastructure. Structural Health Monitoring, 2(3):257 267. 2004.
  • 78. W. Fan and P. Qiao. Vibration-based damage identification methods: A review and comparative study. Structural Health Monitoring, 10(1):83—111, 2011.
  • 79. H. Sohn, C. R. Farrar, F. M. Hemez, D. D. Shunk, D. W. Stinemates, B. R. Nadler, and J. J. Czarnecki. A review of structural health monitoring literature: 1996-2001. Technical Report LA-13976-MS, Los Alamos National Laboratory, 2004.
  • 80. E. P. Carden and P. Fanning. Vibration based condition monitoring: A review. Structural Health Monitoring, 3(4):355-377, 2004.
  • 81. D. Montalvao, N. M. M. Maia, and A. M. R. Ribeiro. A review of vibration-based structural health monitoring with special emphasis on composite materials. The Shock and Vibration Digest, 36(4):295-324, 2006.
  • 82. R. Perera and A. Ruiz. A multi-stage FE updating procedure for damage identification in large scale structures based on multi-objective evolutionary optimization. Mechanical Systems and Signal Processing, 22(4):970-991, 2008.
  • 83. P. F. Liu and J. Y. Zheng. Recent developments on damage modeling and finite element analysis for composite laminates: A review. Materials & Design, 31(8):3825-3834, 2010.
  • 84. Y. An and J. Ou. Experimental and numerical studies on model updating method of damage severity identification utilizing four cost functions. Structural Control & Health Monitoring, 20(1):107-120, 2013.
  • 85. J. Lia, S.S. Law, and Y. Dingc. Substructure damage identification based on response reconstruction in frequency domain and model updating. Engineering Structures, 41:270-284, 2012.
  • 86. I. Benedetti, M. 11. Aliabadi, and A. Milazzo. A fast BEM for the analysis of damaged structures with bonded piezoelectric sensors. Computer Methods in Applied Mechanics and Engineering, 199(9-12):490-501, 2014.
  • 87. F. Zou, I. Benedetti, and M. H. Aliabadi. A boundary element model for structural health monitoring using piezoelectric transducers. Smart Materials and Structures, 23(1): 15 22. 2014.
  • 88. W. Ostachowicz, P. Kudela, M. Krawczuk, and A. Zak. Guided Waves in Structures for SHM: The Time-Domain Spectral Element Method. John Wiley k Sons, Ltd, 2012.
  • 89. W. Ostachowicz. Damage detection of structures using spectral finite element method. Computers and Structures, K6(3 5): 15 1 162. 2008.
  • 90. S. Gopalakrishnan, A. Chakraborty, and D. Roy Mahapatra. Spectral Finite Element Method. Computational Fluid and Solid Mechanics. Springer, 2008.
  • 91. X. Wang and J. Tang. Damage identification using piezoelectric impedance approach and spectral element method. Journal of Intelligent Material Systems and Structures, 20(8):907-921, 2009.
  • 92. M. Krawczuk, M. Palacz, and W. Ostachowicz. The dynamic analysis of a cracked timoshenko beam by the spectral element method. Journal of Sound and Vibration, 264:1139-1153, 2003.
  • 93. M. Rucka. Experimental and numerical study on damage detection in an 1-joint using guided wave propagation. Journal of Sound and Vibration, 329(10):1760-1779, 2010.
  • 94. W. Ostachowicz, M. Krawczuk, and M. Cartmell. The location of a concentrated mass on rectangular plates from measurements of natural vibrations. Computers & Structures, 80(16-17):1419-1428, 2002.
  • 95. M. Friswell and J.E. Mottershead. Finite Element Model Updating in Structural Dynamics. Kluwer Academic, 1995.
  • 96. T. Marwala. Finite Element Model Updating Using Computational Intelligence Techniques. Springer, 2010.
  • 97. J. Nocedal and S. J. Wright. Numerical Optimization. Springer, New York, 2nd edition, 2006.
  • 98. Z. Waszczyszyn and L. Ziemiański. Neurocomputing in the analysis of selected inverse problems of mechanics of structures and materials. Computer Assisted Mechanics and Engineering Sciences (GAMES), 13(1):125—159, 2006.
  • 99. L. E. Mujica Delgado. A hybrid approach of knowledge-based reasoning for structural assessment. PhD thesis, University of Girona, 2006.
  • 100. L. E. Mujica Delgado, J. Vehi, W. Staszewski, and K. Wbrden. Impact damage detection in aircraft composites using knowledge-based reasoning. Structural Health Monitoring: An International Journal, 7(3):215-230, 2008.
  • 101. R. Le Riche, D. Gualandris, J. J. Thomas, and F. Hemez. Neural identification of non-linear dynamic structures. Journal of Sound and Vibration, 248(2) :247-265, 2001.
  • 102. T. Szolc, P. Tauzowski, R. Stocki, and J. Knabel. Damage identification in vibrating rotor-shaft systems by efficient sampling approach. Mechanical Systems and Signal Processing, 23(5):1615—1633, 2009.
  • 103. Z. Waszczyszyn and L. Ziemiański. Neural networks in mechanics of structures and materials - new results and prospects of applications. Computers and Structures, 79(22 25):2261 2276. 2001.
  • 104. T. Uhl and K. Mendrok. Overview of modal model based damage detection methods. In Proc. of the Int'l Conf. on Noise and Vibration Engineering (ISMA2004), pages 561-575, Leuven, Belgium, 20-22 September 2004.
  • 105. N. Lieven and D. Ewins. Spatial correlation of mode shapes, the coordinate modal assurance criterion (COMAC). Proceedings of the 6th International Modal Analysis Conference (IMAG). Kissimmee, FL, USA, pages 690-695, 1988.
  • 106. M. West. Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen. Proceedings of the 4th International Modal Analysis Conference (IMAC), Los Angeles, CA, USA, pages 1-6, 1986.
  • 107. H. Guan and V. Karrbhari. Improved damage detection method based on element modal strain damage index using sparse measurement. Journal of Sound and Vibration, 309(3-6):455-494, 2008.
  • 108. M. Salehi, S. Ziaei-Rad, M. Ghayour, and M. A. Vaziri-Zanjani. A structural damage detection technique based on measured frequency response functions. Contemporary Engineering Sciences, 3(5):215-226, 2010.
  • 109. A. K. Pandey and M. Biswas. Damage detection in structures using changes in flexibility. Journal of Sound and Vibration, 169(1):3-17, 1994.
  • 110. Y. Gao, B. F. Spencer Jr., and D. Bernal. Experimental verification of the flexibility-based damage locating vector method. Journal of Engineering Mechanics, 133(10):1043-1049, 2007.
  • 111. F. Casciati and S. Casciati. Structural health monitoring by lyapunov exponents of non-linear time series. Structural Control and Health Monitoring, 13(1):132-146, 2006.
  • 112. A. Biegus and K. Rykaluk. Collapse of Katowice fair building. Engineering Failure Analysis, 16(5):1643-1654, 2009.
  • 113. O. Caglayan and E. Yuksel. Experimental and finite element investigations on the collapse of a Mero space truss roof structure—A case study. Engineering Failure Analysis, 15(5):458-470, 2008.
  • 114. M. Holicky and M. Sykora. Failures of roofs under snow load: Causes and reliability analysis. Forensic Engineering, 2009:444-453.
  • 115. P. J. Fanning and E. P. Carden. Experimentally validated added mass identification algorithm based on frequency response functions. Journal of Engineering Mechanics, 130(9):1045-1051, 2004.
  • 116. G. Piątkowski and Z. Waszczyszyn. Identification problems of recurrent cascade neural network application in predicting an additional mass location. Computer Assisted Mechanics and Engineering Sciences, 3:217-228, 2011.
  • 117. U. Dackermann, J. Li, and B. Samali. Identification of member connectivity and mass changes on a two-storey framed structure using frequency response functions and artificial neural networks. Journal of Sound and Vibration, 332(16):3636-3653, 2013.
  • 118. E. Papatheou, G. Manson, R.J. Barthorpe, and K. Worden. The use of pseudo-faults for damage location in SHM: An experimental investigation on a Piper Tomahawk aircraft wing. Journal of Sound and Vibration, 333(3):971-990, 2014.
  • 119. K. Dems and Z. Mróz. Damage identification using modal, static and thermographic analysis with additional control parameters. Computers & Structures, 88(21-22):1254-1264, 2010.
  • 120. J. Hou, Ł. Jankowski, and J. Ou. Structural damage identification by adding virtual masses. Structural and Multidisciplinary Optimization, 48(1): 59-72,2013.
  • 121. T. Uhl. The inverse identification problem and its technical application. Archive of Applied Mechanics, 77(5):325-337, May 2007.
  • 122. H. Inoue, J. J. Harrigan, and S. R. Reid. Review of inverse analysis for indirect measurement of impact force. Applied Mechanics Reviews, 54(6) :503-524, 2001.
  • 123. Ł. Jankowski. Off-line identification of dynamic loads. Structural and Multidisciplinary Optimization, 37(6):609-623, 2009.
  • 124. M. Klinkov and C.-P. Fritzen. An updated comparison of the force reconstruction methods. Key Engineering Materials, 347:461-466, 2007.
  • 125. L. E. Mujica, J. Vehi, W. Staszewski, and K. Worden. Impact damage detection in aircraft composites using knowledge-based reasoning. Structural Health Monitoring, 7(3):215-230, 2008.
  • 126. J. Holnicki-Szulc and J. Gierliński. Structural Analysis, Design and Control by the Virtual Distortion Method. John Wiley k, Sons Ltd, Chichester, 1995.
  • 127. G. Suwała and Ł. Jankowski. A model-free method for identification of mass modifications. Structural Control and Health Monitoring, 19(2):216-230, 2012.
  • 128. P. Kołakowski, M. WTikło, and J. Holnicki-Szulc. The virtual distortion method - a versatile reanalysis tool for structures and systems. Structural and Multidisciplinary Optimization, 36(3):217 23 I. 2008.
  • 129. R. Kress. Linear integral equations. Springer, New York, 2nd edition, 1999.
  • 130. P. C. Hansen. Deconvolution and regularization with Toeplitz matrices. Numerical Algorithms, 29(4):323-378, 2002.
  • 131. P. C. Hansen. Rank-deficient and discrete ill-posed problems. Numerical aspects of linear inversion. SIAM, Philadelphia, 1998.
  • 132. P. C. Hansen. Discrete inverse problems: insight and algorithms. SIAM, Philadelphia, 2010.
  • 133. D. I. Papadimitriou and K. C. Giannakoglou. Aerodynamic shape optimization using first and second order adjoint and direct approaches. Archives of Computational Methods in Engineering, 15(4):447-488, 2008.
  • 134. M. Kleiber, H. Antunez, and P. Kowalczyk. Parameter Sensitivity in Nonlinear Mechanics: Theory and Finite Element Computations. John Wiley k, Sons, 1997.
  • 135. G. Suwała and Ł. Jankowski. Nonparametric identification of added masses in frequency domain. In 6th World Conference on Structural Control and Monitoring (6WCSCM 2014), Barcelona, Spain, 15-17 July 2014.
  • 136. J.L. Schiff. The Laplace Transform: Theory and Applications. Springer, 1999.
  • 137. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2nd edition, 2009.
  • 138. M. Rucka and K. Wilde. Neuro-wavelet damage detection technique in beam, plate and shell structures with experimental validation. Journal of Theoretical and Applied Mechanics, 48(3):579-604, 2010.
  • 139. R. R. Goldberg. Methods of real analysis. Wiley, New York, 2nd edition, 1976.
  • 140. L. M. Delves and J. L. Mohamed. Computational methods for integral equations. Cambridge University Press, 1988.
  • 141. E. Beltrami. Sulle funzioni bilineari. Giornale di Mathematiche ad U so delgi Studenti Delle Universita, 11:98-106, 1873.
  • 142. C. Jordan. Mémoire sur les formes bilinéaires. Journal de Mathématiques Pures et Appliquées, 19:35-54, 1874.
  • 143. C. Jordan. Sur la reduction des formes bilinéaires. Comptes Rendus de l’Académie des Sciences, 78:614-617, 1874.
  • 144. J. J. Sylvester. A new proof that a general quadric may be reduced to its canonical form (that is, a linear function of squares) by means of a real orthogonal substitution. Messenger of Mathematics, 19:1-5, 1889.
  • 145. J. J. Sylvester. On the reduction of a bilinear quantic of the nth order to the form of a sum of n products by a double orthogonal subtitution. Messenger of Mathematics, 19:42-46, 1889.
  • 146. J. J. Sylvester. Sur la réduction biorthogonale d'une forme linèo-linèaire à sa forme cannonique. Comptes Rendus de l'Académie des Sciences, 108:651-653, 1889.
  • 147. E. Schmidt. Zur Theorie der linearen und nicht linearen Integralgleichungen. I. Teil: Entwicklung willkürlichen Funktionen nach Systemen vorgeschriebener. Mathematische Annalen, 63:433-476, 1907.
  • 148. H. Weyl. Das asymptotische verteilungsgesetz der eigenwerte linearer partieller differentialgleichungen (mit einer anwendung auf die theorie der hohlraumstrahlung). Mathematische Annalen, 71(4):441-479, 1912.
  • 149. G. W. Stewart. On the early history of the singular value decomposition. SIAM Review, 35(4):551-566, 1993.
  • 150. P. C. Hansen. Regularization tools: A MA II.A I? package for analysis and solution of discrete ill-posed problems. Numerical Algorithms, 6(l):l-35, 1994.
  • 151. P. C. Hansen. The discrete picard condition for discrete ill-posed problems. BIT Numerical Mathematics, 30(4):658-672, 1990.
  • 152. J. L. Humar. Dynamics of Stuctures. CRC Press, 1990.
  • 153. A. N. Tikhonov. Solution of incorrectly formulated problems and the regularization method. Doklady Akademii Nauk SSSR, 151:501-504, 1963.
  • 154. D. L. Phillips. A technique for the numerical solution of certain integral equations of the first kind. Journal of the ACM, 9(l):84-97, 1962.
  • 155. L. Landweber. An iteration formula for Fredholm integral equations of the first kind. American Journal of Mathematics, 73:615-624, 1951.
  • 156. M. R. Hestenes and E. Stiefe. Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6):409-436, 1952.
  • 157. P. J. Davis. Circulant Matrices: Second Edition. American Mathematical Society, 2012.
  • 158. J. Cooley and J. Tukey. An algorithm for the machine calculation of complex Fourier series. Mathematics of computation, 19(90):297-301, 1965.
  • 159. G. H. Golub and C. F. Van Loan. Matrix computations. The John Hopkins University Press, 3rd edition, 1996.
  • 160. P. C. Hansen and D. P. O'Leary. The use of the 1-curve in the regularization of discrete ill-posed problems. SIAM Journal on Scientific Computing, 14(6):1487-1503, 1993.
  • 161. R. Palm. Numerical Comparison of Regularization Algorithms for Solving Ill-Posed Problems. PhD thesis, University of Tartu, January 2010.
  • 162. F. Bauer and M. A. Lukas. Comparing parameter choice methods for regularization of ill-posed problems. Mathematics and Computers in Simulation, 81(9):1795-1841, 2011.
  • 163. P. C. Hansen. Analysis of discrete ill-posed problems by means of the L-curve. SIAM Review, 34(4):561-580, 1992.
  • 164. P.R. Johnston and R.M. Guljarani. Selecting the corner in the L-curve approach to Tikhonov regularization. IEEE Transactions on Biomedical Engineering, 47(9):1293-1296, 2000.
  • 165. P.C. Hansen. The L-curve and its use in the numerical treatment of inverse problems. In P. Johnston, editor, Computational Inverse Problems in Electrocardiology, chapter 5, pages 119-142. WIT Press, 2001.
  • 166. M. Hanke. Limitations of the l-curve method in ill-posed problems. BIT Numerical Mathematics, 36(2):287-301, 1996.
  • 167. MeroForm System USA. M12 System, System k, Function Component Catalogue, http://www.meroform.us/pdf/ml2.pdf. Accessed: 2014-11¬12.
  • 168. M I'S Systems Corporation. 634.25 Axial Extensometers. http://www. mts.com/ucm/groups/public/documents/library/dev_003729.pdf. Accessed: 2014-11-12.
  • 169. A.M. Cohen. Numerical Methods for Laplace Transform, Inversion. Numerical Methods and Algorithms, vol. 5. Springer Science+Business Media, 2007.
  • 170. H. Hassanzadeh and M. Pooladi-Darvish. Comparison of different numerical Laplace inversion methods for engineering applications. Applied Mathematics and Computation, 189(2): 1966-1981, 2007.
  • 171. J. Hou, L. Jankowski, and J. Ou. Experimental study of the substructure isolation method for local health monitoring. Structural Control & Health Monitoring, 19(4):491-510, 2012.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-86c30479-6e40-4957-9b44-e5b1ffe435cb
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.