Structural Engineering and Mechanics

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Damage detection in Ca-Non Bridge using transmissibility and artificial neural networks

  • Nguyen, Duong H. (Department of Electrical energy, metals, mechanical constructions and systems, Faculty of Engineering and Architecture, Ghent University) ;
  • Bui, Thanh T. (University of Transport and Communications) ;
  • De Roeck, Guido (KU Leuven, Department of Civil Engineering, Structural Mechanics) ;
  • Wahab, Magd Abdel (Division of Computational Mechanics, Ton Duc Thang University)
  • Received : 2019.02.15
  • Accepted : 2019.03.26
  • Published : 2019.07.25
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Abstract

This paper deals with damage detection in a girder bridge using transmissibility functions as input data to Artificial Neural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage in a bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridge was used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a moving truck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train the ANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network can be used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique to real bridge data is potentially high.

Keywords

References

  1. Beale, M.H., Hagan, M.T. and Demuth, H.B. (1992), "Neural Network $Toolbox^{TM}$ User's Guide", The Mathworks Inc., MA, USA.
  2. Cao, H., Zhou, Y.L., Chen, Z. and Abdel Wahab, M. (2017), "Form-finding analysis of suspension bridges using an explicit iterative approach", Struct. Eng. Mech., 62(1), 85-95. https://doi.org/10.12989/sem.2017.62.1.085.
  3. Chung, W. and Sotelino, E.D. (2006), "Three-dimensional finite element modeling of composite girder bridges", Eng. Struct., 28(1), 63-71. https://doi.org/10.1016/j.engstruct.2005.05.019.
  4. CSI (2002), SAP2000 V-14: Integrated Finite Element Analysis and Design of Structures Basic Analysis Reference Manual, Computers and Structures Inc, Berkeley, CA, USA.
  5. Demuth, H. and Beale, M. (2009), "Matlab neural network toolbox user's guide version 6", The MathWorks Inc., MA, USA.
  6. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based damage identification methods", Shock Vib. Digest, 30, 91-105. https://doi.org/10.1177/058310249803000201
  7. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz, D.W. (1996), "Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review", https://doi.org/10.2172/249299.
  8. Gillich, G.R., Furdui, H., Wahab, M.A. and Korka, Z.I. (2019), "A robust damage detection method based on multi-modal analysis in variable temperature conditions", Mech. Syst. Signal Process., 115, 361-379. https://doi.org/10.1016/j.ymssp.2018.05.037
  9. Gonzalez-Perez, C. and Valdes-Gonzalez, J. (2011), "Identification of structural damage in a vehicular bridge using artificial neural networks", Struct. Health Monitor., 10, 33-48. https://doi.org/10.1177/1475921710365416.
  10. Gul, M. and Catbas, F.N. (2010), "Damage assessment with ambient vibration data using a novel time series analysis methodology", J. Struct. Eng., 137, 1518-26. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000366.
  11. Hakim, S. and Abdul Razak, H. (2013), "Adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANNs) for structural damage identification", Struct. Eng. Mech., 45(6), 779-802. http://dx.doi.org/10.12989/sem.2013.45.6.779.
  12. Khatir, S. and Abdel Wahab, M. (2018), "Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm", Eng. Fracture Mech., 205, https://doi.org/10.1016/j.engfracmech.2018.09.032.
  13. Khatir, S., Dekemele, K., Loccufier, M., Khatir, T. and Abdel Wahab, M. (2018), "Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization", Comptes Rendus Mecanique 346(2), 110-120. https://doi.org/10.1016/j.crme.2017.11.008.
  14. Khuc, T. and Catbas, F.N. (2017), "Computer vision-based displacement and vibration monitoring without using physical target on structures", Struct. Infrastructure Eng., 13(4), 505-516. https://doi.org/10.1080/15732479.2016.1164729.
  15. Kong, X., Cai, C. and Kong, B. (2014), "Damage detection based on transmissibility of a vehicle and bridge coupled system", J. Eng. Mech., 141(1), https://doi.org/10.1061/(ASCE)EM.1943-7889.0000821.
  16. Maia, N., Silva, J. and Ribeiro, A. (2001), "The transmissibility concept in multi-degree-of-freedom systems", Mech. Syst. Signal Process., 15(1), 129-37. https://doi.org/10.1006/mssp.2000.1356.
  17. Maia, N.M., Urgueira, A.P. and Almeida, R.A. (2011), "Whys and wherefores of transmissibility", Vibration Analysis and Control-New Trends and Developments, IntechOpen Limited, London, Uniuted Kingdom.
  18. Meruane, V. and Mahu, J. (2014), "Real-time structural damage assessment using artificial neural networks and antiresonant frequencies", Shock Vib., 2014. http://dx.doi.org/10.1155/2014/653279.
  19. Miyamoto, A. and Yabe, A. (2011), "Bridge condition assessment based on vibration responses of passenger vehicle", J. Physics Conf. Series, 305, https://doi.org/10.1088/1742-6596/305/1/012103.
  20. Nanthakumar, S., Lahmer, T., Zhuang, X., Zi, G. and Rabczuk, T. (2016), "Detection of material interfaces using a regularized level set method in piezoelectric structures", Inverse Problems Sci. Eng., 24(1), 153-176. https://doi.org/10.1080/17415977.2015.1017485.
  21. Neves, A., Gonzalez, I., Leander, J. and Karoumi, R. (2017), "Structural health monitoring of bridges: a model-free ANNbased approach to damage detection", J. Civil Struct. Health Monitor., 7(5), 689-702. https://doi.org/10.1007/s13349-017-0252-5.
  22. Posenato, D., Lanata, F., Inaudi, D. and Smith, I.F. (2008), "Model-free data interpretation for continuous monitoring of complex structures", Adv. Eng. Informatics, 22(1), 135-144. https://doi.org/10.1016/j.aei.2007.02.002.
  23. Qin, S., Zhou, Y.L., Cao, H. and Wahab, M.A. (2018), "Model updating in complex bridge structures using kriging model ensemble with genetic algorithm", KSCE J. Civil Eng., 22(1), 3567-3578. https://doi.org/10.1007/s12205-017-1107-7.
  24. Salawu, O. (1997), "Detection of structural damage through changes in frequency: A review", Eng. Struct., 19(9), 718-723. https://doi.org/10.1016/S0141-0296(96)00149-6.
  25. Samir, K., Brahim, B., Capozucca, R. and Abdel Wahab, M. (2018), "Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm", Compos. Struct., 187, 344-353. https://doi.org/10.1016/j.compstruct.2017.12.058.
  26. Shi, J., Xu, X., Wang, J. and Li, G. (2010), "Beam damage detection using computer vision technology", Nondestructive Testing Evaluation, 25, 189-204. https://doi.org/10.1080/10589750903242525.
  27. Specht, D.F. (1991), "A general regression neural network", IEEE Transactions on Neural Networks, 2(6), 568-576. https://doi.org/10.1109/72.97934.
  28. Tiachacht, S., Bouazzouni, A., Khatir, S., Abdel Wahab, M., Behtani, A. and Capozucca, R. (2018), "Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm", Eng. Struct., 177, 421-430. https://doi.org/10.1016/j.engstruct.2018.09.070.
  29. Urgueira, A.P., Almeida, R.A. and Maia, N.M. (2011), "On the use of the transmissibility concept for the evaluation of frequency response functions", Mech. Syst. Signal Process., 25(3), 940-951. https://doi.org/10.1016/j.ymssp.2010.07.015.
  30. Vu-Bac, N., Duong, T., Lahmer, T., Zhuang, X., Sauer, R., Park, H. and Rabczuk, T. (2018), "A NURBS-based inverse analysis for reconstruction of nonlinear deformations of thin shell structures", Comput. Method. Appl. Mech. Eng., 331, 427-455. https://doi.org/10.1016/j.cma.2017.09.034.
  31. Worden, K. and Manson, G. (2007), "The application of machine learning to structural health monitoring", Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 365, 515-537. https://doi.org/10.1098/rsta.2006.1938.
  32. Yang, C. and Oyadiji, S.O. (2017), "Damage detection using modal frequency curve and squared residual wavelet coefficients-based damage indicator", Mech. Syst. Signal Process., 83, 385-405. https://doi.org/10.1016/j.ymssp.2016.06.021.
  33. Yang, Y. and Chang, K. (2009), "Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique", J. Sound Vib., 322(4-5), 718-739. https://doi.org/10.1016/j.jsv.2008.11.028.
  34. Yang, Y., Li, Y. and Chang, K. (2014), "Constructing the mode shapes of a bridge from a passing vehicle: a theoretical study", Smart Struct. Syst., 13(5), 797-819. http://dx.doi.org/10.12989/sss.2014.13.5.797.
  35. Yang, Z., Yu, Z. and Sun, H. (2007), "On the cross correlation function amplitude vector and its application to structural damage detection", Mech. Syst. Signal Process., 21, 2918-2932. https://doi.org/10.1016/j.ymssp.2007.03.004
  36. Yin, Z., Liu, J., Luo, W. and Lu, Z. (2018), "An improved Big Bang-Big Crunch algorithm for structural damage detection", Struct. Eng. Mech., 68(6), 735-745. http://dx.doi.org/10.12989/sem.2018.68.6.735.
  37. Zang, C. and Imregun, M. (2001), "Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection", J. Sound Vib., 242(5), 813-827. https://doi.org/10.1006/jsvi.2000.3390.
  38. Zapico, J.L., GonzALez, M.P. and Worden, K. (2003), "Damage assessment using neural networks", Mech. Syst. Signal Process., 17, 119-125. https://doi.org/10.1006/mssp.2002.1547.
  39. Zheng, T., Liu, J., Luo, W. and Lu, Z. (2018), "Structural damage identification using cloud model based fruit fly optimization algorithm", Struct. Eng. Mech., 67, 245-254. http://dx.doi.org/10.12989/sem.2018.67.3.245.
  40. Zhou, Y.-L. and Abdel Wahab, M. (2017), "Cosine based and extended transmissibility damage indicators for structural damage detection", Eng. Struct. 141, 175-183. https://doi.org/10.1016/j.engstruct.2017.03.030.
  41. Zhou, Y.L., Cao, H., Liu, Q. and Wahab, M.A. (2017), "Outputbased structural damage detection by using correlation analysis together with transmissibility", Materials, 10(8), 866. https://doi.org/10.3390/ma10080866.
  42. Zhou, Y.L., Maia, N.M.M., Sampaio, R. and Wahab, M.A. (2016), "Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure", Struct. Health Monitor., 16(6), 711-731. https://doi.org/10.1177/1475921716680849.
  43. Zhou, Y.L. and Wahab, M.A. (2017), "Damage detection using vibration data and dynamic transmissibility ensemble with autoassociative neural network", Mechanika, 23(5), 688-695. http://dx.doi.org/10.5755/j01.mech.23.5.15339.

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