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Application of an Improved Damage Detection Method for Building Structures Based on Ambient Vibration Measurements

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Abstract

This paper presents a novel modal contribution-based damage identification system for low- to medium-rise buildings with simple and regular geometries using multi-step ambient vibration tests (AVT). The algorithm uses a multi-damage sensitivity feature and MATLAB programming to enable vibration-based structural health monitoring (SHM) of structures. To monitor changes in the data received from accelerometers attached to the structure, the system employs classical and stochastic data processing techniques and frequency-domain decomposition (FDD)-based modal identification. The proposed system uses a Modal Contributing Parameter (MCP) for damage identification and is validated using experimentation and finite element analysis (FEA) on two structures: a three-story steel shear frame and a two-story reinforced concrete (RC) frame building scaled down to 1:6. The study includes various single- and multiple-damage cases for the steel shear frame model, and the proposed algorithm successfully detects the induced damages. Additionally, the shake table tests on the two-story RC frame building show that the proposed algorithm can accurately detect damage and locate the damaged story(s). Overall, the results demonstrate the effectiveness of the proposed modal contribution-based damage identification system for condition monitoring and damage detection of building structures.

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References

  1. Alvandi, A.; Cremona, C.: Assessment of vibration-based damage identification techniques. J. Sound Vib. 292(1–2), 179–202 (2006)

    Google Scholar 

  2. Deraemaeker, A.; Reynders, E.; De Roeck, G.; Kullaa, J.: Vibration-based structural health monitoring using output-only measurements under changing environment. Mech. Syst. Signal Process. 22(1), 34–56 (2008)

    Google Scholar 

  3. Caetano, E.; Cunha, Á.; Magalhães, F.; Moutinho, C.: Studies for controlling human-induced vibration of the Pedro e Inês footbridge, Portugal. Part 1: assessment of dynamic behaviour. Eng. Struct. 32(4), 1069–1081 (2010)

    Google Scholar 

  4. Caetano, E.; Cunha, Á.; Moutinho, C.; Magalhães, F.: Studies for controlling human-induced vibration of the Pedro e Inês footbridge, Portugal. Part 2: implementation of tuned mass dampers. Eng. Struct. 32(4), 1082–1091 (2010)

    Google Scholar 

  5. Magalhães, F.; Cunha, Á.; Caetano, E.: Vibration based structural health monitoring of an arch bridge: from automated OMA to damage detection. Mech. Syst. Signal Process. 28, 212–228 (2012)

    Google Scholar 

  6. Hu, W.-H.; Caetano, E.; Cunha, Á.: Structural health monitoring of a stress-ribbon footbridge. Eng. Struct. 57, 578–593 (2013)

    Google Scholar 

  7. Cabboi, A.; Magalhães, F.; Gentile, C.; Cunha, Á.: Automated modal identification and tracking: application to an iron arch bridge. Struct. Control. Health Monit. 24(1), e1854 (2017)

    Google Scholar 

  8. Wang, H.; Mao, J.-X.; Xu, Z.-D.: Investigation of dynamic properties of a long-span cable-stayed bridge during typhoon events based on structural health monitoring. J. Wind Eng. Ind. Aerodyn. 201, 104172 (2020)

    Google Scholar 

  9. Devriendt, C.; Magalhães, F.; Weijtjens, W.; De Sitter, G.; Cunha, Á.; Guillaume, P.: Structural health monitoring of offshore wind turbines using automated operational modal analysis. Struct. Health Monit. 13(6), 644–659 (2014)

    Google Scholar 

  10. Oliveira, G.; Magalhaes, F.; Cunha, Á.; Caetano, E.: Development and implementation of a continuous dynamic monitoring system in a wind turbine. J. Civ. Struct. Heal. Monit. 6(3), 343–353 (2016)

    Google Scholar 

  11. Martins, N.; Caetano, E.; Diord, S.; Magalhães, F.; Cunha, Á.: Dynamic monitoring of a stadium suspension roof: Wind and temperature influence on modal parameters and structural response. Eng. Struct. 59, 80–94 (2014)

    Google Scholar 

  12. Gentile, C.; Guidobaldi, M.; Saisi, A.: One-year dynamic monitoring of a historic tower: damage detection under changing environment. Meccanica 51(11), 2873–2889 (2016)

    Google Scholar 

  13. Doebling, S.W.; Farrar, C.R.; Prime, M.B.; Shevitz, D.W.: Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Science 5, 87 (1996)

    Google Scholar 

  14. Doebling, S.W.; Farrar, C.R.; Prime, M.B.: A summary review of vibration-based damage identification methods. Shock Vib. Digest 30(2), 91–105 (1998)

    Google Scholar 

  15. Sohn, H.; Farrar, C.R.; Hemez, F.M.; Shunk, D.D.; Stinemates, D.W.; Nadler, B.R.; Czarnecki, J.J.: A Review of Structural Health Monitoring Literature: 1996–2001, p. 1. Los Alamos National Laboratory, Los Alamos (2003)

    Google Scholar 

  16. Vahidi, M.; Vahdani, S.; Rahimian, M.; Jamshidi, N.; Kanee, A.T.: Evolutionary-base finite element model updating and damage detection using modal testing results. Struct. Eng. Mech. 70(3), 339–350 (2019)

    Google Scholar 

  17. Cara, F.J.; Carpio, J.; Juan, J.; Alarcón, E.: An approach to operational modal analysis using the expectation maximization algorithm. Mech. Syst. Signal Process. 31, 109–129 (2012)

    Google Scholar 

  18. Andersen, E.; Pedersen, L. Structural monitoring of the great belt east bridge. In: Symposium on Strait Crossings (Year).

  19. Pines, D.; Aktan, A.E.: Status of structural health monitoring of long-span bridges in the United States. Prog. Struct. Mat. Eng. 4(4), 372–380 (2002)

    Google Scholar 

  20. Mufti, A.A.: Structural health monitoring of innovative Canadian civil engineering structures. Struct. Health Monit. 1(1), 89–103 (2002)

    Google Scholar 

  21. Ou, J. The state-of-the-art and application of intelligent health monitoring systems for civil infrastructures in mainland of China. In: Progress in Structural Engineering, Mechanics and Computation, pp. 599–608 (2004).

  22. Yun, C.-B.; Lee, J.-J.; Kim, S.-K.; Kim, J.-W.: Recent R&D activities on structural health monitoring for civil infra-structures in Korea. KSCE J. Civ. Eng. 7(6), 637–651 (2003)

    Google Scholar 

  23. Wong, K.Y.: Instrumentation and health monitoring of cable-supported bridges. Struct. Control. Health Monit. 11(2), 91–124 (2004)

    Google Scholar 

  24. Farrar, C.R.; Worden, K.: An introduction to structural health monitoring. New Trends Vib. Based Struct. Health Monit. 5, 1–17 (2010)

    Google Scholar 

  25. Lynch, J.P.; Loh, K.J.: A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib. Digest 38(2), 91–130 (2006)

    Google Scholar 

  26. Ubertini, F.; Cavalagli, N.; Kita, A.; Comanducci, G.: Assessment of a monumental masonry bell-tower after 2016 Central Italy seismic sequence by long-term SHM. Bull. Earthq. Eng. 16(2), 775–801 (2018)

    Google Scholar 

  27. Venanzi, I.; Kita, A.; Cavalagli, N.; Ierimonti, L.; Ubertini, F.: Earthquake-induced damage localization in an historic masonry tower through long-term dynamic monitoring and FE model calibration. Bull. Earthq. Eng. 18(5), 2247–2274 (2020)

    Google Scholar 

  28. Pai, P.F.; Young, L.G.: Damage detection of beams using operational deflection shapes. Int. J. Solids Struct. 38(18), 3161–3192 (2001)

    MATH  Google Scholar 

  29. Dinh, H.; Nagayama, T.; Fujino, Y.: Structural parameter identification by use of additional known masses and its experimental application. Struct. Control. Health Monit. 19(3), 436–450 (2012)

    Google Scholar 

  30. Bisht, S.S.; Singh, M.P.: Detecting sudden changes in stiffness using high-pass filters. Struct. Control. Health Monit. 19(3), 319–331 (2012)

    Google Scholar 

  31. Omrani, R.; Hudson, R.E.; Taciroglu, E.: Story-by-story estimation of the stiffness parameters of laterally-torsionally coupled buildings using forced or ambient vibration data: I. Formulation and verification. Earthq. Eng. Struct. Dyn. 41(12), 1609–1634 (2012)

    Google Scholar 

  32. Amezquita-Sanchez, J.P.; Adeli, H.: Signal processing techniques for vibration-based health monitoring of smart structures. Arch. Comput. Methods Eng. 23(1), 1–15 (2016)

    MathSciNet  MATH  Google Scholar 

  33. Fan, W.; Qiao, P.: Vibration-based damage identification methods: a review and comparative study. Struct. Health Monit. 10(1), 83–111 (2011)

    Google Scholar 

  34. Yan, Y.; Cheng, L.; Wu, Z.; Yam, L.: Development in vibration-based structural damage detection technique. Mech. Syst. Signal Process. 21(5), 2198–2211 (2007)

    Google Scholar 

  35. Topole, K.G.; Stubbs, N.: Non-destructive damage evaluation of a structure from limited modal parameters. Earthq. Eng. Struct. Dynam. 24(11), 1427–1436 (1995)

    Google Scholar 

  36. Kim, J.-T.; Stubbs, N.: Improved damage identification method based on modal information. J. Sound Vib. 252(2), 223–238 (2002)

    Google Scholar 

  37. Xia, Y.; Hao, H.; Brownjohn, J.M.; Xia, P.Q.: Damage identification of structures with uncertain frequency and mode shape data. Earthq. Eng. Struct. Dynam. 31(5), 1053–1066 (2002)

    Google Scholar 

  38. Garcia-Palencia, A.; Santini-Bell, E.; Gul, M.; Catbas, N.: A FRF-based algorithm for damage detection using experimentally collected data. Struct. Monit. Maint 2(4), 399–418 (2015)

    Google Scholar 

  39. Domaneschi, M.; Sigurdardottir, D.; Glisic, B.: Damage detection on output-only monitoring of dynamic curvature in composite decks. Struct. Monit. Maint. 4(1), 1–15 (2017)

    Google Scholar 

  40. Mei, Q.; Gül, M.; Boay, M.: Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis. Mech. Syst. Signal Process. 119, 523–546 (2019)

    Google Scholar 

  41. Nguyen, D.H.; Nguyen, Q.B.; Bui-Tien, T.; De Roeck, G.; Wahab, M.A.: Damage detection in girder bridges using modal curvatures gapped smoothing method and Convolutional Neural Network: application to Bo Nghi bridge. Theoret. Appl. Fract. Mech. 109, 102728 (2020)

    Google Scholar 

  42. Zhang, L.; Wu, G.; Cheng, X.: A rapid output-only damage detection method for highway bridges under a moving vehicle using long-gauge strain sensing and the fractal dimension. Measurement 158, 107711 (2020)

    Google Scholar 

  43. Providakis, C.; Stefanaki, K.; Voutetaki, M.; Tsompanakis, J.; Stavroulaki, M.: A near and far-field monitoring technique for damage detection in concrete structures. Struct. Monit. Maint. 1(2), 159–171 (2014)

    Google Scholar 

  44. Homaei, F.; Shojaee, S.; Amiri, G.G.: A direct damage detection method using multiple damage localization index based on mode shapes criterion. Struct. Eng. Mech. 49(2), 183–202 (2014)

    Google Scholar 

  45. Yazdanpanaha, O.; Seyedpoor, S.: A new damage detection indicator for beams based on mode shape data. Struct. Eng. Mech. 53(4), 725–744 (2015)

    Google Scholar 

  46. Porcu, M.; Patteri, D.; Melis, S.; Aymerich, F.: Effectiveness of the FRF curvature technique for structural health monitoring. Constr. Build. Mater. 226, 173–187 (2019)

    Google Scholar 

  47. Liu, H.; Yu, L.; Luo, Z.; Chen, Z.: Multi-strategy structural damage detection based on included angle of vectors and sparse regularization. Struct. Eng. Mech. 75(4), 415–424 (2020)

    Google Scholar 

  48. Pedram, M.; Esfandiari, A.; Khedmati, M.R.: Damage detection by a FE model updating method using power spectral density: numerical and experimental investigation. J. Sound Vib. 397, 51–76 (2017)

    Google Scholar 

  49. Dinh-Cong, D.; Nguyen-Thoi, T.; Nguyen, D.T.: A FE model updating technique based on SAP2000-OAPI and enhanced SOS algorithm for damage assessment of full-scale structures. Appl. Soft Comput. 89, 106100 (2020)

    Google Scholar 

  50. Mousavi, Z.; Ettefagh, M.M.; Sadeghi, M.H.; Razavi, S.N.: Developing deep neural network for damage detection of beam-like structures using dynamic response based on FE model and real healthy state. Appl. Acoust. 168, 107402 (2020)

    Google Scholar 

  51. Capecchi, D.; Vestroni, F.: Monitoring of structural systems by using frequency data. Earthq. Eng. Struct. Dynam. 28(5), 447–461 (1999)

    Google Scholar 

  52. Cawley, P.; Adams, R.D.: The location of defects in structures from measurements of natural frequencies. J. Strain Anal. Eng. Des. 14(2), 49–57 (1979)

    Google Scholar 

  53. Li, Y.; Chen, Y.: A review on recent development of vibration-based structural robust damage detection. Struct. Eng. Mech. Int. J. 45(2), 159–168 (2013)

    Google Scholar 

  54. Salawu, O.: Detection of structural damage through changes in frequency: a review. Eng. Struct. 19(9), 718–723 (1997)

    Google Scholar 

  55. Allemang, R. J. A correlation coefficient for modal vector analysis. In: Proceedings of 1st International Modal Analysis Conference.

  56. Frigui, F.; Faye, J.-P.; Martin, C.; Dalverny, O.; Pérès, F.; Judenherc, S.: Global methodology for damage detection and localization in civil engineering structures. Eng. Struct. 171, 686–695 (2018)

    Google Scholar 

  57. Tamura, Y.; Suganuma, S.-y: Evaluation of amplitude-dependent damping and natural frequency of buildings during strong winds. J. Wind Eng. Ind. Aerodynam. 59(2–3), 115–130 (1996)

    Google Scholar 

  58. Li, Q.; Zhi, L.; Yi, J.; To, A.; Xie, J.: Monitoring of typhoon effects on a super-tall building in Hong Kong. Struct. Control. Health Monit. 21(6), 926–949 (2014)

    Google Scholar 

  59. Guo, Y.; Kwon, D.K.; Kareem, A.: Near-real-time hybrid system identification framework for civil structures with application to Burj Khalifa. J. Struct. Eng. 142(2), 04015132 (2016)

    Google Scholar 

  60. Mehboob, S.; Zaman Khan, Q.; Ahmad, S.: Numerical study for evaluation of a vibration based damage index for effective damage detection. Bull. Polish Acad. Sci. Tech. Sci. 5, 1443–1456 (2020)

    Google Scholar 

  61. Chopra, AK: Dynamics of structures. Pearson Education India. (2007)

  62. Brincker, R, Ventura, C: Introduction to operational modal analysis. John Wiley & Sons. (2015)

  63. https://www.investopedia.com/terms/t/t-test.asp (Accessed: August 2021).

  64. Tamuraa, Y.; Matsuib, M.; Pagninic, L.-C.; Ishibashid, R.; Yoshidaa, A.: Measurement of wind-induced response of buildings using RTK-GPS. J. Wind Eng. Ind. Aerodyn. 90, 1783–1793 (2002)

    Google Scholar 

  65. Whelan, M.J.; Gangone, M.V.; Janoyan, K.D.; Jha, R.: Real-time wireless vibration monitoring for operational modal analysis of an integral abutment highway bridge. Eng. Struct. 31(10), 2224–2235 (2009)

    Google Scholar 

  66. Shi, W.; Shan, J.; Lu, X.: Modal identification of Shanghai World Financial Center both from free and ambient vibration response. Eng. Struct. 36, 14–26 (2012)

    Google Scholar 

  67. Foti, D.; Diaferio, M.; Giannoccaro, N.I.; Mongelli, M.: Ambient vibration testing, dynamic identification and model updating of a historic tower. NDT & E Int. 47, 88–95 (2012)

    Google Scholar 

  68. Le, T.-P.; Paultre, P.: Modal identification based on continuous wavelet transform and ambient excitation tests. J. Sound Vib. 331, 2023–2037 (2012)

    Google Scholar 

  69. Park, H.S.; Oh, B.K.: Damage detection of building structures under ambient excitation through the analysis of the relationship between the modal participation ratio and story stiffness. J. Sound Vib. 418, 122–143 (2018)

    Google Scholar 

  70. Seon Park, H.; Kim, J.; Oh, B.K.: Model updating method for damage detection of building structures under ambient excitation using modal participation ratio. Measurement 133, 251–261 (2019)

    Google Scholar 

  71. Le, T.-P.; Paultre, P.: Modal identification based on continuous wavelet transform and ambient excitation tests. J. Sound Vib. 331(9), 2023–2037 (2012)

    Google Scholar 

  72. Rainieri, C.; Fabbrocino, G.: Operational modal analysis of civil engineering structures. Springer 142, 143 (2014)

    Google Scholar 

  73. Mei, L.; Li, H.; Zhou, Y.; Li, D.; Long, W.; Xing, F.: Output-only damage detection of shear building structures using an autoregressive model-enhanced optimal subpattern assignment metric. Sensors (Basel) 20(7), 89 (2020)

    Google Scholar 

  74. Do, N.T.; Gül, M.: A time series based damage detection method for obtaining separate mass and stiffness damage features of shear-type structures. Eng. Struct. 208, 72 (2020)

    Google Scholar 

  75. Niu, Z.: Two-step structural damage detection method for shear frame structures using FRF and Neumann series expansion. Mech. Syst. Signal Process. 5, 149 (2021)

    Google Scholar 

  76. Kang, J.S.; Park, S.-K.; Shin, S.; Lee, H.S.: Structural system identification in time domain using measured acceleration. J. Sound Vib. 288(1–2), 215–234 (2005)

    MATH  Google Scholar 

  77. Carrillo, J.; Vargas, D.; Sánchez, M.: Stiffness degradation model of thin and lightly reinforced concrete walls for housing. Eng. Struct. 168, 179–190 (2018)

    Google Scholar 

  78. Kwon, J.; Ghannoum, W.M.: Assessment of international standard provisions on stiffness of reinforced concrete moment frame and shear wall buildings. Eng. Struct. 128, 149–160 (2016)

    Google Scholar 

  79. Brincker, R.; Zhang, L.; Andersen, P.: Modal identification of output-only systems using frequency domain decomposition. Smart Mater. Struct. 10(3), 441 (2001)

    Google Scholar 

  80. Harris, H.G.; Sabnis, G.: Structural Modeling and Experimental Techniques. CRC Press, London (1999)

    Google Scholar 

  81. Mander, J.; Priestley, M.; Park, R.: Observed stress-strain behavior of confined concrete. J. Struct. Eng. 114(8), 1827–1849 (1988)

    Google Scholar 

  82. Ghannadi, P.; Kourehli, S.S.: Model updating and damage detection in multi-story shear frames using Salp Swarm Algorithm. Earthq. Struct. 17(1), 63–73 (2019)

    Google Scholar 

  83. Nozari, A.; Behmanesh, I.; Yousefianmoghadam, S.; Moaveni, B.; Stavridis, A.: Effects of variability in ambient vibration data on model updating and damage identification of a 10-story building. Eng. Struct. 151, 540–553 (2017)

    Google Scholar 

  84. Genç, A.F.; Ergün, M.; Günaydin, M.; Altunişik, A.C.; Ateş, Ş; Okur, F.Y.; Mosallam, A.S.: Dynamic analyses of experimentally-updated FE model of historical masonry clock towers using site-specific seismic characteristics and scaling parameters according to the 2018 Turkey building earthquake code. Eng. Fail. Anal. 105, 402–426 (2019)

    Google Scholar 

  85. Günaydin, M.: Seismic performance evaluation of a fire-exposed historical structure using an updated finite element model. Eng. Failure Anal. 5, 106 (2019)

    Google Scholar 

  86. Mojtahedi, A.; Hokmabady, H.; Yaghubzadeh, A.; Mohammadyzadeh, S.: An improved model reduction-modal based method for model updating and health monitoring of an offshore jacket-type platform. Ocean Eng. 4, 209 (2020)

    Google Scholar 

  87. Pan, Y.; Ventura, C.E.; Xiong, H.; Zhang, F.-L.: Model updating and seismic response of a super tall building in Shanghai. Comput. Struct. 9, 239 (2020)

    Google Scholar 

  88. Mander, J.B.; Priestley, M.J.; Park, R.: Theoretical stress-strain model for confined concrete. J. Struct. Eng. 114(8), 1804–1826 (1988)

    Google Scholar 

  89. Madas, P.J.: Advanced modelling of composite frames subject to earthquake loading. Science 5, 41 (1993)

    Google Scholar 

  90. Martínez-Rueda, J.E.; Elnashai, A.S.: Confined concrete model under cyclic load. Mater. Struct. 30(3), 139–147 (1997)

    Google Scholar 

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Acknowledgements

The research was financially supported by the Higher Education Commission (HEC) of Pakistan under Grant No. NRPU 3820.

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  1. Department of Civil Engineering, University of Engineering and Technology, Taxila, Pakistan

    Saqib Mehboob & Qaiser uz Zaman Khan

  2. Works and Services Organization, Islamabad, Pakistan

    Sohaib Ahmad

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  1. Saqib Mehboob
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Correspondence to Saqib Mehboob.

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Mehboob, S., Khan, Q.u.Z. & Ahmad, S. Application of an Improved Damage Detection Method for Building Structures Based on Ambient Vibration Measurements. Arab J Sci Eng 48, 13259–13281 (2023). https://doi.org/10.1007/s13369-023-07713-z

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