Skip to main content
SpringerLink
Log in
Book cover

Data Mining in Structural Dynamic Analysis pp 25–40Cite as

Structural Health Monitoring of Periodic Infrastructure: A Review and Discussion

  • Chapter
  • First Online:

Abstract

Periodic structure has obtained wide applications in various infrastructures. The structural health monitoring of periodic infrastructures is motivated by the facts that in-service infrastructures are damage-prone, while traditional inspection and nondestructive evolution hardly meet the requirements in continuous surveillance, timely warning and assessment of anomalies, and cost-effective maintenance. In this chapter, the fundamental principles and applications of the periodic structure are first introduced. Then, the recent research activities on the health monitoring of periodic infrastructures using data mining are summarized. It is followed by a review of instantaneous baseline structural health monitoring that was originally presented for diminishing the vulnerability of anomaly detection performance to environmental and operational conditions. Investigations on structural health monitoring using the inherent property of periodic structure are subsequently reviewed, and none of them incorporates both instantaneous baseline and advanced data mining techniques for the anomaly identification oriented classification, prediction, and optimization. Based on the state-of-the-art review, discussions about current investigations and suggestions for future studies are provided in the final section.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. LMC Steel Buildings Homepage. http://lmcsteel.com/warranty-information/steel_framework2. Accessed 30 Apr 2019

  2. HighestBridges.com Homepage. http://highestbridges.com/wiki/index.php?title=China_2012_Bridge_Trip. Accessed 30 Apr 2019

  3. JSCE Homepage. http://www.jsce-int.org/a_t/achievement/civil/2011. Accessed 30 Apr 2019

  4. DPrint.com Homepage. http://lmcsteel.com/warranty-information/steel_framework2/. Accessed 30 Apr 2019

  5. Friis, L., Ohlrich, M.: Coupling of flexural and longitudinal wave motion in a periodic structure with asymmetrically arranged transverse beams. J. Acoust. Soc. Am. 118, 3010–3020 (2005)

    ADS  Google Scholar 

  6. Mead, D.J.: Wave propagation and natural modes in periodic systems: I. Mono-coupled systems. J. Sound Vib. 40, 1–18 (1975)

    ADS  MathSciNet  MATH  Google Scholar 

  7. Mead, D.J.: Wave propagation and natural modes in periodic systems: II. Multi-coupled systems, with and without damping. J. Sound Vib. 40, 19–39 (1975)

    ADS  MATH  Google Scholar 

  8. Yun, Y., Mak, C.M.: A study of coupled flexural-longitudinal wave motion in a periodic dual-beam structure with transverse connection. J. Acoust. Soc. Am. 126(1), 114–121 (2009)

    ADS  Google Scholar 

  9. Wang, X., Mak, C.M.: Acoustic performance of a duct loaded with identical resonators. J. Acoust. Soc. Am. 131(4), 316–322 (2012)

    ADS  Google Scholar 

  10. Wang, J.F., Mak, C.M., Yun, Y.: A methodology for direct identification of characteristic wave-types in a finite periodic dual-layer structure with transverse connection. J. Vib. Control 18(9), 1406–1414 (2012)

    Google Scholar 

  11. Lin, T.R.: A study of modal characteristics and the control mechanism of finite periodic and irregular ribbed plates. J. Acoust. Soc. Am. 123, 729–737 (2008)

    ADS  Google Scholar 

  12. Brillouin, L.: Wave Propagation in Periodic Structures. Dover Publications, New York (1953)

    MATH  Google Scholar 

  13. Umezawa, H.: Advanced Field Theory: Micro, Macro, and Thermal Physics. American Institute of Physics, New York (1995)

    Google Scholar 

  14. Duhamel, D., Mace, B.R., Brennan, M.J.: Finite element analysis of the vibrations of waveguides and periodic structures. J. Sound Vib. 294, 205–220 (2006)

    ADS  Google Scholar 

  15. Maess, M., Wagner, N., Gaul, L.: Dispersion curves of fluid filled elastic pipes by standard FE models and eigenpath analysis. J. Sound Vib. 296, 264–276 (2006)

    ADS  MATH  Google Scholar 

  16. Mencik, J.M., Ichchou, M.: Wave finite elements in guided elastodynamics with internal fluid. Int. J. Solids Struct. 44, 2148–2167 (2007)

    MATH  Google Scholar 

  17. Manconi, E., Mace, B.R.: Wave characterization of cylindrical and curved panels using a finite element method. J. Acoust. Soc. Am. 125, 154–163 (2009)

    ADS  Google Scholar 

  18. Waki, Y., Mace, B.R., Brennan, M.J.: Numerical issues concerning the wave and finite element method for free and forced vibrations of waveguides. J. Sound Vib. 327, 92–108 (2009)

    ADS  Google Scholar 

  19. Søe-Knudsen, A., Sorokin, S.V.: Analysis of linear elastic wave propagation in piping systems by a combination of the boundary integral equations method and the finite element method. Contin. Mech. Thermodyn. 22, 647–662 (2010)

    ADS  MathSciNet  MATH  Google Scholar 

  20. Renno, J.M., Mace, B.R.: Calculation of reflection and transmission coefficients of joints using a hybrid finite element/wave and finite element approach. J. Sound Vib. 332, 2149–2164 (2013)

    ADS  Google Scholar 

  21. Renno, J.M., Mace, B.R.: Vibration modelling of structural networks using a hybrid finite element/wave and finite element approach. Wave Motion 51(4), 566–580 (2014)

    MathSciNet  MATH  Google Scholar 

  22. Mace, B.R., Jones, R.W., Harland, N.R.: Wave transmission through structure inserts. J. Acoust. Soc. Am. 109, 1417–1421 (2001)

    ADS  Google Scholar 

  23. Sigmund, O., Jensen, J.S.: Systematic design of phononic band-gap materials and structures by topology optimization. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 361(1806), 1001–1019 (2003)

    ADS  MathSciNet  MATH  Google Scholar 

  24. Hussein, M.I., Hulbert, G.M., Scott, R.A.: Dispersive elastodynamics of 1D banded materials and structures: design. J. Sound Vib. 307(3–5), 865–893 (2007)

    ADS  Google Scholar 

  25. Szefi, J.T.: Helicopter gearbox isolation using periodically layered fluidic isolators, Ph.D. thesis, The Pennsylvania State University, Pennsylvania, USA (2003)

    Google Scholar 

  26. Yilmaz, C., Kikuchi, N.: Analysis and design of passive band-stop filter-type vibration isolators for low-frequency applications. J. Sound Vib. 291(3–5), 1004–1028 (2006)

    ADS  Google Scholar 

  27. Asiri, S.: Tunable mechanical filter for longitudinal vibrations. Shock Vib. 14(5), 377–391 (2007)

    Google Scholar 

  28. Jung, W., Gu, Z., Baz, A.: Mechanical filtering characteristics of passive periodic engine mount. Finite Elem. Anal. Des. 46(9), 685–697 (2010)

    Google Scholar 

  29. Wang, J.F., Mak, C.M.: Adaptive-passive vibration isolation between nonrigid machines and nonrigid foundations using a dual-beam periodic structure with shape memory alloy transverse connection. J. Sound Vib. 333(23), 6005–6023 (2014)

    ADS  Google Scholar 

  30. Chimenti, D.E.: Guided waves in plates and their use in materials characterization. Appl. Mech. Rev. 50(5), 247–284 (1997)

    ADS  Google Scholar 

  31. Achenbach, J.D.: Quantitative nondestructive evaluation. Int. J. Solids Struct. 37, 13–27 (2000)

    MATH  Google Scholar 

  32. Rose, J.L.: Guided wave nuances for ultrasonic nondestructive evaluation. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 47(3), 575–583 (2000)

    MathSciNet  Google Scholar 

  33. Chang, F.K.: Introduction to health monitoring: context, problems, solutions. In: Proceedings of the 3rd European Pre-workshop on Structural Health Monitoring, Paris, France (2002)

    Google Scholar 

  34. Ni, Y.Q., Xia, Y., Liao, W.Y., Ko, J.M.: Technology innovation in developing the structural health monitoring system for Guangzhou New TV tower. Struct. Control Health Monit. 16(1), 73–98 (2009)

    Google Scholar 

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

    Google Scholar 

  36. Ou, J.P.: Research and practice of intelligent heath monitoring systems for civil infrastructures in Main China. In: Proceedings of Third China-Japan-US Symposium on Structural Health Monitoring and Control and Fourth Chinese National Conference on Structural Control, Dalian, China (2004)

    Google Scholar 

  37. Fayyad, U.M., et al.: Advance in Knowledge Discovery and Data Mining. MIT Press, Mento Park (1996)

    Google Scholar 

  38. Duan, Z., Zhang, K.: Data mining technology for structural health monitoring. Pac. Sci. Rev. 8, 27–36 (2006)

    Google Scholar 

  39. Gordan, M., Razak, H.A., Ismail, Z., Ghaedi, K.: Recent developments in damage identification of structures using data mining. Lat. Am. J. Solids Struct. 14(13), 2373–2401 (2017)

    Google Scholar 

  40. Hung, S., Huang, C.S., Wen, C.M., Hsu, Y.C.: Nonparametric identification of a building structure from experimental data using wavelet neural network. Comput. Aided Civ. Infrastruct. Eng. 18, 356–368 (2003)

    Google Scholar 

  41. Ni, Y.Q., Wang, J.F., Chan, T.H.T.: Structural damage alarming and localization of cable-supported bridges using multi-novelty indices: a feasibility study. Struct. Eng. Mech. Int. J. 54(2), 337–362 (2015)

    Google Scholar 

  42. Padil, K.H., Bakhary, N., Hao, H.: The use of a non-probabilistic artificial neural network to consider uncertainties in vibration-based-damage detection. Mech. Syst. Signal Process. 83, 194–209 (2017)

    ADS  Google Scholar 

  43. Abayomi, M.A., David, O.O.: Fuzzy control model for structural health monitoring of civil infrastructure systems. J. Control Sci. Eng. 1, 9–20 (2015)

    Google Scholar 

  44. Jiao, Y., Liu, H., Cheng, Y., Gong, Y.: Damage identification of bridge based on Chebyshev polynomial fitting and fuzzy logic without considering baseline model parameters. Shock Vib. 2015, 1–10 (2015)

    Google Scholar 

  45. Jhonatan, C.N., Magda, R., Rodolfo, V., Luis, M., Jabid, Q.: Features of cross-correlation analysis in a data-driven approach for structural damage assessment. Sensors 18(5), 1571 (2018)

    Google Scholar 

  46. Zucconi, M., Sorrentino, L., Ferlito, R.: Principal component analysis for a seismic usability model of unreinforced masonry buildings. Soil Dyn. Earthq. Eng. 96, 64–75 (2017)

    Google Scholar 

  47. Krishnan, M., Bhowmik, B., Hazra, B., Pakrashi, V.: Real time damage detection using recursive principal components and time varying auto-regressive modeling. Mech. Syst. Signal Process. 101, 549–574 (2018)

    ADS  Google Scholar 

  48. Mita, A., Haqiwara, H.: Damage diagnosis of a building structure using support vector machine and modal frequency patterns. Proc. SPIE Int. Soc. Opt. Eng. 5057, 118–125 (2003)

    ADS  Google Scholar 

  49. Chong, J.W., Kim, Y., Chon, K.H.: Nonlinear multiclass support vector machine-based health monitoring system for buildings employing magnetorheological dampers. J. Intell. Mater. Syst. Struct. 25, 1456–1468 (2013)

    Google Scholar 

  50. Huang, Y., Shao, C.S, Wu, B., Beck, J.L., Li, H.: State-of-the-art review on Bayesian inference in structural system identification and damage assessment. Adv. Struct. Eng. 2(6) (2019)

    Google Scholar 

  51. Yin, T., Jiang, Q., Yuen, K.: Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique. Eng. Struct. 132, 260–277 (2017)

    Google Scholar 

  52. Panigrahi, S.K., Chakraverty, S., Mishra, B.K.: damage identification of multistory shear structure from sparse modal information. J. Comput. Civ. Eng. 27, 1–9 (2013)

    Google Scholar 

  53. Guilherme, F.G., Sebastião, S.C., Antonio, C.A.: Damage detection in aeronautical profile by using frequency changes and optimization algorithms. J. Comput. Sci. 7(2), 29–43 (2016)

    Google Scholar 

  54. Ranginkaman, M.H., Haghighi, A., Vali Samani, H.M.: Inverse frequency response analysis for pipelines leak detection using the particle swarm optimization. Int. J. Optim. Civil Eng. 6, 1–12 (2016)

    Google Scholar 

  55. Majumdar, A., Kumar, D., Maity, D.: Damage assessment of truss structures from changes in natural frequencies using ant colony optimization. Appl. Math. Comput. 218, 9759–9977 (2012)

    MATH  Google Scholar 

  56. Arangio, S., Beck, J.L.: Bayesian neural networks for bridge integrity assessment. Struct. Control Health Monit. 19, 3–21 (2012)

    Google Scholar 

  57. Yin, T., Zhu, H.P.: Probabilistic damage detection of a steel truss bridge model by optimally designed bayesian neural network. Sensors 18(10), 3371 (2018)

    Google Scholar 

  58. Adeli, H., Jiang, X.: Dynamic fuzzy wavelet neural network model for structural system identification. J. Struct. Eng. 132(1), 102–111 (2006)

    Google Scholar 

  59. Jiang, X., Mahadevan, S., Adeli, H.: Bayesian wavelet packet denoising for structural system identification. Struct. Control Health Monit. 14, 333–356 (2007)

    Google Scholar 

  60. Zhou, H.F., Ni, Y.Q., Ko, J.M.: Eliminating temperature effect in vibration-based structural damage detection. J. Eng. Mech. 137, 785–797 (2011)

    Google Scholar 

  61. Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Burlington, MA 01803, USA (2011)

    Google Scholar 

  62. Alhajj, R., Gao, H., Li, X., Li, J., Zaiane, O.R.: Advanced data mining and applications. In: 3rd International Conference on Advanced Data Mining and Applications (ADMA), Reda, Harbin, China (2007)

    Google Scholar 

  63. Ko, J.M., Chak, K.K., Wang, J.Y., Ni, Y.Q., Chan, T.H.T.: Formulation of an uncertainty model relating modal parameters and environmental factors by using long-term monitoring data. In: Proceeding of Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures. International Society for Optical Engineering, San Diego, California, United States (2003)

    Google Scholar 

  64. Sohn, H., Worden, K., Farrar, C.R.: Consideration of environmental and operational variability for damage diagnosis. In: Proceedings of SPIE: The International Society for Optical Engineering, Smart Structures and Materials 2002: Smart Systems for Bridges, Structures, and Highways, vol. 4696, pp. 100–111. Society of Photo-Optical Instrumentation Engineers, Bellingham, WA (2002)

    Google Scholar 

  65. Oh, C.K., Sohn, H., Bae, I.H.: Statistical novelty detection within the Yeongjong suspension bridge under environmental and operational variations. Smart Mater. Struct. 18(12), 125022 (2009)

    ADS  Google Scholar 

  66. Hsu, T.Y., Loh, C.H.: Damage detection accommodating nonlinear environmental effects by nonlinear principal component analysis. Struct. Control Health Monit. 17(3), 338–354 (2009)

    Google Scholar 

  67. Reynders, E., Wursten, G., De Roeck, G.: Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification. Struct. Health Monit. 13(1), 82–93 (2014)

    Google Scholar 

  68. Park, H.W., Sohn, H., Law, K.H., Farrar, C.R.: Time reversal active sensing for health monitoring of a composite plate. J. Sound Vib. 302(1–2), 50–66 (2007)

    ADS  Google Scholar 

  69. Kim, S.B., Sohn, H.: Instantaneous reference-free crack detection based on polarization characteristics of piezoelectric materials. Smart Mater. Struct. 16(6), 2375–2387 (2007)

    ADS  Google Scholar 

  70. Sohn, H., Kim, S.B.: Development of dual PZT transducers for reference-free crack detection in thin plate structures. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57(1), 229–240 (2010)

    Google Scholar 

  71. Anton, S.R., Inman, D.J., Park, G.: Reference-free damage detection using instantaneous baseline measurements. AIAA J. 47(8), 1952–1964 (2009)

    ADS  Google Scholar 

  72. Park, S., Anton, S.R., Kim, J.K., et al.: Instantaneous baseline structural damage detection using a miniaturized piezoelectric guided waves system. KSCE J. Civ. Eng. 14(6), 889–895 (2010)

    Google Scholar 

  73. Overly, T.G., Park, G., Farinholt, K.M., Farrar, C.R.: Piezoelectric active-sensor diagnostics and validation using instantaneous baseline data. IEEE Sens. J. 9(11), 1414–1421 (2009)

    ADS  Google Scholar 

  74. Salmanpour, M.S., Khodaei, Z.S., Aliabadi, M.H.: Instantaneous baseline damage localization using sensor mapping. IEEE Sens. J. 17(2), 295–301 (2017)

    ADS  Google Scholar 

  75. Ferri Aliabadi, M.H., Sharif Khodaei, Z.: Structural Health Monitoring for Advanced Composite Structures. Computational and Experimental Methods in Structures (Book 8), World Scientific Europe Ltd, London (2018)

    Google Scholar 

  76. Ni, Y.Q., Wang, J.F., Xie, Q.L., Lam, K.C.: A fiber Bragg grating sensing network for structural integrity monitoring of underground water pipes: analysis of monitoring data. In: The 5th International Forum on Opto-electronic Sensor-based Monitoring in Geo-engineering, Nanjing, China (2014)

    Google Scholar 

  77. Xu, C.: Health condition assessment of underground water pipe monitored by fibre Bragg sensory system. The Hong Kong Polytechnic University, Dissertations, Hong Kong (2016). https://theses.lib.polyu.edu.hk/handle/200/9064

  78. Peng, Z.K., Lang, Z.Q., Chu, F.L., Meng, G.: Locating nonlinear components in periodic structures using nonlinear effects. Struct. Health Monit. 9(5), 401–411 (2010)

    Google Scholar 

  79. Cheng, C.M., Peng, Z.K., Dong, X.J., Zhang, W.M., Meng, G.: Locating non-linear components in two dimensional periodic structures based on NOFRFs. Int. J. Non-Linear Mech. 67, 198–208 (2014)

    ADS  Google Scholar 

  80. Zhao, J., Tang, J., Wang, K.W.: Anomaly amplification for damage detection of periodic structures via piezoelectric transducer networking. Smart Mater. Struct. 20(10), 105006 (2011)

    ADS  Google Scholar 

  81. Zhao, J., Tang, J.: Amplifying damage signature in periodic structures using enhanced piezoelectric networking with negative resistance elements. J. Intell. Mater. Syst. Struct. 24(13), 1613–1625 (2013)

    Google Scholar 

  82. Zhu, H., Wu, M.: The characteristic receptance method for damage detection in large mono-coupled periodic structures. J. Sound Vib. 251(2), 241–259 (2002)

    ADS  Google Scholar 

  83. Zhu, H.P., Xu, Y.L.: Damage detection of mono-coupled periodic structures based on sensitivity analysis of modal parameters. J. Sound Vib. 285(1–2), 365–390 (2005)

    ADS  Google Scholar 

  84. Yin, T., Zhu, H.P., Fu, S.J.: Damage identification of periodically-supported structures following the Bayesian probabilistic approach. Int. J. Struct. Stab. Dyn. 19(1), 1940011 (2019)

    MathSciNet  Google Scholar 

  85. Yin, T., Wang, X.Y., Zhu, H.P.: A probabilistic approach for the detection of bolt loosening in periodically supported structures endowed with bolted flange joints. Mech. Syst. Signal Process. 128, 588–616 (2019)

    ADS  Google Scholar 

  86. Yin, T., Yuen, K.V., Lam, H.F., Zhu, H.P.: Entropy-based optimal sensor placement for model identification of periodic structures endowed with bolted joints. Comput. Aided Civ. Infrastruct. Eng. 32(12), 1007–1024 (2017)

    Google Scholar 

  87. Lin, J.F., Xu, Y.L.: Two-stage covariance-based multisensing damage detection method. J. Eng. Mech. B4016003 (2017)

    Google Scholar 

  88. Lin, J.F., Xu, Y.L.: Response covariance-based sensor placement for structural damage detection. Struct. Infrastruct. Eng. 14(9), 1207–1220 (2018)

    Google Scholar 

  89. Lin, J.F., Xu, Y.L., Law, S.S.: Structural damage detection-oriented multi-type sensor placement with multi-objective optimization. J. Sound Vib. 422, 1–22 (2018)

    Google Scholar 

  90. Lin, J.F., Xu, Y.L., Zhan, S.: Experimental investigation on multi-objective multi-type sensor optimal placement for structural damage detection. Struct. Health Monit. Int. J. 18(3), 882–901 (2019)

    Google Scholar 

  91. Xu, Y.L., Lin, J.F., Zhan, S., F.Y. Wang: Multi-stage damage detection of a transmission tower: Numerical investigation and experimental validation. Struct. Control Health Monit. e2366 (2019)

    Google Scholar 

  92. Wang, P.F., Youn, B.D., Hu, C.: A generic probabilistic framework for structural health prognostics and uncertainty management. Mech. Syst. Signal Process. 28, 622–637 (2012)

    ADS  Google Scholar 

  93. Hu, C., Youn, B.D., Wang, P., Yoon, J.T.: Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliab. Eng. Syst. Saf. 103, 120–135 (2012)

    Google Scholar 

  94. Wang, J.F., Liu, X.Z., Ni, Y.Q.: A Bayesian probabilistic approach for acoustic emission-based rail condition assessment. Comput. Aided Civ. Infrastruct. Eng. 33(1), 21–34 (2018)

    Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge the financial supports from China Earthquake Administration’s Science for Earthquake Resilience Project (No. XH204702) and the Guangdong Provincial Science and Technology Plan Project (No. 2018B020207011).

Author information

Authors and Affiliations

  1. The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China

    Junfang Wang

  2. Center of Safety Monitoring of Engineering Structures, Shenzhen Academy of Disaster Prevention and Reduction, Shenzhen, China

    Jian-Fu Lin

Authors
  1. Junfang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian-Fu Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jian-Fu Lin .

Editor information

Editors and Affiliations

  1. Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore

    Yun Lai Zhou

  2. Faculty of Engineering and Architecture, Ghent University, Laboratory Soete, Ghent, Belgium

    Magd Abdel Wahab

  3. Department of Mechanical Engineering, Technical University Of Lisbon, Lisbon, Portugal

    Nuno M. M. Maia

  4. Engineering Research Center of Railway Environment Vibration and Noise, Ministry of Education, East China Jiaotong University, Nanchang, China

    Linya Liu

  5. Faculty of Engineering, Lusófona University, Lisbon, Portugal

    Elói Figueiredo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wang, J., Lin, JF. (2019). Structural Health Monitoring of Periodic Infrastructure: A Review and Discussion. In: Zhou, Y., Wahab, M., Maia, N., Liu, L., Figueiredo, E. (eds) Data Mining in Structural Dynamic Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-15-0501-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-0501-0_2

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0500-3

  • Online ISBN: 978-981-15-0501-0

  • eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)

Publish with us

Policies and ethics

Search

Navigation