Smart Structures and Systems

  • 제14권2호
  • /
  • Pages.159-189
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  • 2014
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Modal parameters based structural damage detection using artificial neural networks - a review

  • Hakim, S.J.S. (StrucHMRS Group, Department of Civil Engineering, University of Malaya) ;
  • Razak, H. Abdul (StrucHMRS Group, Department of Civil Engineering, University of Malaya)
  • 투고 : 2012.08.11
  • 심사 : 2013.08.16
  • 발행 : 2014.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.

키워드

참고문헌

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  14. Detecting width-wise partial delamination in the composite beam using generalized fractal dimension vol.19, pp.1, 2017, https://doi.org/10.12989/sss.2017.19.1.091
  15. Parallel damage detection through finite frequency changes on multicore processors vol.63, pp.4, 2014, https://doi.org/10.12989/sem.2017.63.4.457
  16. Prediction of the remaining service life of existing concrete bridges in infrastructural networks based on carbonation and chloride ingress vol.21, pp.3, 2014, https://doi.org/10.12989/sss.2018.21.3.305
  17. Application of Artificial Neural Networks to the prediction of out-of-plane response of infill walls subjected to shake table vol.21, pp.4, 2014, https://doi.org/10.12989/sss.2018.21.4.521
  18. Application of compressive sensing and variance considered machine to condition monitoring vol.22, pp.2, 2014, https://doi.org/10.12989/sss.2018.22.2.231
  19. Optimization-based method for structural damage detection with consideration of uncertainties- a comparative study vol.22, pp.5, 2014, https://doi.org/10.12989/sss.2018.22.5.561
  20. Hierarchical neural network for damage detection using modal parameters vol.70, pp.4, 2014, https://doi.org/10.12989/sem.2019.70.4.457
  21. Position and mass identification in nanotube mass sensor using neural networks vol.233, pp.15, 2014, https://doi.org/10.1177/0954406219841075
  22. Coverage intensity of optimal sensors for common, isolated, and integrated steel structures using novel approach of FEM-MAC-TTFD vol.15, pp.8, 2014, https://doi.org/10.1177/1550147719857568
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  24. A review on deep learning-based structural health monitoring of civil infrastructures vol.24, pp.5, 2014, https://doi.org/10.12989/sss.2019.24.5.567
  25. Cracked rotor diagnosis by means of frequency spectrum and artificial neural networks vol.25, pp.4, 2014, https://doi.org/10.12989/sss.2020.25.4.459
  26. Study of damage identification for bridges based on deep belief network vol.23, pp.8, 2020, https://doi.org/10.1177/1369433219898058
  27. Damage detection in truss bridges using transmissibility and machine learning algorithm: Application to Nam O bridge vol.26, pp.1, 2014, https://doi.org/10.12989/sss.2020.26.1.035
  28. Structural health evaluation of the prestressed concrete using advanced acoustic emission (AE) parameters vol.250, pp.None, 2020, https://doi.org/10.1016/j.conbuildmat.2020.118860
  29. Accelerated Monte Carlo analysis of flow-based system reliability through artificial neural network-based surrogate models vol.26, pp.2, 2014, https://doi.org/10.12989/sss.2020.26.2.175
  30. Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model vol.10, pp.18, 2014, https://doi.org/10.3390/app10186476
  31. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications vol.147, pp.None, 2021, https://doi.org/10.1016/j.ymssp.2020.107077
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  33. Using artificial neural network and non‐destructive test for crack detection in concrete surrounding the embedded steel reinforcement vol.22, pp.5, 2014, https://doi.org/10.1002/suco.202000767
  34. Structural Assessment under Uncertain Parameters via the Interval Optimization Method Using the Slime Mold Algorithm vol.12, pp.4, 2014, https://doi.org/10.3390/app12041876