Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,278

The DOI matches its Crossref record.

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

Abdeljaber, Osama, Avci, Onur, Kiranyaz, Serkan, Gabbouj, Moncef, Inman, Daniel J.·2017·Journal of Sound and Vibration

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jsv.2016.10.043

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 33

  1. Anton, 2009, Reference-free damage detection using instantaneous baseline measurements, AIAA J., 47, 1952, 10.2514/1.43252 · doi:10.2514/1.43252
  2. Mansouri, 2015, Iterated square root unscented Kalman filter for nonlinear states and parameters estimation: three DOF damped system, J. Civil. Struct. Health Monit., 5, 493, 10.1007/s13349-015-0134-7 · DOI
  3. M.Mansouri, O.Avci, H.Nounou, M.Nounou, A comparative assessment of nonlinear state estimation methods for structural health monitoring, in: Proceedings of IMAC XXXIII Conference and Exposition on Structural Dynamics, Orlando, FL, USA, 2015. · DOI
  4. Hakim, 2015, Fault diagnosis on beam-like structures from modal parameters using artificial neural networks, Measurement, 76, 45, 10.1016/j.measurement.2015.08.021 · DOI
  5. Ng, 2014, Application of Bayesian-designed artificial neural networks in Phase II structural health monitoring benchmark studies, Aust. J. Struct. Eng., 15, 27, 10.7158/S12-042.2014.15.1 · DOI
  6. Goh, 2013, Prediction of unmeasured mode shape using artificial neural network for damage detection, J. Teknol. (Sci. Eng.), 61, 57
  7. Mehrjoo, 2008, Damage detection of truss bridge joints using artificial neural networks, Expert Syst. Appl., 35, 1122, 10.1016/j.eswa.2007.08.008 · DOI · Paper page
  8. Betti, 2014, Damage detection on a three-storey steel frame using artificial neural networks and genetic algorithms, Meccanica, 50, 875, 10.1007/s11012-014-0085-9 · DOI
  9. Pawar, 2006, Damage detection in beams using spatial fourier analysis and neural networks, J. Intell. Mater. Syst. Struct., 18, 347, 10.1177/1045389X06066292 · DOI
  10. Lee, 2005, Neural networks-based damage detection for bridges considering errors in baseline finite element models, J. Sound Vib., 280, 555, 10.1016/j.jsv.2004.01.003 · DOI · Paper page
  11. Meruane, 2015, Online sequential extreme learning machine for vibration-based damage assessment using transmissibility data, J. Comput. Civil. Eng., 30, 4015042, 10.1061/(ASCE)CP.1943-5487.0000517 · DOI
  12. Zhou, 2014, Damage localization of cable-supported bridges using modal frequency data and probabilistic neural network, Math. Probl. Eng., 2014, 10.1155/2014/837963 · DOI
  13. Jiang, 2006, Structural damage detection by integrating data fusion and probabilistic neural network, Adv. Struct. Eng., 9, 445, 10.1260/136943306778812787 · DOI
  14. Wen, 2007, Unsupervised fuzzy neural networks for damage detection of structures, Struct. Control Health Monit., 14, 144, 10.1002/stc.116 · DOI
  15. Chun, 2015, Bridge damage severity quantification using multipoint acceleration measurement and artificial neural networks, Shock Vib., 2015
  16. Dackermann, 2010, Dynamic-based damage identification using neural network ensembles and damage index method, Adv. Struct. Eng., 13, 1001, 10.1260/1369-4332.13.6.1001 · DOI · Paper page
  17. Dackermann, 2014, Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks, Struct. Health Monit., 13, 430, 10.1177/1475921714542890 · DOI
  18. Bandara, 2013, The three-stage artificial neural network method for damage assessment of building structures, Austr. J. Struct. Eng., 14
  19. Liu, 2011, Structure damage diagnosis using neural network and feature fusion, Eng. Appl. Artif. Intell., 24, 87, 10.1016/j.engappai.2010.08.011 · DOI
  20. Figueiredo, 2011, Machine learning algorithms for damage detection under operational and environmental variability, Struct. Health Monit., 10, 559, 10.1177/1475921710388971 · DOI · Paper page
  21. Santos, 2016, Machine learning algorithms for damage detection: kernel-based approaches, J. Sound Vib., 363, 584, 10.1016/j.jsv.2015.11.008 · DOI · Paper page
  22. Avci, 2015, Self-organizing maps for structural damage detection: a novel unsupervised vibration-based algorithm, J. Perform. Constr. Facil.
  23. Abdeljaber, 2016, Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps, J. Arch. Eng., 10.1061/(ASCE)AE.1943-5568.0000205 · DOI
  24. O.Abdeljaber, O.Avci, N.T.Do, M.Gul, O.Celik, F.N.Catbas, Quantification of structural damage with self-organizing maps BT – structural health monitoring, damage detection & mechatronics, in: A. Wicks, C. Niezrecki (Eds.), Proceedings of the 34th IMAC, a Conference and Exposition on Structural Dynamics 2016, vol. 7, Springer International Publishing, Cham, 2016, pp. 47–57. doi:10.1007/978-3-319-29956-3_5. · DOI
  25. Yan, 2011, Substructure vibration narx neural network approach for statistical damage inference, J. Eng. Mech., 139, 737, 10.1061/(ASCE)EM.1943-7889.0000363 · DOI
  26. Gul, 2010, Damage assessment with ambient vibration data using a novel time series analysis methodology, J. Struct. Eng., 137, 1518, 10.1061/(ASCE)ST.1943-541X.0000366 · DOI
  27. Cire/csan, 2010, Deep, big, simple neural nets for handwritten digit recognition, Neural Comput., 22, 3207, 10.1162/NECO_a_00052 · DOI · Paper page
  28. D.Scherer, A.Müller, S.Behnke, Evaluation of pooling operations in convolutional architectures for object recognition, in: Proceedings of the 20th International Conference on Artificial Neural Networks: Part III, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 92–101. 〈http://dl.acm.org/citation.cfm?Id=1886436.1886447〉. · DOI · Paper page
  29. S.Kiranyaz, M.A.Waris, I.Ahmad, R.Hamila, M.Gabbouj, Face segmentation in thumbnail images by data-adaptive convolutional segmentation networks, in: Proceedings of IEEE International Conference on Image Processing (ICIP), 2016, pp. 2306–2310. doi:10.1109/ICIP.2016.7532770, 2016. · DOI
  30. Hubel, 1959, Receptive fields of single neurones in the cat’s striate cortex, J. Physiol., 148, 574, 10.1113/jphysiol.1959.sp006308 · DOI · Paper page
  31. Kiranyaz, 2016, Real-time patient-specific ECG classification by 1-D convolutional neural networks, IEEE Trans. Biomed. Eng., 63, 664, 10.1109/TBME.2015.2468589 · DOI · Paper page
  32. Ince, 2016, Real-time motor fault detection by 1-D convolutional neural networks, IEEE Trans. Ind. Electron., 63, 7067, 10.1109/TIE.2016.2582729 · DOI · Paper page
  33. Abdeljaber, 2016, Dynamic testing of a laboratory stadium structure, ASCE Geotech. Struct. Eng. Congr., 1719

Cited by

See all 1,278

Metadata

Authors
Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., & Inman, D. J.
Venue
Journal of Sound and Vibration
Publisher
Elsevier BV
Published
2017-02
Volume
388
Pages
154-170
ISSN
0022-460X (ISSN-L)
OpenAlex
W2556345765
Cited by
1,278
References
0
FWCI
68.41
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. https://app.cochat.ai/papers/10.1016/j.jsv.2016.10.043
The paper
Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., & Inman, D. J. (2017). Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration. https://doi.org/10.1016/j.jsv.2016.10.043

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.