Skip to main content
SpringerLink
Log in

Convolutional Neural Network-Based Methodology for Detecting, Locating and Quantifying Corrosion Damage in a Truss-Type Bridge Through the Autocorrelation of Vibration Signals

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Corrosion degrades the performance of any civil structure. This work proposes a methodology based on the autocorrelation of vibration signals, a data treatment stage, and a processing stage based on one-dimension convolutional neural networks (1D-CNNs) to detect, locate, and quantify the corrosion damage. The autocorrelation method generally highlights or broadens relevant features into the vibration signals. The treatment stage splits, shuffles, and normalizes the autocorrelated data, then 1D-CNNs analyze data to compute a set of damage indicators. These indices represent the probability of damage in the structure for a particular location and a specific severity level. A truss-type bridge model is studied to test the proposed method, a truss-type bridge model located at the Autonomous University of Queretaro, Mexico, is studied. There are three levels of damage, i.e., incipient, moderate, severe, and healthy conditions. Obtained results demonstrate that the proposed method is a valuable tool since 100% of effectiveness is obtained.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Gonzalez, A.; Schorr, M.; Valdez, B.; Mungaray, A.: Bridges structures and materials, ancient and modern. In: Sepasgozar, S.M.E. (Ed.) Management of critical infrastructure. Infrastructure management and construction, pp. 91–117. IntechOpen, London (2020)

    Google Scholar 

  2. Wang, Q.; Nakamura, S.; Okumatsu, T.; Nishikawa, T.: Comprehensive investigation on the cause of a critical crack found in a diagonal member of a steel truss bridge. Eng. Struct. 132, 659–670 (2017)

    Article  Google Scholar 

  3. Guo, S.; Si, R.; Dai, Q.; You, Z.; Ma, Y.; Wang, J.: A critical review of corrosion development and rust removal techniques on the structural/environmental performance of corroded steel bridges. J. Clean. Prod. 233, 126–146 (2019)

    Article  Google Scholar 

  4. Falcone, R.; Lima, C.; Martinelli, E.: Soft computing techniques in structural and earthquake engineering: a literature review. Eng. Struct. 207, 110269 (2020)

    Article  Google Scholar 

  5. Beskhyroun, S.; Navabian, N.; Wotherspoon, L.; Ma, Q.: Dynamic behaviour of a 13-story reinforced concrete building under ambient vibration, forced vibration, and earthquake excitation. J. Build. Eng. 28, 101066 (2020)

    Article  Google Scholar 

  6. Zhou, Y.; Sun, L.: Effects of high winds on a long-span sea-crossing bridge based on structural health monitoring. J. Wind Eng. Ind. Aerodyn. 174, 260–268 (2018)

    Article  Google Scholar 

  7. Price, S.J.; Figueira, R.B.: Corrosion protection systems and fatigue corrosion in offshore wind structures: current status and future perspectives. Coatings 7, 1–25 (2017)

    Article  Google Scholar 

  8. Silva, M.; Santos, A.; Figueiredo, E.: Damage detection for structural health monitoring of bridges as a knowledge discovery in databases Process. In: Zhou, Y.L.; Wahab, M.A.; Maia, N.M.M., et al. (Eds.) Data Mining in Structural Dynamic Analysis: A Signal Processing Perspective, pp. 1–24. Springer, Singapore (2019)

    Google Scholar 

  9. Alkayem, N.F.; Cao, M.; Zhang, Y.; Bayat, M.; Su, Z.: Structural damage detection using finite element model updating with evolutionary algorithms: a survey. Neural Comput. Appl. 30, 389–411 (2018)

    Article  Google Scholar 

  10. Rafiei, M.H.; Adeli, H.: A novel unsupervised deep learning model for global and local health condition assessment of structures. Eng. Struct. 156, 598–607 (2018)

    Article  Google Scholar 

  11. Alokita, S.; Rahul, V.; Jayakrishna, K.; Rajesh, M.; Thirumalini, S.; Manikandan, M.: Recent advances and trends in structural health monitoring. In: Jawaid, M.; Thariq, M.; Saba, N. (Eds.) Structural Health Monitoring of Biocomposites, Fibre-Reinforced Composites and Hybrid Composites, pp. 53–73. Woodhead Publishing, Sawston (2019)

    Chapter  Google Scholar 

  12. Heitner, B.; Obrien, E.J.; Yalamas, T.; Schoefs, F.; Leahy, C.; Décatoire, R.: Updating probabilities of bridge reinforcement corrosion using health monitoring data. Eng. Struct. 190, 41–51 (2019)

    Article  Google Scholar 

  13. Erazo, K.; Sen, D.; Nagarajaiah, S.; Sun, L.: Vibration-based structural health monitoring under changing environmental conditions using Kalman filtering. Mech. Syst. Signal Process. 117, 1–15 (2019)

    Article  Google Scholar 

  14. Jayasundara, N.; Thambiratnam, D.; Chan, T.; Nguyen, A.: Vibration-based dual-criteria approach for damage detection in arch bridges. Struct. Health Monit. 18, 2004–2019 (2019)

    Article  Google Scholar 

  15. Boscato, G.; Fragonara, L.Z.; Cecchi, A.; Reccia, E.; Baraldi, D.: Structural health monitoring through vibration-based approaches. Shock Vib. 2019, 1–5 (2019). https://doi.org/10.1155/2019/2380616

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  17. Goyal, D.; Pabla, B.S.: The vibration monitoring methods and signal processing techniques for structural health monitoring: a review. Arch. Comput. Methods Eng. 23, 585–594 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  18. Hossain, M.S.; Ong, Z.C.; Ismail, Z.; Noroozi, S.; Khoo, S.Y.: Artificial neural networks for vibration based inverse parametric identifications: a review. Appl. Soft Comput. 52, 203–219 (2017)

    Article  Google Scholar 

  19. Agis, D.; Pozo, F.: Vibration-based structural health monitoring using piezoelectric transducers and parametric t-SNE. Sensors 20, 1716 (2020)

    Article  Google Scholar 

  20. Ibrahim, A.; Eltawil, A.; Na, Y.; El-Tawil, S.: A machine learning approach for structural health monitoring using noisy data sets. IEEE Trans. Autom. Sci. Eng. 17, 900–908 (2020)

    Article  Google Scholar 

  21. Avci, O.; Abdeljaber, O.; Kiranyaz, S.; Hussein, M.; Inman, D.: Wireless and real-time structural damage detection: a novel decentralized method for wireless sensor networks. J. Sound Vib. 424, 158–172 (2018)

    Article  Google Scholar 

  22. Amezquita-Sanchez, J.P.; Adeli, H.: Nonlinear measurements for feature extraction in structural health monitoring. Sci. Iran. 26, 3051–3059 (2019)

    Google Scholar 

  23. Vafaei, M.; Alih, S.C.: Adequacy of first mode shape differences for damage identification of cantilever structures using neural networks. Neural Comput. Appl. 30, 2509–2518 (2018)

    Article  Google Scholar 

  24. Babajanian, H.; Ghodrati, G.; Nekooei, M.; Darvishan, E.: Damage detection of a cable-stayed bridge using feature extraction and selection methods. Struct. Infrastruct. Eng. 15, 1165–1177 (2019)

    Article  MATH  Google Scholar 

  25. Datteo, A.; Lucà, F.; Busca, G.: Statistical pattern recognition approach for long-time monitoring of the G. Meazza stadium by means of AR models and PCA. Eng. Struct. 153, 317–333 (2017)

    Article  Google Scholar 

  26. Datteo, A.; Busca, G.; Quattromani, G.; Cigada, A.: On the use of AR models for SHM: a global sensitivity and uncertainty analysis framework. Reliab. Eng. Syst. Saf. 170, 99–115 (2018)

    Article  Google Scholar 

  27. Gharehbaghi, V.R.; Nguyen, A.; Farsangi, E.N.; Yang, T.Y.: Supervised damage and deterioration detection in building structures using an enhanced autoregressive time-series approach. J. Build. Eng. 30, 101292 (2020)

    Article  Google Scholar 

  28. Shi, B.; Qiao, P.: A new surface fractal dimension for displacement mode shape-based damage identification of plate-type structures. Mech. Syst. Signal Process. 103, 139–161 (2018)

    Article  Google Scholar 

  29. Tao, K.; Zheng, W.; Jiang, D.: Entropy method for structural health monitoring based on statistical cause and effect analysis of acoustic emission and vibration signals. IEEE Access 7, 172515–172525 (2019)

    Article  Google Scholar 

  30. Wang, F.; Chen, Z.; Song, G.: (2020) Monitoring of multi-bolt connection looseness using entropy-based active sensing and genetic algorithm-based least square support vector machine. Mech. Syst. Signal Process. 136, 106507 (2020)

    Article  Google Scholar 

  31. Amezquita-Sanchez, J.P.; Park, H.S.; Adeli, H.: A novel methodology for modal parameters identification of large smart structures using MUSIC, empirical wavelet transform, and Hilbert transform. Eng. Struct. 147, 148–159 (2017)

    Article  Google Scholar 

  32. Zajam, S.; Joshi, T.; Bhattacharya, B.: Application of wavelet analysis and machine learning on vibration data from gas pipelines for structural health monitoring. Proc. Struct. Integ. 14, 712–719 (2019)

    Google Scholar 

  33. Azami, M.; Salehi, M.: Response-based multiple structural damage localization through multi-channel empirical mode decomposition. J. Struct. Integr. Maint. 4, 195–206 (2019)

    Google Scholar 

  34. 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)

    Article  Google Scholar 

  35. Tibaduiza, D.; Torres-Arredondo, M.Á.; Vitola, J.; Anaya, M.; Pozo, F.: A damage classification approach for structural health monitoring using machine learning. Complexity 2018, 1–14 (2018)

    Article  Google Scholar 

  36. Yanez-Borjas, J.J.; Machorro-Lopez, J.M.; Camarena-Martinez, D.; Amezquita-Sanchez, J.P.; Carrion-Viramontes, F.J.; Quintana-Rodriguez, J.A.: A new damage index based on statistical features, PCA, and Mahalanobis distance for detecting and locating cables loss in a cable-stayed bridge. Int. J. Struct. Stab. Dyn. 21, 2150127 (2021)

    Article  MathSciNet  Google Scholar 

  37. Lin, T.-K.; Chen, Y.-C.: (2020) Integration of refined composite multiscale cross-sample entropy and backpropagation neural networks for structural health monitoring. Appl. Sci. 10, 839 (2020)

    Article  Google Scholar 

  38. Neves, A.C.; González, I.; Leander, J.; Karoumi, R.: Structural health monitoring of bridges: a model-free ANN-based approach to damage detection. J. Civil Struct. Health Monit. 7, 689–702 (2017)

    Article  Google Scholar 

  39. Padil, K.H.; Bakhary, N.; Abdulkareem, M.; Li, J.; Hao, H.: Non-probabilistic method to consider uncertainties in frequency response function for vibration-based damage detection using artificial neural network. J. Sound Vib. 467, 115069 (2020)

    Article  Google Scholar 

  40. Abu-Mahfouz, I.; Banerjee, A.: Crack detection and identification using vibration signals and fuzzy clustering. Procedia Comput. Sci. 114, 266–274 (2017)

    Article  Google Scholar 

  41. Rabcan, J.; Levashenko, V.; Zaitseva, E.; Kvassay, M.; Subbotin, S.: Non-destructive diagnostic of aircraft engine blades by fuzzy decision tree. Eng. Struct. 197, 109396 (2019)

    Article  Google Scholar 

  42. Ruocci, G.; Cumunel, G.; Le, T.; Argoul, P.; Point, N.; Dieng, L.: Damage assessment of pre-stressed structures: a SVD-based approach to deal with time-varying loading. Mech. Syst. Signal Process. 47, 50–65 (2014)

    Article  Google Scholar 

  43. Ettouney, M.M.; Alampalli, S.: Infrastructure Health in Civil Engineering: Theory and Components. CRC Press, London (2016)

    Book  Google Scholar 

  44. Qarib, H.; Adeli, H.: Recent advances in health monitoring of civil structures. Sci. Iran. 21, 1733–1742 (2014)

    Google Scholar 

  45. Valtierra-Rodriguez, M.; Rivera-Guillen, J.R.; Basurto-Hurtado, J.A.; De-Santiago-Perez, J.J.; Granados-Lieberman, D.; Amezquita-Sanchez, J.P.: Convolutional neural network and motor current signature analysis during the transient state for detection of broken rotor bars in induction motors. Sensors 20, 3721 (2020). https://doi.org/10.3390/s20133721

    Article  Google Scholar 

  46. Kiranyaz, S.; Ince, T.; Abdeljaber, O.; Avci, O.; Gabbouj, M.: 1-D convolutional neural networks for signal processing applications. In 2019 IEEE international conference on acoustics, speech and signal processing (ICASSP) pp. 8360–8364 (2019)

  47. Kiranyaz, S.; Ince, T.; Gabbouj, M.: Real-time patient-specific ECG classification by 1-D convolutional neural networks. IEEE Trans. Biomed. Eng. 63, 664–675 (2016)

    Article  Google Scholar 

  48. He, W.; Wang, G.; Hu, J.; Li, C.; Guo, B.; Li, F.: Simultaneous human health monitoring and time-frequency sparse representation using EEG and ECG signals. IEEE Access 7, 85985–85994 (2019)

    Article  Google Scholar 

  49. Acharya, U.R.; Oh, S.L.; Hagiwara, Y.; Tan, J.H.; Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018)

    Article  Google Scholar 

  50. Morabito, F.C.; Ieracitano, C.; Mammone, N.: An explainable artificial intelligence approach to study MCI to AD conversion via HD-EEG processing. Clin. EEG Neurosci. (2021). https://doi.org/10.1177/15500594211063662

    Article  Google Scholar 

  51. Ince, T.; Kiranyaz, S.; Eren, L.; Askar, M.; Gabbouj, M.: Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Ind. Electron. 63, 7067–7075 (2016)

    Article  Google Scholar 

  52. Jing, L.; Zhao, M.; Li, P.; Xu, X.: A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Meas. 111, 1–10 (2017)

    Article  Google Scholar 

  53. Han, T.; Liu, C.; Yang, W.; Jiang, D.: Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions. ISA Trans. 93, 341–353 (2019)

    Article  Google Scholar 

  54. Yao, Y.; Zhang, S.; Yang, S.; Gui, G.: Learning attention representation with a multi-scale CNN for gear fault diagnosis under different working conditions. Sensors 20, 1233 (2020)

    Article  Google Scholar 

  55. Xin, Y.; Li, S.; Wang, J.; An, Z.; Zhang, W.: Intelligent fault diagnosis method for rotating machinery based on vibration signal analysis and hybrid multi-object deep CNN. IET Sci. Meas. Technol. 14, 407–415 (2020)

    Article  Google Scholar 

  56. Liang, Y.; Li, B.; Jiao, B.: A deep learning method for motor fault diagnosis based on a capsule network with gate-structure dilated convolutions. Neural Comput. Appl. 33, 1401–1418 (2021)

    Article  Google Scholar 

  57. Reddy, A.; Indragandhi, V.; Ravi, L.; Subramaniyaswamy, V.: Detection of cracks and damage in wind turbine blades using artificial intelligence-based image analytics. Meas. 147, 106823 (2019)

    Article  Google Scholar 

  58. Khodabandehlou, H.; Pekcan, G.; Fadali, M.S.: Vibration-based structural condition assessment using convolution neural networks. Struct. Control Health Monit. 26, 2308 (2019)

    Google Scholar 

  59. Sarawgi, Y.; Somani, S.; Chhabra, A.: Nonparametric vibration based damage detection technique for structural health monitoring using 1D CNN. In: Nain, N.; Vipparthi, S.K.; Raman, B. (Eds.) Computer Vision and Image Processing, pp. 146–157. Springer, Singapore (2020)

    Chapter  Google Scholar 

  60. Dorafshan, S.; Azari, H.: Evaluation of bridge decks with overlays using impact echo, a deep learning approach. Autom. Constr. 113, 103133 (2020)

    Article  Google Scholar 

  61. Gao, Y.; Mosalam, K.M.: Deep transfer learning for image-based structural damage recognition. Comput. Aid. Civ. Infrastruct. 33, 748–768 (2018)

    Article  Google Scholar 

  62. Ren, Y.; Huang, J.; Hong, Z.; Lu, W.; Yin, J.; Zou, L.; Shen, X.: Image-based concrete crack detection in tunnels using deep fully convolutional networks. Constr. Build. Mater. 234, 117367 (2020)

    Article  Google Scholar 

  63. Kim, B.; Yuvaraj, N.; Sri Preethaa, K.R.; Arun Pandian, R.: Surface crack detection using deep learning with shallow CNN architecture for enhanced computation. Neural Comput. Applic. 33, 9289–9305 (2021)

    Article  Google Scholar 

  64. Abdeljaber, O.; Avci, O.; Kiranyaz, M.S.; Boashash, B.; Sodano, H.; Inman, D.J.: 1-D CNNs for structural damage detection: verification on a structural health monitoring benchmark data. Neurocomputing 275, 1308–1317 (2018)

    Article  Google Scholar 

  65. Johnson, E.A.; Lam, H.F.; Katafygiotis, L.S.; Beck, J.L.: Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data. J. Eng. Mech. 130, 3–15 (2004)

    Google Scholar 

  66. Lakshmi, K.; Rao, A.R.M.: Baseline-free hybrid diagnostic technique for detection of minor incipient damage in the structure. J. Perform. Constr. Facil. 33, 04019018 (2019)

    Article  Google Scholar 

  67. Rangel-Magdaleno, J.; Peregrina-Barreto, H.; Ramirez-Cortes, J.; Morales-Caporal, R.; Cruz-Vega, I.: Vibration analysis of partially damaged rotor bar in induction motor under different load condition using DWT. Shock Vib. 2016, 3530464 (2016)

    Google Scholar 

  68. Box, G.E.P.; Jenkins, G.M.; Reinsel, G.C.; Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, New Jersey (2015)

    MATH  Google Scholar 

  69. Zubaydi, A.; Haddara, M.R.; Swamidas, A.S.J.: On the use of the autocorrelation function to identify the damage in the side shell of a ship’s hull. Mar. Struct. 13, 537–551 (2000)

    Article  Google Scholar 

  70. Yanez-Borjas, J.J.; Amezquita-Sanchez, J.P.; Valtierra-Rodriguez, M.; Camarena-Martinez, D.: Nonlinear mode decomposition-based methodology for modal parameters identification of civil structures using ambient vibrations. Meas. Sci. Technol. 31, 015007 (2019)

    Article  Google Scholar 

  71. Bisgaard, S.; Kulahci, M.: Time Series Analysis and Forecasting by Example. Wiley, New Jersey (2011)

    Book  MATH  Google Scholar 

  72. Eren, L.; Ince, T.; Kiranyaz, S.: A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. J. Sign. Process Syst. 91, 179–189 (2019)

    Article  Google Scholar 

  73. Wu, C.; Jiang, P.; Ding, C.; Feng, F.; Chen, T.: Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Comput. Ind. 108, 53–61 (2019)

    Article  Google Scholar 

  74. Avci, O.; Abdeljaber, O.; Kiranyaz, S.; Inman, D.: Convolutional neural networks for real-time and wireless damage detection. In: Pakzad, S. (Ed.) Dynamics of Civil Structures, Vol. 2, pp. 129–136. Springer, Cham (2020)

    Chapter  Google Scholar 

  75. Abdeljaber, O.; Avci, O.; Kiranyaz, S.; Gabbouj, M.; Inman, D.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017)

    Article  Google Scholar 

  76. Abdeljaber, O.; Sassi, S.; Avci, O.; Kiranyaz, S.; Ibrahim, A.A.; Gabbouj, M.: Fault detection and severity identification of ball bearings by online condition monitoring. IEEE Trans. Ind. Electron. 66, 8136–8147 (2019)

    Article  Google Scholar 

  77. Cao, S.; Ouyang, H.: Robust structural damage detection and localization based on joint approximate diagonalization technique in frequency domain. Smart Mater. Struct. 26, 015005 (2016)

    Article  Google Scholar 

  78. Rafiei, M.H.; Adeli, H.: (2017) A novel machine learning-based algorithm to detect damage in high-rise building structures. Struct. Des. Tall Spec. 26, e1400 (2017)

    Article  Google Scholar 

  79. Affonso, L.O.A.: Corrosion. In: Affonso, L.O.A. (Ed.) Machinery Failure Analysis Handbook, pp. 83–99. Gulf Publishing Company, Texas (2006)

    Chapter  Google Scholar 

  80. Schofield, M.J.: Corrosion. In: Snow, D.A. (Ed.) Plant Engineer’s Reference Book (Second Edition), pp. 33–41. Butterworth-Heinemann, Oxford (2002)

    Google Scholar 

  81. Kruger, J.; Begum, S.: Corrosion of Metals: Overview. In: Reference Module in Materials Science and Materials Engineering. Elsevier (2006)

  82. Moreno-Gomez, A.; Amezquita-Sanchez, J.; Valtierra-Rodriguez, M.; Perez-Ramirez, C.A.; Dominguez-Gonzalez, A.; Chavez-Alegria, O.: EMD-Shannon entropy-based methodology to detect incipient damages in a truss structure. Appl. Sci. 8, 2068 (2018)

    Article  Google Scholar 

  83. Li, W.; Liu, T.; Gao, S.; Luo, M.; Wang, J.; Wu, J.: An electromechanical impedance-instrumented corrosion-measuring probe. J. Intell. Mater. Syst. Struct. 30, 2135–2146 (2019)

    Article  Google Scholar 

  84. Pang, B.; Qian, J.; Zhang, Y.; Jia, Y.; Ni, H.; Pang, S.D.; Liu, Z.: Multifunctional intelligent coating with superdurable, superhydrophobic, self-monitoring, self-heating, and self-healing properties for existing construction application. ACS Appl. Mater. Interfaces 11, 29242–29254 (2019)

    Article  Google Scholar 

  85. Park, S.; Park, S.-K.: Quantitative corrosion monitoring using wireless electromechanical impedance measurements. Res. Nondestruct. Eval. 21, 184–192 (2010)

    Article  Google Scholar 

  86. Han, J.; Kamber, M.; Pei, J.: Data Mining, p. 393–442. Elsevier, London (2012)

    Book  MATH  Google Scholar 

  87. Benfenati, E.; Chrétien, J.R.; Gini, G.: Validation of the models. In: Benfenati, E. (Ed.) Quantitative Structure-Activity Relationships (QSAR) for Pesticide Regulatory Purposes, pp. 185–199. Elsevier, Amsterdam (2007)

    Chapter  Google Scholar 

  88. Chen, Z.; Pan, C.; Yu, L.: Structural damage detection via adaptive dictionary learning and sparse representation of measured acceleration responses. Meas. 128, 377–387 (2018)

    Article  Google Scholar 

  89. Yang, H.; Zhang, J.; Chen, L.; Zhang, H.L.; Liu, S.L.: Fault diagnosis of reciprocating compressor based on convolutional neural networks with multisource raw vibration signals. Math. Probl. Eng. 2019, 6921975 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Council of Science and Technology (CONACyT) by the scholarship 481368, the “FI-Problemas Nacionales 2021-202112” project, and the project 34/2018 of the program “Investigadoras e Investigadores por México” del CONACYT (Cátedras CONACYT).

Author information

Authors and Affiliations

  1. CA Procesamiento Digital de Señales, Universidad de Guanajuato. División de Ingenierías Campus Irapuato-Salamanca (DICIS), 36885, Salamanca, Guanajuato, México

    Jesus J. Yanez-Borjas & David Camarena-Martinez

  2. ENAP-RG, CA Sistemas Dinámicos, Facultad de Ingeniería, Departamento de Electromecánica, Universidad Autónoma de Querétaro, Campus San Juan del Río, 76807, San Juan del Río, Querétaro, México

    Martin Valtierra-Rodriguez & Juan P. Amezquita-Sanchez

  3. Investigador CONACYT - Instituto Mexicano del Transporte, km 12 Carretera Estatal No. 431 “El Colorado-Galindo” San Fandila, 76703, Pedro Escobedo, Querétaro, México

    Jose M. Machorro-Lopez

Authors
  1. Jesus J. Yanez-Borjas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Valtierra-Rodriguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose M. Machorro-Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Camarena-Martinez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juan P. Amezquita-Sanchez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan P. Amezquita-Sanchez.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yanez-Borjas, J.J., Valtierra-Rodriguez, M., Machorro-Lopez, J.M. et al. Convolutional Neural Network-Based Methodology for Detecting, Locating and Quantifying Corrosion Damage in a Truss-Type Bridge Through the Autocorrelation of Vibration Signals. Arab J Sci Eng 48, 1119–1141 (2023). https://doi.org/10.1007/s13369-022-06731-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-022-06731-7

Keywords

Search

Navigation