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ACI Avances en Ciencias e Ingenierías

SECCIÓN C: INGENIERÍAS

Vol. 11 Núm. 2 (2019)

Potencial de las redes neuronales para la localización de daños estructurales

DOI
https://doi.org/10.18272/aci.v11i2.1305
Enviado
noviembre 15, 2018
Publicado
2019-05-30

Resumen

La tecnología de fabricación y el estado del arte de la ingeniería estructural han conducido a un uso cada vez mayor de estructuras esbeltas, lo que las hace más susceptibles a acciones estáticas y dinámicas que pueden provocar algún tipo de daño. En este contexto, es necesario realizar inspecciones y evaluaciones periódicas para detectar y predecir daños estructurales y establecer acciones de mantenimiento capaces de garantizar la seguridad y durabilidad estructurales con un coste mínimo. Sin embargo, estos procedimientos son tradicionalmente bastante largos y costosos, por lo que son necesarias técnicas que permitan una detección de daños más eficaz. Este trabajo evalúa el potencial de los modelos de Redes Neuronales Artificiales (RNA) en la predicción de la localización de daños en elementos estructurales, en función de sus propiedades dinámicas - se utilizan las tres primeras frecuencias naturales. Basándose en 64 ejemplos numéricos de vigas de acero dañadas (en su mayoría) y no dañadas, se propone un modelo analítico basado en RNA como un estimador de localización de daños altamente preciso y eficiente. El modelo propuesto arrojó errores máximos del 0,2% y el 0,7% en 64 puntos de datos numéricos y 3 puntos de datos experimentales, respectivamente. Debido a la alta calidad de los resultados, el siguiente paso de los autores es la aplicación de enfoques similares a estructuras completas, basadas en conjuntos de datos mucho más grandes.

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Citas

  1. Nguyen VV, Dackermann U, Li J, Makki Alamdari M, Mustapha S, Runcie P, Ye L (2015). Damage Identification of a Concrete Arch Beam Based on Frequency Response Functions and Artificial Neural Networks. Electronic Journal of Structural Engineering, 14(1), 75-84.
  2. Onur Avci, P. O., & Abdeljaber, A. O. (2016). Self-Organizing Maps for Structural Damage Detection: A Novel Unsupervised Vibration-Based Algorithm. Journal of Performance of Constructed Facilities, 30(3), 1-11.
  3. Jin C, Jang S, Sun X, Li J, Christenson R (2016). Damage detection of a highway bridge under severe temperature changes using extended Kalman filter trained neural network. Journal of Civil Structural Health Monitoring, 6(3), 545 - 560.
  4. Chengyin L, Wu X, Wu N, Liu C (2014). Structural Damage Identification Based on Rough Sets and Artificial Neural Network, The Scientific World Journal, 2014(ID 193284), 1-9, doi: 10.1155/2014/193284
  5. Meruane V, Mahu J (2014). Real-Time Structural Damage Assessment Using Artificial Neural Networks and Antiresonant Frequencies. Shock and Vibration, 2014 (ID 653279), 1-14, doi: 10.1155/2014/653279
  6. Structural Vibration Solutions A/S (SVS) (2018). ARTeMIS Modal 4.0®, Aalborg, Denmark.
  7. Bandara RP, Chan THT, Thambiratnam DP (2013). The Three Stage Artificial Neural Network Method for Damage Assessment of Building Structures. Australian Journal of Structural Engineering, 14 (1), 13-25.
  8. Ahmed MS (2016). Damage Detection in Reinforced Concrete Square Slabs Using Modal Analysis and Artificial Neural Network. PhD thesis, Nottingham Trent University, Nottingham, UK.
  9. Vakil-Baghmisheh M-T, Peimani M, Sadeghi MH, Ettefagh MM (2008). Crack detection in beam-like structures using genetic algorithms. Applied Soft Computing, 8(2), 1150-1160. doi:10.1016/j.asoc.2007.10.003.
  10. Aydin K, Kisi O (2015). Damage Diagnosis in Beam-Like Structures by Artificial Neural Networks. Journal of Civil Engineering and Management, 21(5), 591-604, doi:10.3846/13923730.2014.890663
  11. Kourehli SS (2015). Damage Assessment in Structures Using Incomplete Modal Data and Artificial Neural Network. International Journal of Structural Stability and Dynamics, 15(06), 1450087-1-17, doi:10.1142/s0219455414500874
  12. Nazarko P, Ziemianski L (2017). Application of artificial neural networks in the damage identification of structural elements. Computer Assisted Methods in Engineering and Science, 18(3), 175-189, Available at: http://cames.ippt.gov.pl/index.php/cames/article/view/113 (accessed on Nov 2nd 2018).
  13. Brasiliano A (2005). Identificação de Sistemas e Atualização de Modelos Numéricos com Vistas à Avaliação (in Portuguese). PhD thesis, Technology Faculty, University of Brasilia (UnB), Brasília, Brazil.
  14. Gerdau (2018). Perfil U Gerdau. [online] Available at https://www.gerdau.com/br/pt/produtos/perfil-u-gerdau#ad-image-0 [Accessed 15 Oct. 2018].
  15. ANSYS, Inc. (2018). ANSYS® - Academic Research Mechanical, Release 18.1, Canonsburg, PA, USA.
  16. Marcy M, Brasiliano A, da Silva G, Doz, G (2014). Locating damages in beams with artificial neural network. Int. J. of Lifecycle Performance Engineering, 1(4), 398-413.
  17. Callister WD, Rethwisch DG (2009). Materials Science and Engineering: An Introduction (8th ed). John Wiley & Sons, Versailles, USA.
  18. Authors (2018a). data_set_ANN + results [Data set]. Zenodo, http://doi.org/10.5281/zenodo.1463849
  19. Hertzmann A, Fleet D (2012). Machine Learning and Data Mining, Lecture Notes CSC 411/D11, Computer Science Department, University of Toronto, Canada.
  20. McCulloch WS, Pitts W (1943). A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, 5(4), 115-133.
  21. Hern A (2016). Google says machine learning is the future. So I tried it myself. Available at: www.theguardian.com/technology/2016/jun/28/all (Accessed: 2 November 2016).
  22. Wilamowski BM, Irwin JD (2011). The industrial electronics handbook: Intelligent Systems, CRC Press, Boca Raton.
  23. Prieto A, Prieto B, Ortigosa EM, Ros E, Pelayo F, Ortega J, Rojas I (2016). Neural networks: An overview of early research, current frameworks and new challenges, Neurocomp., 214(Nov), 242-268.
  24. Flood I (2008). Towards the next generation of artificial neural networks for civil engineering, Advanced Engineering Informatics, 228(1), 4-14.
  25. Haykin SS (2009). Neural networks and learning machines, Prentice Hall/Pearson, New York.
  26. The Mathworks, Inc (2017). MATLAB R2017a, User"™s Guide, Natick, USA.
  27. Bhaskar R, Nigam A (1990). Qualitative physics using dimensional analysis, Artificial Intelligence, 45(1-2), 111-73.
  28. Gholizadeh S, Pirmoz A, Attarnejad R (2011). Assessment of load carrying capacity of castellated steel beams by neural networks, Journal of Constructional Steel Research, 67(5), 770-779.
  29. Kasun LLC, Yang Y, Huang G-B, Zhang Z (2016). Dimension reduction with extreme learning machine, IEEE Transactions on Image Processing, 25(8), 3906-18.
  30. Lachtermacher G, Fuller JD (1995). Backpropagation in time-series forecasting, Journal of Forecasting 14(4), 381-393.
  31. Pu Y, Mesbahi E (2006). Application of artificial neural networks to evaluation of ultimate strength of steel panels, Engineering Structures, 28(8), 1190-1196.
  32. Tohidi S, Sharifi Y (2014). Inelastic lateral-torsional buckling capacity of corroded web opening steel beams using artificial neural networks, The IES Journal Part A: Civil & Structural Eng, 8(1), 24-40.
  33. Flood I, Kartam N (1994a). Neural Networks in Civil Engineering: I-Principals and Understanding, Journal of Computing in Civil Engineering, 8(2), 131-148.
  34. Mukherjee A, Deshpande JM, Anmala J (1996), Prediction of buckling load of columns using artificial neural networks, Journal of Structural Engineering, 122(11), 1385-7.
  35. Wilamowski BM (2009). Neural Network Architectures and Learning algorithms, IEEE Industrial Electronics Magazine, 3(4), 56-63.
  36. Xie T, Yu H, Wilamowski B (2011). Comparison between traditional neural networks and radial basis function networks, 2011 IEEE International Symposium on Industrial Electronics (ISIE), IEEE(eds), 27-30 June 2011, Gdansk University of Technology Gdansk, Poland, 1194-99.
  37. Aymerich F, Serra M (1998). Prediction of fatigue strength of composite laminates by means of neural networks. Key Eng. Materials, 144(September), 231-240.
  38. Rafiq M, Bugmann G, Easterbrook D (2001). Neural network design for engineering applications, Computers & Structures, 79(17), 1541-1552.
  39. Xu S, Chen L (2008). Novel approach for determining the optimal number of hidden layer neurons for FNN"™s and its application in data mining, In: International Conference on Information Technology and Applications (ICITA), Cairns (Australia), 23-26 June 2008, pp 683-686.
  40. Gunaratnam DJ, Gero JS (1994). Effect of representation on the performance of neural networks in structural engineering applications, Computer-Aided Civil and Infrastructure Engineering, 9(2), 97-108.
  41. Lefik M, Schrefler BA (2003). Artificial neural network as an incremental non-linear constitutive model for a finite element code, Computer Methods in Applied Mech and Eng, 192(28-30), 3265-3283.
  42. Bai Z, Huang G, Wang D, Wang H, Westover M (2014). Sparse extreme learning machine for classification. IEEE Transactions on Cybernetics, 44(10), 1858-70.
  43. Schwenker F, Kestler H, Palm G (2001). Three learning phases for radial-basis-function networks, Neural networks, 14(4-5), 439-58.
  44. Waszczyszyn Z (1999). Neural Networks in the Analysis and Design of Structures, CISM Courses and Lectures No. 404, Springer, Wien, New York.
  45. Deng W-Y, Bai, Z., Huang, G.-B. and Zheng, Q.-H. (2016). A fast SVD-Hidden-nodes based extreme learning machine for large-scale data Analytics, Neural Networks, 77(May), 14-28.
  46. Wilamowski BM (2011). How to not get frustrated with neural networks, 2011 IEEE International Conference on Industrial Technology (ICIT), 14-16 March, IEEE (eds), Auburn Univ., Auburn, AL.
  47. Huang G-B, Zhu Q-Y, Siew C-K (2006a). Extreme learning machine: Theory and applications, Neurocomputing, 70(1-3), 489-501.
  48. Liang N, Huang G, Saratchandran P, Sundararajan N (2006). A fast and accurate online Sequential learning algorithm for Feedforward networks, IEEE Transactions on Neural Networks, 17(6), 1411-23.
  49. Huang G, Chen L, Siew C (2006b). Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE transactions on neural networks, 17(4), 879-92.
  50. Huang G-B, Chen L (2007). Convex incremental extreme learning machine, Neurocomputing, 70(16-18), 3056-3062.
  51. Beyer W, Liebscher M, Beer M, Graf W (2006). Neural Network Based Response Surface Methods - A Comparative Study, 5th German LS-DYNA Forum, October 2006, 29-38, Ulm.
  52. Wilson DR, Martinez TR (2003). The general inefficiency of batch training for gradient descent learning, Neural Networks, 16(10), 1429-1451.
  53. Researcher, The (2018). "Annsoftwarevalidation-report.pdf", figshare, doi: http://doi.org/10.6084/m9.figshare.6962873
  54. Authors (2018b). W_b_arrays [Data set]. Zenodo, http://doi.org/10.5281/zenodo.1469120