Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Solving Engineering Optimization Problems Using Machine Learning Classification-Assisted Differential Evolution Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Deep Neural Network and YUKI Algorithm for Inner Damage Characterization Based on Elastic Boundary Displacement

Authors : Nasreddine Amoura, Brahim Benaissa, Musaddiq Al Ali, Samir Khatir

Published in: Proceedings of the International Conference of Steel and Composite for Engineering Structures

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The efficiency of deep neural networks has been proven in several research fields. In this study, we suggest using this method of inverse crack identification based on the structural response of boundary displacement. This structural response is particularly challenging for surrogate modelling due to the overall similarity in the effect of different cracks. From the inverse problem perspective, this corresponds to a problem of many local minima. To solve this problem we use the newly suggested search technique of the dynamic search space reduction by the YUKI algorithm, build to solve this type of problem. We compare the performance of the suggested approach of the RBF modelling technique in terms of direct problem prediction and inverse problem identification accuracy. Deep Neural Networks are found to have better performance in both problems, although the computation time is significantly higher than RBF.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Collapse Analysis of Reinforced Masonry Arches: A Comparison of Associated and Non-associated Sliding
next chapter Energy Dissipation Based Structural Condition Assessment Using Random Decrement Technique
Literature
1.
Amoura, N., Kebir, H., Benzerdjeb, A.: 3D crack identification using the Nelder-Mead Simplex algorithm combined with a random generation of crack positions. Frattura e Integrita Strutturale16(59), 243–255 (2022)
2.
Amoura, N., Kebir, H., Rechak, S., Roelandt, J.: Axisymmetric and two-dimensional crack identification using boundary elements and coupled quasi-random downhill simplex algorithms. Eng. Anal. Boundary Elem. 34, 611–618 (2010)CrossRefMATH
3.
Al Thobiani, F., Khatir, S., Benaissa, B., Ghandourah, E., Mirjalili, S., Wahab, M. A.: A hybrid PSO and Grey Wolf Optimization algorithm for static and dynamic crack identification. Theoret. Appl. Fract. Mech. 118, 103213 (2022)
4.
Benaissa, B., Köppen, M., Wahab, M.A., Khatir, S.: Application of proper orthogonal decomposition and radial basis functions for crack size estimation using particle swarm optimization. J. Phys. Conf. Ser. 842(1), 012014 (2017) 
5.
Khatir, S., Abdel Wahab, M., Tiachacht, S., Le Thanh, C., Capozucca, R., Magagnini, E., et al.: Damage identification in steel plate using FRF and inverse analysis. Frattura ed Integrita Strutturale-Fracture and Structural Integrity 58, 416–433 (2021)
6.
Khatir, S., Tiachacht, S.,  Benaissa, B., Le Thanh, C., Capozucca, R.,  Abdel Wahab, M.: Damage identification in frame structure based on inverse analysis. In: Proceedings of the 2nd International Conference on Structural Damage Modelling and Assessment, pp. 197–211 (2022)
7.
Khatir, S., Wahab, M.A., Benaissa, B., Köppen, M.: Crack identification using eXtended IsoGeometric analysis and particle swarm optimization. In: Fracture, Fatigue and Wear, pp. 210–222 (2018) 
8.
Behtani, A., Tiachacht, S., Khatir, T., Khatir, S., Wahab, M.A., Benaissa, B.: Residual Force Method for damage identification in a laminated composite plate with different boundary conditions. Frattura ed Integrità Strutturale 16, 35–48 (2022)CrossRef
9.
Amoura, N., Kebir, H., Rechak, S.,  Roelandt, J.: Numerical simulation of the behaviour of cracks in axisymmetric structures by the dual boundary element method. In: Damage and Fracture Mechanics, ed: Springer, pp. 435–443 (2009)
10.
Samir, K.,  Idir,  B., Serra, R.,  Brahim, B., Aicha, A.: Genetic algorithm based objective functions comparative study for damage detection and localization in beam structures. In: Journal of Physics: Conference Series, p. 012035 (2015)
11.
Samir, K., Brahim, B., Capozucca, R., Wahab, M.A.: Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm. Compos. Struct. 187, 344–353 (2018)CrossRef
12.
Benaissa, B., Belaidi, I., Hamrani, A.: identifying defect size in two dimensional plates based on boundary measurements using reduced model and genetic algorithm. J. Sci. Technol. 2, 7–12 (2017)
13.
Gomes, G.F., Mendez, Y.A.D., da Silva Lopes Alexandrino, P.,  da Cunha, S.S., Ancelotti, A.C.:  A review of vibration based inverse methods for damage detection and identification in mechanical structures using optimization algorithms and ANN. Arch. Comput. Methods Eng. 26, 883–897 (2019)
14.
Barai, S., Pandey, P.: Vibration signature analysis using artificial neural networks. J. Comput. Civ. Eng. 9, 259–265 (1995)CrossRef
15.
Zang, C., Imregun, M.: Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. J. Sound Vib. 242, 813–827 (2001)CrossRefMATH
16.
Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., Inman, D.J.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 388, 154–170 (2017)CrossRef
17.
Stavroulakis, G.E., Engelhardt, M., Likas, A., Gallego, R., Antes, H.: Neural network assisted crack and flaw identification in transient dynamics. J. Theor. Appl. Mech. 42, 629–649 (2004)
18.
Stavroulakis, G., Antes, H.: Neural crack identification in steady state elastodynamics. Comput. Methods Appl. Mech. Eng. 165, 129–146 (1998)CrossRefMATH
19.
Khaleghi, M., Haghighat, E., Vahab, M., Shahbodagh, B., Khalili, N.: Fracture characterization from noisy displacement data using artificial neural networks. Eng. Fract. Mech. 271, 108649 (2022)CrossRef
20.
Benaissa, B., Hocine, N.A., Khatir, S., Riahi, M.K., Mirjalili, S.: YUKI algorithm and POD-RBF for Elastostatic and dynamic crack identification. J. Comput. Sci. 55(1851), 101451 (2021)
21.
22.
Stavroulakis, G., Antes, H.: Nondestructive elastostatic identification of unilateral cracks through BEM and neural networks. Comput. Mech. 20, 439–451 (1997)CrossRefMATH
23.
Buljak, V., Maier, G.: Proper orthogonal decomposition and radial basis functions in material characterization based on instrumented indentation. Eng. Struct. 33, 492–501 (2011)CrossRef
24.
Arora, Y., Singhal, A., Bansal, A.: A study of applications of RBF network. Int. J. Comput. Appl. 94(2), 17–20 (2014)
25.
Goodfellow, I., Bengio, Y.,  Courville, A.: Deep learning. MIT press, Cambridge (2016)
26.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature, 521, 436–444 (2015)
27.
Hinton, G.E.: Learning multiple layers of representation. Trends Cogn. Sci. 11, 428–434 (2007)CrossRef
28.
Sharma, O.: Deep challenges associated with deep learning. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 72–75 (2019)
Metadata
Title
Deep Neural Network and YUKI Algorithm for Inner Damage Characterization Based on Elastic Boundary Displacement
Authors
Nasreddine Amoura
Brahim Benaissa
Musaddiq Al Ali
Samir Khatir
Copyright Year
2023
Book
Proceedings of the International Conference of Steel and Composite for Engineering Structures

Print ISBN: 978-3-031-24040-9

Electronic ISBN: 978-3-031-24041-6

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-24041-6_18

Premium Partners

    Image Credits