DOI QR코드

DOI QR Code

Damage assessment of beams from changes in natural frequencies using ant colony optimization

  • Majumdar, Aditi (Department of Aerospace Engineering, Indian Institute of Technology) ;
  • De, Ambar (Department of Civil Engineering, Indian Institute of Technology) ;
  • Maity, Damodar (Department of Civil Engineering, Indian Institute of Technology) ;
  • Maiti, Dipak Kumar (Department of Aerospace Engineering, Indian Institute of Technology)
  • Received : 2011.06.21
  • Accepted : 2013.01.06
  • Published : 2013.02.10

Abstract

A numerical method is presented here to detect and assess structural damages from changes in natural frequencies using Ant Colony Optimization (ACO) algorithm. It is possible to formulate the inverse problem in terms of optimization and then to utilize a solution technique employing ACO to assess the damage/damages of structures using natural frequencies. The laboratory tested data has been used to verify the proposed algorithm. The study indicates the potentiality of the developed code to solve a wide range of inverse identification problems in a systematic manner. The developed code is used to assess damages of beam like structures using a first few natural frequencies. The outcomes of the simulated results show that the developed method can detect and estimate the amount of damages with satisfactory precision.

Keywords

References

  1. Beena, P. and Ganguli, R. (2010), "Structural damage detection using fuzzy cognitive maps and hebbian learning", Applied Soft Computing, 11, 1014 - 1020.
  2. Bell, E.J. and McMullen, R.P. (2004), "Ant colony optimization techniques for the vehicle routing problem", Advanced Engineering Informatics, 18, 41-48 https://doi.org/10.1016/j.aei.2004.07.001
  3. Cerri, N.M. and Vestroni, F. (2000), "Detection of damage in beams subjected to diffused cracking", J. Sound Vibration, 234, 259-276. https://doi.org/10.1006/jsvi.1999.2887
  4. Dorigo, M. and Stutzle, T. (2004), Ant colony optimization, The MIT Press, Cambridge, Massachusetts, London, England.
  5. Kaveh, A. and Daei, M. (2009), "Efficient force method for the analysis of finite element models comprising of triangular elements using ant colony optimization", Finite Elements in Analysis and Design, 45, 710-720. https://doi.org/10.1016/j.finel.2009.06.005
  6. Kaveh, A., Farahmand, B.A., Hadidi, A., Sorochi, F.R. and Talatahari, S. (2010), "Performance-based seismic design of steel frames using ant colony optimization", Journal of Constructional Steel Research, 66(4), 566-574. https://doi.org/10.1016/j.jcsr.2009.11.006
  7. Kaveh, A., Hassani, B., Shojaee, B. and Tavakkoli, M.S. (2008), "Structural topology optimization using ant colony methodology", Computers and Structures, 86, 1539-1549. https://doi.org/10.1016/j.compstruc.2007.05.009
  8. Liu, R.G. and Chen, C.S. (2002), "A noval technique for inverse identification of distributed stiffness factor in structures", J. Struct. Eng., 254, 823-835.
  9. Maity, D. and Saha, A. (2004), "Damage assessment in structure from changes in static parameter using neural networks", Sadhana, Journal of the Indian Academy of Sciences, 29, 315-327.
  10. Maity, D. and Tripathy, R.R. (2005), "Damage assessment of structure from changes in natural frequencies using genetic algorithm", Structural Engineering and Mechanics, 19, 21-42. https://doi.org/10.12989/sem.2005.19.1.021
  11. Mares, C. and Surace, C. (1996), "An application of genetic algorithms to identify damage in elastic structures", J. Sound Vibration, 195, 195-215. https://doi.org/10.1006/jsvi.1996.0416
  12. Meziane, R., Massim, Y., Zeblah, A., Ghoraf, A. and Rahli, R. (2005), "Reliability optimization using ant colony algorithm under performance and cost constraints", Electric Power Systems Research, 76, 1-8. https://doi.org/10.1016/j.epsr.2005.02.008
  13. Morassi, A. (2001), "Identification of a crack in in a rod based on changes in a pair of natural frequencies", J. Sound Vibration, 242, 577-596. https://doi.org/10.1006/jsvi.2000.3380
  14. Nikolakopoulos, P., Katsareas, D. and Papadopoulos, C. (1997), "Crack identification in frame structures", J. Computer Structure, 64, 389-406. https://doi.org/10.1016/S0045-7949(96)00120-4
  15. Ruotolo, R. and Surace, C. (1997), "Damage assessment of multiple cracked beams: Numerical results and experimental validation", J. Sound Vibration, 206, 567-588. https://doi.org/10.1006/jsvi.1997.1109
  16. Sahoo, B. and Maity, D. (2007), "Damage assessment of structures using hybrid neuro-genetic algorithm", Applied Soft Computing, 7, 89-104. https://doi.org/10.1016/j.asoc.2005.04.001
  17. Sanayei, M. and Onipede, O. (1991), "Damage assessment of structure using static test data", AIAA, 29, 1174-1179. https://doi.org/10.2514/3.10720
  18. Shyu, S.J., Lin, T.M.B. and Yin, Y.P.(2004), "Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time", Computer & Industrial Engineering, 47, 181-193. https://doi.org/10.1016/j.cie.2004.06.006
  19. Tripathy, R.R. and Maity, D. (2004), "Damage assessment of structures from changes its curvature damage factor using artificial neural network", Indian J. of Engineering & material Science, 11, 369-377.
  20. Vallabhaneni, V. and Maity, D. (2011), "Application of Radial Basis Neural Network on Damage Assessment of Structures ", Procedia Engineering, 14, 3104 - 3110. https://doi.org/10.1016/j.proeng.2011.07.390
  21. Wu, X., Ghaboussi, J. and Garrett, H.J. (1992), "Use of neural networks in detection of structural damage", Computer Structure, 42, 649-659. https://doi.org/10.1016/0045-7949(92)90132-J

Cited by

  1. Multi-swarm fruit fly optimization algorithm for structural damage identification vol.56, pp.3, 2015, https://doi.org/10.12989/sem.2015.56.3.409
  2. Modal parameter based inverse approach for structural joint damage assessment using unified particle swarm optimization vol.242, 2014, https://doi.org/10.1016/j.amc.2014.05.115
  3. Structural damage detection including the temperature difference based on response sensitivity analysis vol.53, pp.2, 2015, https://doi.org/10.12989/sem.2015.53.2.249
  4. Structural damage detection by principle component analysis of long-gauge dynamic strains vol.54, pp.2, 2015, https://doi.org/10.12989/sem.2015.54.2.379
  5. Crack Assessment in Frame Structures Using Modal Data and Unified Particle Swarm Optimization Technique vol.17, pp.5, 2014, https://doi.org/10.1260/1369-4332.17.5.747
  6. Damage identification of 2D and 3D trusses by using complete and incomplete noisy measurements vol.52, pp.1, 2014, https://doi.org/10.12989/sem.2014.52.1.149
  7. Identification of distributed damage in bridges from vehicle-induced dynamic responses vol.19, pp.6, 2016, https://doi.org/10.1177/1369433216630443
  8. Natural Computing Applied to the Underground System: A Synergistic Approach for Smart Cities vol.18, pp.12, 2018, https://doi.org/10.3390/s18124094
  9. Hierarchical neural network for damage detection using modal parameters vol.70, pp.4, 2013, https://doi.org/10.12989/sem.2019.70.4.457
  10. Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization vol.12, pp.13, 2013, https://doi.org/10.3390/ma12132133