Application of artificial neural network on vibration test data for damage identification in bridge girder

International Journal of Physical Sciences

Volumn 6, Issue 35, 2011, Pages 7991-8001

  Hakim, S J S ^a   Abdul Razak, H ^a  

^a UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

Artificial neural networks (ANNs); Back propagation (BP); Damage identification; Natural frequency

Indexed keywords

EID: 84455181467     PISSN: 19921950     EISSN: None     Source Type: Journal    
DOI: 10.5897/IJPS11.1198     Document Type: Article

References (19)

1. Abdul Razak H, Choi FC (2001). The Effect of corrosion on the natural frequency and modal damping of reinforced concrete beams. J. Eng. Struct., 23(1): 1126-1133.
2. Chakraborty D (2005). Artificial neural network based delamination prediction in laminated composites. J. Mat. Design, 26: 1-7.
3. Chen HM, Tsai KH, Qi GZ, Yang JCS, Amini F (1995). Neural network for structure control. J. Comput. Civil Eng., 9(2): 168-175.
4. Fonseca ET, Vellasco PGS (2003). A path load parametric analysis using neural networks. J. Constructional Steel Res., 59: 251-267.
5. Ghaboussi J, Joghatatie A (1995). Active control of structures using neural networks. J. Eng. Mech., ASCE 121(4): 55-567.
6. Hakim SJS, Noorzaei J, Jaafar MS, Jameel M, Mohammadhassani M (2011). Application of Artificial Neural Networks to Predict Compressive strength of High Strength Concrete. Int. J. Phys. Sci., (IJPS). 6(5): 975-981.
7. Hola J, Schabowicz K (2005). Application of artificial neural networks to determine concrete compressive strength based on non-destructive tests. J. Civil Eng. Manag., 11(1): 23-32.
8. Ince R (2004). Prediction of fracture parameters of concrete by artificial neural networks. J. Eng. Fracture Mech., 71: 2143-2159.
9. Jeyasehar CA, Sumangala K (2006). Damage assessment of prestressed concrete beams using artificial neural network (ANN) approach. J. Computers Struct., 84: 1709-1718.
10. Kim YY, Kapania RK (2002). Neural networks for inverse problems in damage identification and optical imaging. 43rd AIAA/ASME/ASCE/AHS/ASC Structures. Structural Dynamics and Materials Conferences, Denver, Colorado.
11. th InternationalModal Analysis Conference. pp. 1128-1134.
12. Lee JJ, Lee JW, Yi JH, Yun CB, Jung HY (2005). Neural networks-based damage detection for bridges considering errors in baseline finite element models. J. Sound, Vibration. 280(3-5): 555-578.
13. Lee SC (2003). Prediction of concrete strength using artificial neural networks. J. Eng. Struct., 25: 849-857.
14. Mansour MY, Dicleli M, Lee YL, Zhang J (2004). Predicting the shear strength of reinforced concrete beams using artificial neural networks. J. Eng. Struct., 26: 781-799.
15. Rumelhart DE, Hinton GE, Williams RJ (1986). Learning representations by back propagation errors. J. Nature, 323: 533-536.
16. Sahin M, Shenoi RA (2003). Quantification and localization of damage in beam-like structures by using artificial neural networks with experimental validation. J. Eng. Struct., 25: 1785-1802.
17. Suh MW, Shim MB, Kim MY (2000).Crack identification using hybrid neuro-genetic technique. J. Sound Vibration, 234(4): 617-635.
18. Wu ZS, Xu B, Yokoyama K (2002). Decentralized parametric damage based on neural networks. J. Computer-Aided Civil Infrastruct. Eng., 17: 175-184.
19. Xu B, Wu ZS, Yokoyama K (2002). A localized identification method with neural networks and its application to structural health monitoring. Structural Engineering. JSCE, 48A: 419-427. http://www.alyuda.com/index.html