Sage Journals: Discover world-class research

Abstract

Aging aerospace structures undergo severe damage due to corrosion, fatigue, creep, and other factors. The objective of this study is to estimate a unique parameter, called a safety index that gives an indication of the safety or reliability of these structures. To obtain the safety index, evolutionary computations using neural network based particle swarm optimization technique were developed to evolve the required data for the network. A simulation program, based on the neural network and optimization techniques, was implemented in MS visual basic/VC⁺⁺ environment to predict the safety index of a damaged structure. Analytical solutions were used to predict the effects of crack, fatigue, and creep damage mechanisms. The results obtained from the simulation program were compared to the analytical solutions as there is no experimental data available for multiple damage mechanisms of an aging structure. Based on the results obtained, it appears that the proposed approach of obtaining the safety of a structure is reasonable, and the developed simulation program might be useful as an initial estimate to assess the structural integrity. However, more experimental work will be needed to establish the utility of this approach for practical applications.

Keywords

aging structures safety index structural integrity neural networks particle swarm optimization

Get full access to this article

View all access options for this article.

References

Atluri, S.N. and Tong, P. 1991. “Computational Schemes for Integrity Analyses of Fuselage Panels in Aging Airplanes,” In: Atluri, S.N. , Sampath, S.G. and Tong, P. (eds), Structural Integrity of Aging Airplanes, Springer-Verlag, Berlin, Heidelberg .

Bartelds, G. 1998. “Aircraft Structural Health Monitoring, Prospects for Smart Solutions from a European Viewpoint” , Journal of Intelligent Material Systems and Structures, 9: 906–910 .

Boeringer, D.W. and Werner, D.H. 2004. “Particle Swarm Optimization versus Genetic Algorithms for Phased Array Synthesis” , IEEE Trans. Antennas Propag., 52: 771–779 .

Cheng, L. , Huang, W.L. and Zhou, C.Y. 1999. “Artificial Neural Network Technology for the Data Processing of On-line Corrosion Fatigue Crack Growth Monitoring” , International Journal of Pressure Vessels and Piping, 76: 113–116 .

Dowling, N.E. 1993. Mechanical Behavior of Materials, Prentice Hall Inc., Englewood Cliffs, NJ .

Eberhart, R.C. and Kennedy, J. 1995. “A New Optimizer using Particle Swarm Theory” , In: Proc. Sixth Intl. Symp. on Micro Machine and Human Science, (Nagoya Japan), pp. 30–43 , IEEE Service Center, Piscataway, NJ.

Gies, D. and Rahmat-Samii, Y. 2003. “Particle Swarm Optimization for Reconfigurable Phase-differentiated Array Design” , Microwave Optics. Technol. Lett., 38: 168–175 .

Kennedy, J. and Eberhart, R.C. 1995. “Particle Swarm Optimization” , In: Proc. IEEE Intl. Conf. on Neural Networks, Volume IV, pp. 1942–1948 , IEEE Service Center, Piscataway, NJ.

Koch, G.H. 1995. “On the Mechanisms of Interaction between Corrosion and Fatigue Cracking in Aircraft Aluminum Alloys,” In: Chang, C.I. and Sun, C.T. (eds), Structural Integrity of Aging Aircraft, AD-Vol. 47, pp. 159–169, American Society of Mechanical Engineers .

10.

Mar, J.W. 1991. “Structural Integrity of Aging Airplanes: A Perspective,” In: Atluri, S.N. , Sampath, S.G. and Tong, P. (eds), Structural Integrity of Aging Airplanes, Springer-Verlag, Berlin, Heidelberg .

11.

Moukawsher, E.J. , Heinemann, M.B. and Grandt, Jr., A.F. 1996a. “Residual Strength of Panels with Multiple Site Damage” , Journal of Aircraft, 33(5): 1014–1021 .

12.

Moukawsher, E.J. , Grandt, Jr., A.F. and Neuss, M.A. 1996b. “Fatigue Life of Panels with Multiple Site Damage” , Journal of Aircraft, 33(5): 1003–1013 .

13.

Pidaparti, R.M.V. and Palakal, M.J. 1995. “Neural Network Approach to Fatigue-crack-growth Prediction under Aircraft Spectrum Loading” , Journal of Aircraft, 32(4): 825–831 .

14.

Pidaparti, R.M.V. and Palakal, M.J. 1998. “Fatigue Crack Growth Predictions in Aging Aircraft Panels using Optimization Neural Network” , AIAA Journal, 36(7): 1300–1304 .

15.

Pidaparti, R.M. and Jayanti, S. 2003. “Corrosion Fatigue Through Particle Swarm Optimization” , AIAA J., 41: 1167–1171 .

16.

Russell, P. and Norvig, P. 1995. Artificial Intelligence: A Modern Approach, Prentice Hall Inc., Upper Saddle River, New Jersey .

17.

Schutte, J.F. , Koh, B. , Reinbolt, J.A. , Haftka, R.T. , George, A. D. and Fregly, B.J. 2005. “Evaluation of a Particle Swarm Algorithm for Biomechanical Optimization” , ASME Journal of Biomechanical Engineering, 127: 465–474 .

18.

Shi, Y.H. and Eberhart, R.C. 1999. “Parameter Selection Particle Swarm Optimizer” , In: Proceedings EP98: Annual Conference on Evolutionary Programming, pp. 591–600 , San Diego, California.

19.

Smith, B.L. , Saville, Perry, A. , Mouak, Adil and Myose, Roy, Y. 2000. “Strength of 2024-T3 Aluminum Panels with Multiple Site Damage” , Journal of Aircraft, 37(2): 325–331 .

20.

Venkatesh, V. and Rack, H.J. 1999. “A Neural Network Approach to Elevated Temperature Creep-fatigue Life Prediction” , International Journal of Fatigue, 21: 225–234 .

21.

Wang, L. and Atluri, S.N. 1996. “Predictions of Stable Growth of Lead Crack and Multiple Site Damage using Elastic-plastic Finite Element Method (EPFEM) and Elastic-plastic Finite Element Alternating Method (EPFEAM)” , In: Proceedings of the FAA/NASA Symposium on the continued Airworthiness of Aircraft Structures, pp. 505–518 , Atlanta, GA.

22.

Wang, Q.Y. , Pidaparti, R.M. and Palakal, M.J. 2001. “Comparative Study Corrosion Fatigue in Aircraft Materials” , AIAA Journal, 39(2): 325–330 .

23.

Zamber, J.E. and Hillberry, B.M. 1999. “Probabilistic Approach to Predicting Fatigue lives of Corroded 2024-T3” , AIAA Journal, 37(10): 1311–1317 .

Estimation of Safety Index of Aging Structures through Neural Networks and Particle Swarm Optimization

Abstract

Keywords

Get full access to this article

References