Lifetime prediction using accelerated test data and neural networks

Computers and Structures

Volumn 87, Issue 19-20, 2009, Pages 1187-1194

  Freitag, S ^a   Beer, M ^b   Graf, W ^a   Kaliske, M ^a  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Accelerated life testing; Acceleration function; Lifetime prediction; Model free prediction; Neural network; Textile reinforcement

Indexed keywords

ACCELERATED LIFE TESTING; ACCELERATED TESTS; FUNCTION APPROXIMATION; INITIAL STAGES; LIFETIME PREDICTION; LIMITED DATA; MODEL-FREE PREDICTION; NOVEL MATERIALS; NUMERICAL EXAMPLE; SERVICE LOADS; TEXTILE REINFORCED CONCRETES; TEXTILE REINFORCEMENT; TIME-SCALES;

BUILDING MATERIALS; CIVIL ENGINEERING; CONCRETES; LIGHT MEASUREMENT; NEURAL NETWORKS; REINFORCED CONCRETE; TESTING; TEXTILES;

REINFORCEMENT;

EID: 69249234659     PISSN: 00457949     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compstruc.2008.12.007     Document Type: Article

References (26)

1. Viertl R. Statistical methods in accelerated life testing (1988), Vandenhoeck & Ruprecht, Göttingen
2. Bagdonavicius V., and Nikulin M. Accelerated life models: modeling and statistical analysis (2001), CRC-Press, Boca Raton
3. Schuller S., Schilinsky P., Hauch J., and Brabec C.J. Determination of the degradation constant of bulk heterojunction solar cells by accelerated lifetime measurements. Appl Phys A 79 (2004) 37-40
4. Bank L.C., Gentry T.R., Thompson B.P., and Russell J.S. A model specification for FRP composites for civil engineering structures. Constr Build Mater 17 6-7 (2003) 405-437
5. Benjamin J.R., and Cornell C.A. Probability, statistics and decision for civil engineers (1970), McGraw-Hill, New York
6. Beer M. Model-free sampling. Struct Saf 29 (2007) 49-65
7. Simpson T.W., Peplinski J.D., Koch P.N., and Allen J.K. Metamodels for computer-based engineering design: survey and recommendations. Eng Comput 17 (2001) 129-150
8. Liebscher M, Beer M, Pannier S, Thiele M, Graf W. Sampling schemes for crashworthiness investigations in view of robustness and reliability. In: Papadrakakis, Charmpis, Lagaros, Tsompanakis, editors. ECCOMAS, Thematic conference on computational methods in structural dynamics and earthquake engineering, CD-ROM. Rethymno; 2007.
9. Papadrakakis M., and Lagaros N.D. Reliability-based structural optimization using neural networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 191 (2002) 3491-3507
10. Liebscher M, Beer M, Möller B, Graf W. Fuzzy structural analysis in view of numerical efficiency. In: Kanda, Takada, Furuta, editors. ICASP, Applications of statistics and probability in civil engineering, CD-ROM. London: Taylor & Francis; 2007.
11. Most T., and Bucher C. Probabilistic analysis of concrete cracking using neural networks and random fields. Probabilist Eng Mech 22 (2007) 219-229
12. Unger JF, Könke C. Neural networks as material models within a multiscale approach. In: Topping, editor. Proceedings of the ninth international conference on the application of artificial intelligence to civil, structural and environmental engineering. Stirlingshire: Civil-Comp Press; 2007. Paper 36.
13. Novák D., and Lehkeý D. ANN inverse analysis based on stochastic small-sample training set simulation. Eng Appl Artif Intel 19 (2006) 731-740
14. Möller B., and Reuter U. Uncertainty forecasting in engineering (2007), Springer, Berlin, Heidelberg
15. Beer M, Spanos PD. Neural network based Monte Carlo simulation of random processes. In: Augusti, Schuëller, Ciampoli, editors. Proceedings of the ninth international conference on structural safety and reliability. Rotterdam: Millpress; 2005. p. 2179-86
16. Adeli H, Jiang X. Toward smart structures: novel wavelet-chaos-dynamic neural network models for vibration control and health monitoring of highrise building and bridge structures under extreme dynamic loading. In: Topping BHV, editor. Civil engineering computations: tools and techniques. Stirlingshire: Saxe-Coburg Publications; 2007. p. 1-24.
17. Marquardt C., and Zenner H. Lifetime calculation under variable amplitude loading with the application of artificial neural networks. Int J Fatigue 27 (2005) 920-927
18. Pierce S.G., Worden K., and Bezazi A. Uncertainty analysis of a neural network used for fatigue lifetime prediction. Mech Syst Signal Proc 22 (2008) 1395-1411
19. Bezazi A., Pierce S.G., Worden K., and Harakati E.H. Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network. Int J Fatigue 29 (2007) 738-747
20. Man H., Furukawa T., Hoffman M., and Imlao S. An indirect implicit technique for modelling piezoelectric ceramics. Comp Mater Sci 43 (2008) 629-640
21. Meade A.J., and Fernandez A.A. Solution of non-linear ordinary differential equations by feedforward neural networks. Math Comput Model 20 9 (1994) 19-44
22. Hyndman R.J., and Fan Y. Sample quantiles in statistical packages. Am Stat 50 (1996) 361-365
23. Bosch K. Statistik Taschenbuch (1998), Oldenbourg, München
24. Haykin S. Neural networks (1999), Prentice-Hall, Upper Saddle River
25. Beer M, Spanos PD. A neural network approach for simulating stationary stochastic processes. Special issue on uncertainly modeling in structural analysis and mechanics. Int J Struct Eng Mech, 2009, in press.
26. Ortlepp S, Jesse F. Experimental investigation of static fatigue strength of textile reinforced concrete. In: Hegger, Brameshuber, Will, editors. Textile reinforced concrete - proceedings of the first international RILEM conference. Bagneux: RILEM Publications S.A.R.L.; 2006. p. 131-40.