The predetection evolution of stress corrosion cracking has been examined as a necessary preliminary to effective detection of such cracks. Anodic dissolution (AD) and hydrogen embrittlement (HE) have been considered to calculate the stress corrosion crack (SCC) growth in AA7050-T6 for a surface-breaking crack with blunt tip in an aqueous environment. Since these processes are not completely deterministic, several advanced statistical methods have been used to introduce probabilistic considerations. Based on the data from designed computer experiments, the computer code developed by the authors (Lee et al., 2015, “A Comprehensive Analysis of the Growth Rate of Stress Corrosion Cracks,” Proc. R. Soc. A, 471(2178), p. 20140703) to conduct deterministic stress corrosion crack growth analysis has been represented by metamodels using Gaussian process regression. Through sensitivity analysis, important variables which need to be calibrated have been identified. The dynamic Bayesian network (DBN) model and Monte Carlo simulation (MCS) have been utilized to quantify uncertainties. Statistical parameters of input variables have been obtained by a machine learning technique. The calibrated model has been validated using Bayesian hypothesis testing. Since the DBN model yields a probability of detection (POD) comparable to the probability based on binary validation data, the probabilistic model with calibrated parameters is expected to well represent the growth of a stress corrosion crack. The results also show that the reliability largely depends on the accuracy of flaw detection methods and on the critical crack length.
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February 2016
Research-Article
Probabilistic Analysis of Stress Corrosion Crack Growth and Related Structural Reliability Considerations
Dooyoul Lee,
Dooyoul Lee
Department of Mechanical Engineering,
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
Search for other works by this author on:
Yonggang Huang,
Yonggang Huang
Department of Mechanical Engineering,
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
Search for other works by this author on:
Jan D. Achenbach
Jan D. Achenbach
Department of Mechanical Engineering,
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
Search for other works by this author on:
Dooyoul Lee
Department of Mechanical Engineering,
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
Yonggang Huang
Department of Mechanical Engineering,
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
Jan D. Achenbach
Department of Mechanical Engineering,
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
McCormick School of Engineering
and Applied Science,
Northwestern University,
Evanston, IL 60208
Contributed by the Applied Mechanics Division of ASME for publication in the JOURNAL OF APPLIED MECHANICS. Manuscript received September 11, 2015; final manuscript received October 20, 2015; published online November 11, 2015. Assoc. Editor: Arun Shukla.
J. Appl. Mech. Feb 2016, 83(2): 021003 (9 pages)
Published Online: November 11, 2015
Article history
Received:
September 11, 2015
Revised:
October 20, 2015
Citation
Lee, D., Huang, Y., and Achenbach, J. D. (November 11, 2015). "Probabilistic Analysis of Stress Corrosion Crack Growth and Related Structural Reliability Considerations." ASME. J. Appl. Mech. February 2016; 83(2): 021003. https://doi.org/10.1115/1.4031899
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