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How coupon and element tests reduce conservativeness in element failure prediction

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  • Park, Chan Y.
  • Kim, Nam H.
  • Haftka, Raphael T.

Abstract

Structural elements, such as stiffened panels, are designed by combining material strength data obtained from coupon tests with a failure theory for 3D stress field. Material variability is captured by dozens of coupon tests, but there remains epistemic uncertainty due to error in the failure theory, which can be reduced by element tests. Conservativeness to compensate for the uncertainty in failure prediction (as in the A- or B-basis allowables) results in a weight penalty. A key question, addressed here, is what weight penalty is associated with this conservativeness and how much it can be reduced by using coupon and element tests. In this paper, a probabilistic approach is used to estimate the conservative element failure strength by quantifying uncertainty in the element strength prediction. A convolution integral is used to efficiently combine uncertainty from coupon tests and that from the failure theory. Bayesian inference is then employed to reduce the epistemic uncertainty using element test results. The methodology is examined with typical values of material variability (7%), element test variability (3%), and the error in the failure theory (5%). It is found that the weight penalty associated with no element test is significant (20% heavier than an infinite number of element tests), and it is greatly reduced by more element tests (4.5% for 5 element tests), but the effect of the number of coupon tests is much smaller.

Suggested Citation

  • Park, Chan Y. & Kim, Nam H. & Haftka, Raphael T., 2014. "How coupon and element tests reduce conservativeness in element failure prediction," Reliability Engineering and System Safety, Elsevier, vol. 123(C), pages 123-136.
  • Handle: RePEc:eee:reensy:v:123:y:2014:i:c:p:123-136
    DOI: 10.1016/j.ress.2013.10.012
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    References listed on IDEAS

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    1. Jiang, Xiaomo & Mahadevan, Sankaran, 2007. "Bayesian risk-based decision method for model validation under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 707-718.
    2. Park, Inseok & Amarchinta, Hemanth K. & Grandhi, Ramana V., 2010. "A Bayesian approach for quantification of model uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 777-785.
    3. Urbina, Angel & Mahadevan, Sankaran & Paez, Thomas L., 2011. "Quantification of margins and uncertainties of complex systems in the presence of aleatoric and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1114-1125.
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