Title page for ETD etd-01242009-063009

Type of Document Master's Thesis
Author Hess, Paul E.
URN etd-01242009-063009
Title Uncertainty in marine structural strength with application to compressive failure of longitudinally stiffened panels
Degree Master of Science
Department Aerospace Engineering
Advisory Committee
Advisor Name Title
Hughes, Owen F. Committee Chair
Adamchak, John C. Committee Member
Ayyab, Bilal M. Committee Member
Nikolaidis, Efstratios Committee Member
  • Reliability (Engineering)
Date of Defense 1994-05-05
Availability restricted
It is important in structural analysis and design, whether deterministic or reliability-based, to know the level of uncertainty for the methods of strength prediction. The uncertainty associated with strength prediction is the result of ambiguity and vagueness in the system. This study addresses the ambiguity component of uncertainty; this includes uncertainty due to randomness in the basic strength parameters (random uncertainty) and systematic errors and scatter in the prediction of strength (modeling uncertainty). The vagueness component is briefly discussed.

A methodology for quantifying modeling and random uncertainty is presented for structural failure modes with a well defined limit state. A methodology is also presented for determining the relative importance of the basic strength parameters in terms of their importance to the total random uncertainty. These methodologies are applied to the compressive failure of longitudinally stiffened panels. The strength prediction model used in this analysis was developed in the UK and is widely used in analysis and design. Several experimental sample sets are used in the analysis. Mean values and coefficients of variation are reported for the random and modeling uncertainties.

A comparison with results from other studies with several strength prediction algorithms is undertaken for the modeling uncertainty. All of these studies involve longitudinally stiffened panels which fail in axially compressive collapse. Ranges for the mean and coefficient of variation of the modeling uncertainty are presented.

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