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In this work, we introduce an interval formulation that accounts for uncertainty in supporting conditions of structural systems. Uncertainty in structural systems has been the focus of a wide range of research. Different models of uncertain parameters have been used. Conventional treatment of uncertainty involves probability theory, in which uncertain parameters are modeled as random variables. Due to specific limitation of probabilistic approaches, such as the need of a prior knowledge on the distributions, lack of complete information, and in addition to their intensive computational cost, the rationale behind their results is under debate. Alternative approaches such as fuzzy sets, evidence theory, and intervals have been developed. In this work, it is assumed that only bounds on uncertain parameters are available and intervals are used to model uncertainty. Here, we present a new approach to treat uncertainty in supporting conditions. Within the context of Interval Finite Element Method (IFEM), all uncertain parameters are modeled as intervals. However, supporting conditions are considered in idealized types and described by deterministic values without accounting for any form of uncertainty. In the current developed approach, uncertainty in supporting conditions is modeled as bounded range of values, i.e., interval value that capture any possible variation in supporting condition within a given interval. Extreme interval bounds can be obtained by analyzing the considered system under the conditions of the presence and absence of the specific supporting condition. A set of numerical examples is presented to illustrate and verify the accuracy of the proposed approach.
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Optimization Under Uncertainty Versus Algebraic Heuristics: A Research Method for Comparing Computational Design Methods
In this paper, we introduce a research method for comparing computational design methods. This research method addresses the challenge of measuring the difference in performance of different design methods in a way that is fair and unbiased with respect to differences in modeling abstraction, accuracy and uncertainty representation. The method can be used to identify the conditions under which each design method is most beneficial. To illustrate the research method, we compare two design methods for the design of a pressure vessel: 1) an algebraic approach, based on the ASME pressure vessel code, which accounts for uncertainty implicitly through safety factors, and 2) an optimization-based, expected-utility maximization approach which accounts for uncertainty explicitly. The computational experiments initially show that under some conditions the algebraic heuristic surprisingly outperforms the optimization-based approach. Further analysis reveals that an optimization-based approach does perform best as long as the designer applies good judgment during uncertainty elicitation. An ignorant or overly confident designer is better off using safety factors.
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- Award ID(s):
- 1645316
- PAR ID:
- 10026049
- Date Published:
- Journal Name:
- Proceedings of the ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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