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Computations with Imprecise Parameters in Engineering Design: Background and Theory
 ASME JOURNAL OF MECHANISMS, TRANSMISSIONS, AND AUTOMATION IN DESIGN
, 1989
"... A technique to perform design calculations on imprecise representations of parameters has been developed and is presented. The level of imprecision in the description of design elements is typically high in the preliminary phase of engineering design. This imprecision is represented using the fuzzy ..."
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Cited by 58 (18 self)
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A technique to perform design calculations on imprecise representations of parameters has been developed and is presented. The level of imprecision in the description of design elements is typically high in the preliminary phase of engineering design. This imprecision is represented using the fuzzy calculus. Calculations can be performed using this method, to produce (imprecise) performance parameters from imprecise (input) design parameters. The Fuzzy Weighted Average technique is used to perform these calculations. A new metric, called the Î³level measure, is introduced to determine the relative coupling between imprecise inputs and outputs. The background and theory supporting this approach are presented, along with one example.
Imprecise probabilities of engineering system failure from random and fuzzy set reliability analysis
 In: de Cooman G, Fine TL, Seidenfeld T, editors. Proc 2nd International symposium on imprecise probabilities and their applications. Maastricht: Shaker Publishing
, 2001
"... Reliability analysis of engineering systems conventionally represents the system state variables as precise probability distributions and generates precise estimates of the probability of system failure. It is demonstrated how this conventional approach can be extended to handle imprecise knowledge ..."
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Cited by 10 (0 self)
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Reliability analysis of engineering systems conventionally represents the system state variables as precise probability distributions and generates precise estimates of the probability of system failure. It is demonstrated how this conventional approach can be extended to handle imprecise knowledge about the system state variables, represented in general as random sets, in order to generate bounds on the probability of failure. The conventional assumption of a precise limit state function is then relaxed. A new method based on linguistic covering of the state variable space with fuzzy set labels is introduced and is used to generate an imprecise limit state function from very scarce experimental data.
Inexact inference for rulebased damage assessment of existing structures
 In Proceedings of the 7th International Joint Conference on Artificial Intelligence
, 1981
"... The knowledge organization of a rulebased damage assessment system of existing structures subjected to earthquake excitation is outlined in this paper. A principle of inexact inference to obtain a rational solution is presented. The fuzzy set theory and the production system with certainty factor a ..."
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Cited by 2 (0 self)
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The knowledge organization of a rulebased damage assessment system of existing structures subjected to earthquake excitation is outlined in this paper. A principle of inexact inference to obtain a rational solution is presented. The fuzzy set theory and the production system with certainty factor are employed jointly in the inexact inference to deal with the continuous nature of the damage state and to attain the modularity of uncertain knowledge, respectively. The role of damage assessment of existing structures has received increasing attention recently [32,33,343. Existing structures refer to
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"... The paper discusses the role of uncertainty in decision making involving spatially distributed systems. It focuses on engineering structures, but draws on experience with other distributed systems. Uncertainty is divided into measurable (aleatoric), modelling (epistemic) and surprisal (ontological), ..."
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The paper discusses the role of uncertainty in decision making involving spatially distributed systems. It focuses on engineering structures, but draws on experience with other distributed systems. Uncertainty is divided into measurable (aleatoric), modelling (epistemic) and surprisal (ontological), and the primary emphasis is on the latter. All three are involved in decision making, and the problem addressed is how to balance their different contributions, given the difficulty of achieving a common metric. The paper concentrates on surprisal uncertainty. Various strategies for assessing it are considered. The effects of surprise can be reduced by increasing system resilience, and recent work in this area is discussed. 1.
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"... is ntem iversi ginee 5 Ma From the engineering point of view, a structural prototypes are performed before the series production and contribute to improve and to validate the model. In is very unusual, not only for economical reasons, but also because a prototype tested in a laboratory can never dif ..."
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is ntem iversi ginee 5 Ma From the engineering point of view, a structural prototypes are performed before the series production and contribute to improve and to validate the model. In is very unusual, not only for economical reasons, but also because a prototype tested in a laboratory can never different. In this sense, particular attention must also be paid to special structures which cannot be listed in the traditional building categories and, as such, are not part of the experience inheritance from the past. In addition, due in particular to the growing complexity of structural systems faced by nowadays designers, the uncertain