Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Vertex method for computing functions of fuzzy variables
Fuzzy Sets and Systems
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
The three semantics of fuzzy sets
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Introduction to Fuzzy Reliability
Introduction to Fuzzy Reliability
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Theory and Practice of Uncertain Programming
Theory and Practice of Uncertain Programming
The transformation method for the simulation and analysis of systems with uncertain parameters
Fuzzy Sets and Systems - Fuzzy intervals
Fuzzy logic and probability applications: bridging the gap
Fuzzy logic and probability applications: bridging the gap
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
The vulnerability of structures to unforeseen events
Computers and Structures
Reliability bounds through random sets: Non-parametric methods and geotechnical applications
Computers and Structures
Designing robust structures - A nonlinear simulation based approach
Computers and Structures
Prediction of uncertain structural responses using fuzzy time series
Computers and Structures
Structural collapse simulation under consideration of uncertainty - Fundamental concept and results
Computers and Structures
Uncertainty Assessment of Large Finite Element Systems
Uncertainty Assessment of Large Finite Element Systems
Designing robust structures - A nonlinear simulation based approach
Computers and Structures
Structural reliability assessment based on probability and convex set mixed model
Computers and Structures
A survey on approaches for reliability-based optimization
Structural and Multidisciplinary Optimization
Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data
Computers and Structures
Sequential robust optimization of a V-bending process using numerical simulations
Structural and Multidisciplinary Optimization
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Interval multi-objective optimisation of structures using adaptive Kriging approximations
Computers and Structures
On robust design optimization of truss structures with bounded uncertainties
Structural and Multidisciplinary Optimization
On the use of a class of interior point algorithms in stochastic structural optimization
Computers and Structures
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This paper provides a review of various non-traditional uncertainty models for engineering computation and responds to the criticism of those models. This criticism imputes inappropriateness in representing uncertain quantities and an absence of numerically efficient algorithms to solve industry-sized problems. Non-traditional uncertainty models, however, run counter to this criticism by enabling the solution of problems that defy an appropriate treatment with traditional probabilistic computations due to non-frequentative characteristics, a lack of available information, or subjective influences. The usefulness of such models becomes evident in many cases within engineering practice. Examples include: numerical investigations in the early design stage, the consideration of exceptional environmental conditions and socio-economic changes, and the prediction of the behavior of novel materials based on limited test data. Non-traditional uncertainty models thus represent a beneficial supplement to the traditional probabilistic model and a sound basis for decision-making. In this paper non-probabilistic uncertainty modeling is discussed by means of interval modeling and fuzzy methods. Mixed, probabilistic/non-probabilistic uncertainty modeling is dealt with in the framework of imprecise probabilities possessing the selected components of evidence theory, interval probabilities, and fuzzy randomness. The capabilities of the approaches selected are addressed in view of realistic modeling and processing of uncertain quantities in engineering. Associated numerical methods for the processing of uncertainty through structural computations are elucidated and considered from a numerical efficiency perspective. The benefit of these particular developments is emphasized in conjunction with the meaning of the uncertain results and in view of engineering applications.