Rafi L. Muhanna

Learn More
Latest scientific and engineering advances have started to recognize the need of defining multiple types of uncertainty. Probabilistic modeling cannot handle situations with incomplete or little information on which to evaluate a probability, or when that information is nonspecific, ambiguous, or conflicting [46, 11, 43]. Many interval-based models of(More)
Uncertainty assessment in basin modeling and reservoir characterization is traditionally treated by geostatistical methods which are normally based on stochastic probabilistic approaches. In this talk, an alternative interval-based approach will be present. A solution for the transient heat conduction in sedimentary basins will be introduced using an(More)
Latest scientific and engineering advances have started to recognize the need of defining multiple types of uncertainty. Probabilistic modeling cannot handle situations with incomplete or little information on which to evaluate a probability, or when that information is nonspecific, ambiguous, or conflicting [1]. Many generalized models of uncertainty have(More)
In this work structural reliability assessment is presented for structures with uncertain loads and material properties. Uncertain variables are modeled as fuzzy random variables and Interval Monte Carlo Simulation along with interval finite element method is used to evaluate failure probability. Interval Monte Carlo is compared with existing search(More)
Rafi Muhanna, Vladik Kreinovich, Pavel Šoĺın, Jack Chessa, Roberto Araiza, and Gang Xiang 1Center for Reliable Engineering Computing (REC), Department of Civil & Environmental Engineering, Regional Engineering Program, Georgia Institute of Technology, 210 Technology Circle, Savannah, GA 31407-3038, USA, rafi.muhanna@gtrep.gatech.edu 2University of Texas at(More)
In order to ensure the safety of a structure, one must provide for adequate strength of structural elements. In addition, one must prevent large unstable deformations such as buckling. In most analyses of buckling, structural properties and applied loads are considered certain. This approach ignores the fact that imperfections and unknown changes in(More)
Abstract. Artificial neural networks are powerful tools to learn functional relationships between data. They are widely used in engineering applications. Recurrent neural networks for fuzzy data have been introduced to map uncertain structural processes with deterministic or uncertain network parameters. Based on swarm intelligence, a new training strategy(More)
This paper illustrates how interval analysis can be used as a basis for generalized models of uncertainty. When epistemic uncertainty is presented as a range and the aleatory is based on available information, or when random variables are assigned an interval probability, the uncertainty will have a Probability Bound (PB) structure. When Interval Monte(More)
In the first part of this work, an interval-based finite element formulation for stiffness uncertainty in solid and structural mechanics was introduced. An element-by-element technique was used and Lagrange multipliers method was applied to impose the necessary constraints for compatibility and equilibrium. An exact inverse for the interval stiffness matrix(More)