Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
| Hi-index | 0.01 |
This paper presents a general methodology for computer-aided multi-criteria optimal design which allows all the different design criteria to be traded off while accounting for modeling and loading uncertainties. Uncertain system parameters are modeled as random variables with a prescribed joint probability density function, whereas the loads are modeled as stochastic processes. Second-order probabilistic descriptors are combined with approximate extreme response theories to obtain reliability estimates for the structural systems. A multi-criteria optimal design methodology, based on a preference aggregation rule, is used in this formulation. Optimization is carried out by generating and solving a sequence of explicit approximate problems. The uncertainty in system parameters is taken explicitly in the analysis and its effect is investigated on the optimal design. Uncertainties are important because they can change the optimal design significantly. A numerical example demonstrates the performance and efficiency of the proposed methodology.