In order to determine the probabilistic characteristics (i.e., means and standard deviations) of the extremities of structural natural frequencies, an extended unified interval stochastic sampling (X-UISS) method is implemented for the purpose of effective hybrid uncertain free vibration analysis.
This paper presents a new robust method namely, unified interval Chebyshev-based random perturbation method, to tackle hybrid random interval structural natural frequency problem.
Note that such an interval represents individual data pages and continuous data pages in a unified way the interval of an individual data page at position a on disk will be [a, a].
The concept of robust structural reliability profile for systems involving hybrid uncertainties is discussed, and then a new computational scheme, namely the unified interval stochastic reliability sampling (UISRS) approach, is proposed for assessing the safety of engineering structures.
A comprehensive computational analysis framework, namely generalized unified interval stochastic sampling, is devised to furnish the statistical features, including means, standard deviations, probability density functions and cumulative distribution functions, of the lower and upper bounds of the nonlinear random interval structural behaviours.
A novel computational approach, namely the extended unified interval stochastic sampling (X-UISS) method, is proposed to calculate the statistical characteristics (i.e., mean and standard deviation) of the extreme bounds (i.e., lower and upper bounds) of the concerned responses (e.g., displacement and stress) of engineering structure involving hybrid spatially dependent uncertainties.
A hybrid probabilistic and interval computational scheme, namely the unified interval stochastic sampling (UISS) approach, is proposed to determine the statistical characteristics (i.e., mean and standard deviation) of the lower and upper bounds of the collapse loads of engineering structures involving mixture of random and interval uncertain system parameters.
By using spline interpolation, we resample the data and confine the interval to a unified size.
We prefer a unified approach, where test and confidence interval is based on similar principles.
This is done by combining both local and global information into a unified formalism by using aggregation operators and according to fuzzy interval representation of the desired objectives.
