Applications

Uncertainty quantification

The goal of uncertainty quantification is to assign an appropriate mathematical model to real-world information with respect to objective and subjective uncertainty.

Prediction of system responses

Future structural behavior can be predicted by analysis and forecast of fuzzy time series by applying fuzzy ARMA processes as well as fuzzy artificial neural networks

Life time processes

Fuzzy functions and fuzzy processes are used to describe the uncertain load and modification process of a structure. The numerical simulation of the load and modification process enables the calculation and prediction of structural behavior and the assessment of the structural condition.

Time-dependent reliability

The time-dependent reliability of structures is computed with the aid of the new methods fuzzy Monte-Carlo simulation and fuzzy adaptive importance sampling.

Blasting of structures

In the stochastic sense each blasting operation is an individual event that is generally characterized by limited data and distinctive data uncertainty. For a formal description of data uncertainty the mathematical model fuzzy randomness allows a generalized modeling of uncertainty where the special cases randomness and fuzziness are included.

Numerical efficiency improvement

Numerical efficiency is a decisive issue in uncertainty processing in view of making the respective analysis methods applicable in engineering practice. Subsequently, a selection of beneficial efficiency improving methods and procedures from the variety of developments are presented.