Advanced engineering solutions are characterized by inherent robustness and flexibility as essential features for a faultless life of structures under uncertain and changing conditions. An implementation of these features in a structure or system requires a comprehensive consideration of uncertainty in structural and environmental parameters as well as in the numerical simulation models. This is indispensable for a reliable numerical analysis, assessment and prediction of the life cycle of a structure or system under environmental processes associated.

Structural design should consider the polymorphic nature and characteristic of the available information. Uncertainties inherently present in e.g. resistance of structural materials, environmental and man-imposed loads, boundary conditions, physical and numerical models. Generally, the availability of information in engineering practice is limited. Incomplete, fragmentary, diffuse, and frequently expert specified knowledge leads to imprecision in data. In addition, engineers have to cope with the objective variability and fluctuations in material, geometry and loading.

This paper presents research results recently obtained by the authors. The main focus is computational methods developed for consideration of polymorphic uncertainty in structural analysis and design. The improvement of the numerical efficiency is discussed utilizing neural network based response surface approximation schemes. Artificial neural network approaches and extreme learning machines are highlighted, which can by applied for several tasks, e.g., numerical structural monitoring, reliability analysis and robustness assessment.

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