Polymorphic uncertainty modeling of heterogeneous thermo-hydro-mechanical coupled systems under vague assumptions of parameter correlations

New hybrid working materials and constructions with heterogeneous material properties are due to the development of new light-weight building concepts more and more in use. Furthermore, many natural and technical materials are used which show a heterogeneous material distribution in the existing engineering constructions. Examples are typical geoscience materials or aggregate materials w.r.t their meso- and microscopic treatment in a multi-scale approach. The modeling of the material behavior during forecast simulations can either be done by the estimation of upper and lower bounds or by the application of multi-dimensional random fields.

In multi-field situations, e.g. coupled thermal-hydro-mechanical systems like dams, dikes or geologic deposits, there are a series of sensitive material properties which can be modeled via random fields. Often, these fields exhibit a certain correlation, e.g. in regions of old and aged material the hydraulic permeability might be increased while the mechanical stiffness is reduced. The question arising is, if this has also effects on other material properties and if yes, how the interdependency can be taken effectively into consideration.
In the given proposal, a general methodology shall be derived which allows, based on a polymorphic uncertainty model, the generation of random fields for multi-physic applications, where however, the degree of interdependence is not fully known. Therefore, the project comprises the development, analysis and application of a polymorphic uncertainty model, which captures the random variability of the material properties with vague information concerning their correlations.

The methodology is planned to be applied for the assessment of the reliability of the structures in civil and geotechnical engineering which are dominated by multi-physical phenomena.The qualitative gain applying polymorphic uncertainty models compared to classicaluni-morphic models should be analyzed.
As outlook for a second funding term, the related inverse problems might be tackled, e.g. how to determine parameters of the systems in a polymorphic uncertainty model and how to asses uncertainties in the identified parameters.