Structural design of reinforced concrete based on multiscale modelling and polymorphic uncertainty
Abstract of the planned project content, taking into account the results from the first project phase
In the second funding phase of the subproject, the focus is on the one hand on the consideration of time-dependent loadings and resistances and on the other hand on the further development of the mechanical modelling of a mesocopic representative volume element (RVE) for concrete. Time-dependent loading processes require new efficient uncertainty quantification methods for stationary or transient stochastic or fuzzy processes. As a consequence of the time dependence of the structural analysis, both the numerical effort and the complexity of the uncertainty quantification increase. Suitable reduction methods are necessary to be developed and investigated in order to decouple the uncertainty quantification and the calculation of representative measures of the structural RVE analysis.
The mechanical behaviour of the concrete RVE and the interaction of the seperate heterogeneities are intended to be modelled more realistically. The model generator of the geometrically uncertain RVEs is extended by steel reinforcement elements and biaxial textile structures, and the interaction with the surrounding cement matrix will be assumed by Coulomb friction and damage evolution within the interface layer. In the first funding phase of the subproject, the constitutive behaviour of the RVE was approximated with numerical material tests and recurrent neural networks with long-short term memory cells and utilized as uncertain material tangent at the structural level. In addition to gated recurrent units, data-driven solution methods are investigated and compared with regard to their suitability in the context of numerical multi-scale simulation. Special focus is put on the extension of both methods (machine learning-based and data-driven) for a precise prediction of time-dependent uncertain structural responses of the RVE. Beforehand, adaptive sampling methods (ensemble learning, spce filling, etc.) are developed for the approximation methods.
The developed approach of uncertain multiscale simulation of concrete structures will be applied in the context of shape optimisation yielding robust structures. The objective of the uncertainty analysis is to identify and evaluate the robustness of a structural design over a predefined period of use.
Essential project goals and objectives
- realistic geometric and mechanical modelling of a reinforced concrete RVE
- uncertainty quantification considering time-dependent loadings and structural responses
- adaptive sampling methods for ML and data-driven approximation of effective RVE quantities
- shape-based Robust Design Optimization (RDO)
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Leichsenring, F.; Graf. W.; Kaliske, M.:
Surrogate Model based Structural Analysis of reinforced concrete structures with Polymorphic Uncertainties. 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP),
Crete, 2019 -
Leichsenring, F.; Graf. W.; Kaliske, M.:
Modelling of polymorphic uncertainty in the mesoscopic scale of reinforced concrete structures. 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13), Seoul National University, 2019 -
Leichsenring, F; Fuchs, A.; Graf. W.; Kaliske, M.:
Application of Recurrent Neural Networks in Structural Analysis of reinforced concrete structures considering polymorphic uncertainty. 90th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM),
TU Wien, 2019 -
Kremer, K.; Edler, P.; Miska, N.; Leichsenring, F.; Balzani, D.; Freitag, S.; Graf, W.; Kaliske, M.; Meschke, G.:
Modeling of structures with polymorphic uncertainties at different length scales.
Surveys for Applied Mathematics and Mechanics (GAMM-Mitteilungen), 2019, Link - Graf, W.; Richter, J.; Götz, M.; Kaliske, M.:
Numerical analysis of reinforced concrete based on multiscalemodelling and polymorphic uncertain data. In: Beck, A.T. (ed.),3thInternational Conference on Vulnerability andRisk Analysis and Management (ICVRAM) & 7thInternational Symposium on Uncertainty Modelling and Analysis (ISUMA), Florianopolis & University of São Paulo, 2018 - Leichsenring, F.; Jenkel, C.; Graf, W.; Kaliske, M.:
Numerical simulation of wooden structures with polymorphicuncertainty in material parameters, special issue onComputing with polymorphic uncertain data (REC 2016),International Journal of Reliability and Safety,Vol. 12 (2018) No. 1/2, pp. 24-45DOI: 10.1504/IJRS.2018.10013787 - Graf, W.; Götz, M.; Kaliske, M.:
Computational framework for design of structures with polymorphic uncertaindata. In: Bucher, C. et al. (eds.), Proceedings 12th ICOSSAR, pp. 1163-1173, TU Vienna, 2017 - Götz, M.; Serafinska, A.; Leichsenring, F.; Graf, W.; Kaliske, M.:
Multi objective optimization considering poly-morphic uncertainty. In:2ndInternational Conference Uncertainty Quantification in Computational Sciences andEngineering (UNCECOMP), abstract (online available), Rhodos, 2017