Integration of reliability and sensitivity assessment with data assimilation for improved decision support
The management of engineering structures and systems requires adequate predictions of their performance throughout their intended service life. For most applications, effective numerical models for such predictions exist, but the parameters of these models are commonly uncertain or random. This is particularly relevant when the interest is in the reliability of these systems, because rare events are most affected by uncertainty. Because the uncertainty arises from different sources, it has been recognized that it is not always sufficient to provide an estimate of reliability as a single number, in particular if the underlying calculation is based on vague information. In many instances, it can be desirable to separate the influence of different sources of uncertainty on the final prediction. Such a polymorphic treatment of uncertainties leads to computational challenges in the underlying structural reliability analysis, which will be addressed in this project.
Reliability analysis and UQ for improved decision support under consideration of polymorphic uncertainty
The goal of this project is to develop an integral framework to adaptively estimate the structural reliability and its sensitivity to changes in design parameters under varying information. This work is based on a recently proposed method for reliability analysis using Sequential Importance Sampling, as well as new approach for Bayesian analysis of rare events. The latter is of interest in many – if not most – engineering applications, where data assimilation (e.g. by monitoring and collection of field data) is an effective way of reducing uncertainty and increasing reliability. The framework shall be combined with lower-dimensional representations of uncertain quantities and adaptive proxy models of the potentially complex engineering model for enhancing the computational efficiency of the estimation.
The framework will enable the investigation of effective ways for representing reliability and sensitivities for engineering decision support in view of polymorphic uncertainties.
M. Sc. Max Ehre
M. Sc. Max Ehre
Technische Universität München (TUM)
Engineering Risk Analysis Group
- Sebastian Geyer, Iason Papaioannou, Daniel Straub (2019). Cross entropy-based importance sampling using Gaussian densities revisited. Structural Safety, 76, 15-27
- Max Ehre, Iason Papaioannou, Daniel Straub (2018). Efficient estimation of variance-based reliability sensitivities in the presence of multi-uncertainty. Proc. 19th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2018). Zürich, Switzerland.
- Iason Papaioannou, Max Ehre, Daniel Straub (2018). Efficient PCE representation for reliability analysis in high dimensions. Proc. 19th working conference of the IFIP Working Group 7.5 on Reliability and Optimization of Structural Systems (IFIP 2018). Zürich, Switzerland.
- Max Ehre, Iason Papaioannou, Daniel Straub (2018). Efficient Conditional Reliability Updating with Sequential Importance Sampling. Proceedings in Applied Mathematics and Mechanics - 89th GAMM Annual Meeting - 2018.