MS03 Imprecise Probabilistic Approaches for Structural Safety and Risk Analysis

Prof.Michael Beer: beer@irz.uni-hannover.de

Session Chairs:
Dr.Matthias Faes, KU Leuven, matthias.faes@kuleuven.be
Prof. Dr.-Ing.Michael Beer, Leibniz Universität Hannover – Institute for Risk and Reliability, beer@irz.uni-hannover.de
Prof. Dr.Hao Zhang, University of Sydney, hao.zhang@sydney.edu.au
Prof. Dr.Edoardo Patelli, University of Strathclyde Glasgow, edoardo.patelli@strath.ac.uk
Prof. Dr.Pengfei Wei, Northwestern Polytechnical University,  pengfeiwei@nwpu.edu.cn

Abstract of the special session:
Computational models have played a crucial role in the transition of a traditional experiment-centered engineering practice towards a virtual design context where the performance of designed component is assessed long before the first prototype is built. However, in most realistic engineering cases, the designer is faced with a multitude of sources of uncertainty on both the actual model form (i.e., the equations that have to be solved) as on the physical quantities that are used to parametrize these models. Such uncertainty stems either from the apparently pure random nature of some physical quantities, incomplete knowledge on the actual value of these quantities, or a combination of both. When uncertainty stemming from incomplete knowledge is involved in the design process, imprecise probabilistic approaches are gaining momentum for the assessment of the (bounds on the) reliability and safety of designed structures and components, and the quantification of the underlying model response uncertainty, as they allow for a more objective assessment.
This special session is aimed at gathering contributions that discuss new theoretical developments and advanced applications of imprecise probabilistic approaches towards uncertainty quantification and structural safety and risk analysis. More specifically, papers discussing theoretical developments in the modelling of, and simulation with imprecise probabilistic representations of uncertainty such as intervals, fuzzy sets, set-valued approaches, interval probabilities, credal sets, Dempster-Shafer belief functions or imprecise stochastic processes, as well as efficient numerical or (semi-)analytical propagation schemes for forward and inverse analysis with such uncertainty models are of particular interest for this special session. In view of assisting the translation of these novel methods towards practical application, also papers that apply these methods to challenging and realistic engineering cases in general are welcomed, especially if the non-probabilistic or hybrid models for the uncertainty are based on real data.