Uncertainty Quantification and Risk Reduction in Vague and Changing Environments – Institute for Risk and Reliability

We pursue extensive research on dealing with rare and imprecise information in various types of engineering analyses. In practice, information comes in diverse forms, which include variability, imprecision, incompleteness, vagueness, ambiguity, indeterminacy, dubiety, subjective experience and expert knowledge, etc. Our methods and techniques help to quantify such critical input information properly and to process this information through engineering analyses in order to arrive at realistic results and reasonable decisions from engineering analyses. By means of comprehensive modelling of uncertainty and imprecision we can reveal new insight into engineering problems, for example, helping to identify robust optimal solutions and decisions.

Our methods and tailored solutions to address these problematic cases are not only based on probabilistic including Bayesian approaches but include set-theoretical approaches and combinations of probabilistic and set theoretical components in a hybrid manner. With the aid of intervals, fuzzy sets and imprecise probabilities we process uncertainty and imprecision in its natural form and avoid artificial model assumptions, which are often too narrow - a phenomenon known as expert overconfidence. Using set-valued descriptors we only limit the models to some domain. The results are then obtained in form of bounded quantities covering all plausible options in the light of the deficient information on input quantities and boundary conditions. This helps to avoid wrong decisions due to artificial restrictions in the modelling. We complement this approach by analysing accidents to identify critical issues, in particular with focus on human factors in design and operations.

Application areas include but are not limited to reliability assessment, analysis of model output sensitivities, model validation and verification, model updating, and risk reduction.

We provide tailored solutions for a variety of problems from this spectrum and beyond.

Selected References

Valdebenito, M.A.; Beer, M.; Jensen, H.A.; Chen, J.B.; Wei, P.F. (2020): Fuzzy Failure Probability Estimation Applying Intervening Variables, Structural Safety, Structural Safety, 83, Article 101909.
DOI: 10.1016/j.strusafe.2019.101909
Bi, S.F.; Broggi, M.; Wei, P.F.; Beer, M. (2019): The Bhattacharyya distance: enriching the P-box in stochastic sensitivity analysis, Mechanical Systems and Signal Processing, 129, pp. 265-281.
DOI: 10.1016/j.ymssp.2019.04.035
Faes, M.; Broggi, M.; Patelli, E.; Govers, Y.; Mottershead, J.; Beer, M.; Moens, D. (2019): A multivariate interval approach for inverse uncertainty quantification with limited experimental data, Mechanical Systems and Signal Processing, 118, 534–548.
DOI: 10.1016/j.ymssp.2018.08.050
Faes, M.; Sadeghi, J.; Broggi, M.; De Angelis, M.; Patelli, E.; Beer, M.; Moens, D. (2019): On the robust estimation of small failure probabilities for strong non-linear models, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 5, Article 041007.
DOI: 10.1115/1.4044044
Wei, P.; Song, J.; Bi, S.; Broggi, M.; Beer, M.; Lu, Z.; Yue, Z. (2019): Non-intrusive stochastic analysis with parameterized imprecise probability models: II. Reliability and rare events analysis, Mechanical Systems and Signal Processing, 126:227-247.
DOI: 10.1016/j.ymssp.2019.02.015
Wei, P.F.; Song, J.W.; Bi, S.F.; Broggi, M.; Beer, M.; Lu, Z.Z.; Yue, Z.F. (2019): Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimation, Mechanical Systems and Signal Processing, 124, 349-368.
DOI: 10.1016/j.ymssp.2019.01.058
Bi, S.F.; Broggi, M.; Beer, M. (2018): The role of the Bhattacharyya distance in stochastic model updating, Mechanical Systems and Signal Processing, 117: 437-452.
DOI: 10.1016/j.ymssp.2018.08.017
Wang, C.; Zhang, H.; Beer, M. (2018): Computing tight bounds of structural reliability under imprecise probabilistic information, Computers and Structures, 208, 92–104.
DOI: 10.1016/j.compstruc.2018.07.003
Zhang, Y.; Kim, C.-W.; Beer, M.; Dai, H.L.; Soares, C.G. (2018): Modeling multivariate ocean data using asymmetric copulas, Coastal Engineering, 135: 91-111.
DOI: 10.1016/j.coastaleng.2018.01.008
Comerford, L.; Jensen, H.A.; Mayorgab, F.; Beer, M.; Kougioumtzoglou, I.A. (2017): Compressive sensing with an adaptive wavelet basis for structural system response and reliability analysis under missing data, Computers and Structures; 182: 26-40.
DOI: 10.1016/j.compstruc.2016.11.012
Moura, R.; Beer, M.; Patelli, E.; Lewis, J.; Knoll, F. (2016): Learning from major accidents to improve system design, Safety Science; 84: 37-45.
DOI: 10.1016/j.ssci.2015.11.022
Patelli, E.; Alvarez, D. A.; Broggi, M.; de Angelis, M. (2015): Uncertainty Management in Multidisciplinary Design of Critical Safety Systems, Journal of Aerospace Information Systems; 12(1): 140-169.
Beer, M.; Ferson, S.; Kreinovich, V. (2013): Imprecise probabilities in engineering analyses, Mechanical Systems and Signal Processing; 37(1-2): 4—29.
Beer, M.; Zhang, Y.; Quek, S. T.; Phoon, K. K. (2013): Reliability analysis with scarce information: Comparing alternative approaches in a geotechnical engineering context, Structural Safety; 41: 1—10.
DOI: 10.1016/j.strusafe.2012.10.003