This page gives an overview of recent research by PhD students affiliated with ESRA members.
Congratulations to all!!
Salvatore F. Greco, Eidgenössische Technische Hochschule Zürich (ETH Zürich) & Paul Scherrer Institute (Villigen PSI), Switzerland
Bayesian integration of simulator data and judgment to develop empirically-based reference values for human reliability (2021)
Supervisors: Prof. Dr. Horst-Michael Prasser (ETH Zürich, Switzerland), Prof. Dr. Marloes H. Maathuis (ETH Zürich, Switzerland), Dr. Luca Podofillini (Paul Scherrer Institute, Switzerland)
Keywords: Human Reliability Analysis, control room simulator, data variability, crew behavior models, Bayesian population variability models
The collection of human performance data from plant simulators is receiving renewed attention by the Human Reliability Analysis (HRA) community to improve the empirical basis of our models. However, gaps remain on how to incorporate the data into HRA models.
More information on Salvo's work
The present Ph.D. work aims at developing new quantitative models, based on Bayesian statistical methods, traceably integrating simulator data and expert judgment in the production of human failure probability values and bounds.
References and links :
- Greco SF, Podofillini L, and Dang VN. A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis. Reliab Eng Syst Safe 2021, 206:107309, ISSN 0951-8320. https://www.sciencedirect.com/science/article/pii/S095183202030805X
- Greco SF, Podofillini L, and Dang VN. Crew performance variability in human error probability quantification: a methodology based on behavioral patterns from simulator data. Proc I Mech E Part O: J Risk and Reliability 2021, doi:10.1177/1748006X20986743. https://journals.sagepub.com/doi/abs/10.1177/1748006X20986743
Jingwen Song, Gottfried Wilhelm Leibniz Universität Hannover, Germany
Stochastic Simulation Methods for Structural Reliability under Mixed Uncertainties (2020)
Supervisors: Prof. Dr. Michael Beer (Leibniz Universität Hannover, Germany)
Keywords: Uncertainty quantification; Imprecise probabilities; Line sampling; Active Learning; Gaussian process regression; Bayes rule; Dimension reduction
Uncertainty quantification (UQ) has been widely recognized as an important and challenging task in structural engineering. This thesis contributes three developments concerning efficient numerical propagation of mixed uncertainties, including aleatory and epistemic uncertainties. First, a generalized Non-intrusive Imprecise Stochastic Simulation (NISS) method is proposed to successfully solve the NASA Langley UQ challenge. Second, the classical line sampling is injected into the NISS framework to substantially improve the efficiency of rare event analysis. Third, an active learning strategy is embedded into line sampling procedure to tackle highly nonlinear problems. The effectiveness of those developments is clearly interpreted with real-world test examples.
More information on Jingwen's work
References and links :
- Song, J., Wei, P., Valdebenito, M., & Beer, M. (2021). Active learning line sampling for rare event analysis. Mechanical Systems and Signal Processing, 147, 107113. https://doi.org/10.1016/j.ymssp.2020.107113
- Song, J., Wei, P., Valdebenito, M., Beer, M. (2020). Adaptive reliability analysis for rare events evaluation with global imprecise line sampling. Computer Methods in Applied Mechanics and Engineering, 372, 113344. https://doi.org/10.1016/j.cma.2020.113344
- Song, J., Valdebenito, M., Wei, P., Beer, M., & Lu, Z. (2020). Non-intrusive imprecise stochastic simulation by line sampling. Structural Safety, 84, 101936. https://doi.org/10.1016/j.strusafe.2020.101936
- Song, J., Wei, P., Valdebenito, M., Bi, S., Broggi, M., Beer, M., & Lei, Z. (2019). Generalization of non-intrusive imprecise stochastic simulation for mixed uncertain variables. Mechanical Systems and Signal Processing, 134, 106316. https://doi.org/10.1016/j.ymssp.2019.106316
- Wei, P., Song, J., Bi, S., Broggi, M., Beer, M., Lu, Z., & Yue, Z. (2019). Non-intrusive stochastic analysis with parameterized imprecise probability models: I. Performance estimation. Mechanical Systems and Signal Processing, 124, 349-368. https://doi.org/10.1016/j.ymssp.2019.01.058
- 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. https://doi.org/10.1016/j.ymssp.2019.02.015
Google Scholar: https://scholar.google.com/citations?user=fFF8_EkAAAAJ&hl=zh-CN&oi=ao
By Caroline A. Metcalfe (Johnson), University of Stavanger (UiS), Norway, 2020
Contributions to improved risk and vulnerability assessment of critical infrastructure (2020)
Supervisors: Prof. Dr. Roger Flage (UiS, Norway), Prof. Dr. Seth D. Guikema (University of Michigan, USA)
Keywords: Critical infrastructure, network models, interdependent systems, vulnerability assessment, infrastructure PRA
The present PhD work aims to provide contributions to the assessment of critical infrastructure risk. Firstly, by improving use of network models in vulnerability assessments of critical infrastructure. This included investigating:
- how to better model spatial disruptions
- characteristics significant in improving robustness of interdependent systems
- presenting an example of developing a real-world model of dependent infrastructure systems in a poor data setting.
Secondly, by exploring the feasibility of a full PRA for complex infrastructure systems and highlighting how more common methods can be improved to better estimate the results of a full PRA.
More information on Caroline's work
References and links:
- Johnson, C. A., Flage, R., & Guikema, S. D. Characterising the robustness of coupled power-law networks. Reliability Engineering & System Safety 2019, 191, 106560. https://www.sciencedirect.com/science/article/pii/S0951832018311803
- Johnson CA, Reilly AC, Flage R, Guikema SD. Characterizing the robustness of power-law networks that experience spatially-correlated failures. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability. 2021, 235(3):403-415. https://journals.sagepub.com/doi/abs/10.1177/1748006X20974476
- Stødle K, Metcalfe CA, Brunner LG, Saliani JN, Flage R, Guikema SD. Dependent infrastructure system modeling: A case study of the St. Kitts power and water distribution systems, Reliability Engineering & System Safety 2021, Volume 209, 107421, ISSN 0951-8320. https://doi.org/10.1016/j.ress.2020.107421
- Johnson CA, Flage R, Guikema SD. Feasibility study of PRA for critical infrastructure risk analysis, Reliability Engineering & System Safety 2021, Volume 212, 107643, ISSN 0951-8320. https://doi.org/10.1016/j.ress.2021.107643