Leistungsfähigkeit und Resilienz komplexer Systeme

Performance, Risk and Resilience of Complex Systems and Networks

© Jan-Philipp Thiele (CC0)

We develop highly efficient and generally applicable approaches and numerical techniques to analyse individual and coupled engineering systems and networks representing infrastructure in the wider sense. This includes the simulation of dynamic processes on systems and networks to examine their time-dependent behaviour and reliability capturing deterioration and dynamic demand changes, as well. As a specific challenge in the analysis, seemingly minor issues and events can lead to catastrophic risks and dramatic consequences through cascading failures (low-probability-high-consequence events).

Our approaches of generalized uncertainty quantification support the identification of those critical issues and mechanisms and of vulnerabilities in a targeted and efficient manner and as a basis for risk reduction and mitigation. Considering the interconnection between different systems and networks we aim at calculating failure consequences and, hence, risk in a structured quantitative manner as an intrinsic feature of the analysis. Simulating processes on systems and networks supports not only the optimal specification of maintenance plans but also the assessment and implementation resilience and the identification of optimal recovery strategies.

In order to achieve a comprehensive and realistic analysis, we do not only utilize traditional models and analysis techniques but we also resort on Bayesian models (Bayesian networks) and imprecise probabilities to quantify and integrate expert knowledge (even if conflicting) as well as vague and imprecise information. For analyzing large systems and networks we develop highly efficient approaches and analysis techniques by combining the concept of survival signature with advanced stochastic simulation techniques. This enables us, as well, to consider arbitrary distributional lifetime descriptions for the system components and any dependency characterization between failure events.

Application areas include but are not limited to performance and reliability assessment, lifecycle analysis and lifetime estimation, analysis of sensitivities and importance of components, optimal maintenance scheduling, risk reduction, risk control and mitigation, resilience assessment, resilient design and upgrade, and optimal recovery planning. Our approaches and techniques are generally applicable and be utilized for analyzing non-engineering systems and networks, as well.

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


  • Mi, J.H, Beer, M.; Li, Y.F.; Broggi, M.; Cheng, Y.H. (2020): Reliability and Importance Analysis of Uncertain System with Common Cause Failures Based on Survival SignatureReliability Engineering and System Safety, 201, Article 106988
  • Behrensdorf, J.; Broggi, M.; Beer, M. (2019): Reliability analysis of networks interconnected with copulasASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 5(4), Article 041006
    DOI: 10.1115/1.4044043
  • George-Williams, H.; Feng, G.; Coolen, F.P.A.; Beer, M.; Patelli, E. (2019): Extending the Survival Signature Paradigm to Complex Systems with Non-repairable Dependent FailuresProceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 233(4), 505–519
  • Salomon, J.; Broggi, M.; Kruse, S.; Weber, S.; Beer, M. (2019): Resilience Decision-Making Method For Complex SystemsASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(2), Article 020901 | Datei |
    DOI: 10.1115/1.4044907
  • Tolo, S.; Patelli, E.; Beer, M. (2018): An open toolbox for the reduction, inference computation and sensitivity analysis of Credal NetworksAdvances in Engineering Software, 115, 126–148
    DOI: 10.1016/j.advengsoft.2017.09.003
  • Zhang, D.M.; Du, F.; Huang, H.W.; Zhang, F.; Ayyub, B.M.; Beer, M. (2018): Resiliency Assessment of Urban Rail Transit Networks: Shanghai Metro as an ExampleSafety Science, 106: 230-243
  • Zuev, K.M.; Beer, M. (2018): Reliability of Critical Infrastructure Networks: ChallengesIn: Beer, M.; Huang, H.W.; Ayyub, B.M.; Zhang, D.M.; Phillips, B.M. (eds.), Resilience Engineering for Urban Tunnels, American Society of Civil Engineers, Infrastructure Resilience Publications (IRP) IRP 2, 71-82
    DOI: 10.1061/9780784415139.ch06
  • Tolo, S.; Patelli, E.; Beer, M. (2017): Risk Assessment of Spent Nuclear Fuel Facilities Considering Climate ChangeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3(2), G4016003, 1–15
    DOI: 10.1061/AJRUA6.0000874
  • Tolo, S.; Patelli, E.; Beer, M. (2017): Robust vulnerability analysis of nuclear facilities subject to external hazardsStochastic Environmental Research and Risk Assessment 31 (10), 2733–2756
  • Feng, G.; Patelli, E.; Beer, M.; Coolen, F. P. (2016): Imprecise system reliability and component importance based on survival signatureReliability Engineering & System Safety; 150: 116—125
    DOI: 10.1016/j.ress.2016.01.019
  • Rocchetta, R.; Patelli, E.; Broggi, M.; Schewe, S. (2016): Robust probabilistic risk/safety analysis of complex systems and critical infrastructuresReliability Engineering and System Safety, 136, 47-61
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