Simulation for Transportation and Operations – Institute for Risk and Reliability

Design and operation of infrastructure do not only target at providing and maintaining a reliable service over a desired timeframe, but they also involve an optimal allocation and utilisation of resources for this purpose. Typically, optimal solutions are approached through complex multi-criteria decision-making processes. However, the derivation of rigorous solutions requires a realistic and efficient modelling and simulation not only of the infrastructure’s performance but also of its interaction with its environment and operators including all uncertainties and risks. A particular challenge for optimal resource allocation and risk control is the cross disciplinary nature of the problem, which requires a quantitative connection of the engineering performance and reliability analysis with economic, social and perhaps even political aspects. Besides technical matters, human behaviour and even cultural aspects need to be considered. Comprehensive monitoring and control concepts are part of this challenge.

We develop numerical approaches for optimal design and operations, which aim at rigorous and efficient solutions based on a combination of engineering, mathematical and computer-science concepts. Our multi-disciplinary solutions include physics-based models in association with stochastic simulation and machine learning approaches to form efficient numerical technologies for multi-criteria optimization. We complement this approach by analysing accidents to identify and eliminate critical issues, in particular with focus on human factors in design and operations. Our developments serve as decision-support technology for application by engineers as well as by stakeholders, operators or authorities.

The fields of application cover engineering system and structural design optimization and optimal maintenance scheduling, reduction of development time & costs using computer experiments, robust and reliable product life-cycle management, decision-making under uncertainty, health monitoring, project management, optimal traffic routing and navigation, logistics optimization, process control, risk communication and more.

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

Selected References

Morais, C.; Moura, R.; Beer, M.; Patelli, E. (2020): Analysis and Estimation of Human Errors From Major Accident Investigation Reports, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6, 011014-1, 1–16.
DOI: 10.1115/1.4044796
Nieto-Cerezo, O.; Wenzelburger, J.; Patelli, E.; Beer, M. (2020): Optimal regulation of the construction of reliable sea defences, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6(2), Article, 04020023, 1–12.
Salomon, J.; Broggi, M.; Kruse, S.; Weber, S.; Beer, M. (2019): Resilience Decision-Making Method For Complex Systems, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6(2), Article 020901. | File |
DOI: 10.1115/1.4044907
Berthold, T.; Milbradt, P.; Berkhahn, V. (2018): Valid approximation of spatially distributed grain size distributions – A priori information encoded to a feedforward network, Computers & Geosciences, 113: 23–32.
DOI: 10.1016/j.cageo.2018.01.007
Zhong, S.; Pantelous, A.A.; Beer, M.; Zhou, J. (2018): Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms, Mechanical Systems and Signal Processing, 104: 347-369.
DOI: 10.1016/j.ymssp.2017.10.035
Attarzadeh, M.; Chua, D.; Beer, M.; Abbott, E.L.S. (2017): Options-based negotiation management of PPP-BOT infrastructure projects, Construction Management and Economics, 35(11–12), 676–692.
DOI: 10.1080/01446193.2017.1325962
de Angelis, M.; Patelli, E.; Beer, M. (2017): Forced Monte Carlo Simulation Strategy for the Design of Maintenance Plans with Multiple Inspections, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3(2), D4016001, 1–9.
DOI: 10.1061/AJRUA6.0000868
Moura, R.; Beer, M.; Patelli, E.; Lewis, J. (2017): Learning from major accidents: Graphical representation and analysis of multi-attribute events to enhance risk communication, Safety Science, Volume 99, Part A, Pages 58-70.
DOI: 10.1016/j.ssci.2017.03.005
Moura, R.; Beer, M.; Patelli, E.; Lewis, J., Knoll, F. (2017): Learning from accidents: interactions between human factors, technology and organisations as a central element to validate risk studies, Safety Science, 99, 196–214.
DOI: 10.1016/j.ssci.2017.05.001
Rinke, N.; von Gösseln, I.; Kochkine, V.; Schweitzer, J.; Berkhahn, V.; Berner, F.; Kutterer, H.; Neumann, I.; Schwieger, V. (2017): Simulating quality assurance and efficiency analysis between construction management and engineering geodesy, Automation in Construction, 76: 24-35.
DOI: 10.1016/j.autcon.2017.01.009
Mitseas, I. P.; Kougioumtzoglou, I. A.; Beer, M. (2016): An approximate stochastic dynamics approach for nonlinear structural system performance-based multi-objective optimum design, Structural Safety; 60: 67-76.
DOI: 10.1016/j.strusafe.2016.01.003
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
Schiermeyer, C.; Pascucci, F.; Rinke, N. (2016): A genetic algorithm approach for the calibration of a social force based model for shared spaces, Proceedings of Pedestrian and Evacuation Dynamics 2016, University of Science and Technology of China Press
Bode, M.; Berkhahn, V. (2014): Multiskalenansatz für reaktive Prozess- und Ablaufplanung, ASIM 2014, 22. Symposium Simulationstechnik.
Rinke, N.; Gösseln, I.; Berkhahn, V. (2012): High-level Petri nets for modeling of geodetic processes and their integration into construction processes, ECPPM 2012 - eWork and eBusiness in Architecture, Engineering and Construction, Reykjavik.
Berkhahn, V.; Berner, F.; Kuttner, H.; Schwieger, V.; Hirschner, J.; Rehr, I.; Rinke, N.; Schweitzer, J. (2010): Effizienzoptimierung und Qualitätssicherung ingenieurgeodätischer Prozesse im Hochbau, Bauingenieur; 85(11).