Research in Engineering Risk Analysis
We perform research in the broad area of optimized decision-making in engineering systems under uncertainty. In particular, we work on reliability analysis, predictive modeling and sensitivity analysis, risk assessment and management methods.
Key research areas of the group include:
- Reliability analysis methods
- Risk assessment
- Bayesian predictive modeling
- Decision analysis
- Predictive maintenance & value of information
- Natural hazards
- Reliability of structures
- Reliability of sensing systems
Ongoing research projects
- BIG-ROHU: Development of a Health and Usage Monitoring System using Big Data Analyses, 2024-2026, funded by the Federal Ministry for Econonmic Affairs and Climate Action
- INFRA.RELEARN: Intelligent Infrastructure Maintenance with Deep Reinforcement Learning, 2022-2026, funded by the Georg Nemetschek Institute Artificial Intelligence for the Built World
- Bayesian Multilevel Uncertainty Quantification for Enhanced Reliability Assessment and Decision Support, 2020-2023, funded by DFG
- Cost-benefit analysis for flood polders at the Danube river. 2020-2022, funded by LfU
- Decision support with Structural Health Monitoring. 2019-2022, funded by the Institute for Advanced Study, TUM
- Validation of sensor systems for automated vehicles. 2019-2022, funded by AUDI
- Uncertainty Quantification in Dynamical Systems using Monte Carlo Methods. 2019-2023, funded by the AvH Foundation
- RIESGOS - Multirisk analysis and information system components for the Andes region. 2017-2024, with DLR, GfZ Potsdam and others, funded by BMBF
- Grusibau 2.0 - Structural codes for the 21st century. 2017 - 2022, funded by DIBt
- Reliability of hydraulic structures considering spatial variability. 2017-2021, funded by BAW
- Assessment and optimization of monitoring and inspection in ageing structures. 2016-2019, funded by DFG