Chair of Structural Analysis
We research and teach how civil structures deform, support and react - from everyday structures to modern, intelligent systems. The Chair of Structural Analysis combines theoretical principles, numerical simulations, experiments and data-based methods to open up new paths for the construction of the future.
Research profile
Data-driven modeling and identification
Our research focuses on the development of data-driven methods to represent the load-bearing behavior of structures more precisely and efficiently. This makes it possible to significantly accelerate particularly dynamic analyses and to reduce material consumption and workload.
The developed data-driven models are real-time capable and are applied in adaptive vibration control and structural monitoring. Using sensor data, they can be continuously updated to reflect the current condition of the structure, enabling more accurate vibration control and assessment.
Another field of application is real-time hybrid simulation, in which experiments are coupled with computational simulations to conduct dynamic tests on structural components.
We are also developing data-driven approaches for material modeling and parameter identification to gain new insights into nonlinear material behavior.
Main research areas
- Dynamic structural analysis
- Vibration control and structural monitoring
- Real-time hybrid simulations
- Material modeling
Publications
A list of the most recent publications
A novel boundary-based machine learning approach for 2D crack analysis in elastic and piezoelectric materials
A novel boundary-based machine learning approach for 2D crack analysis in elastic and piezoelectric materials
Semi-active omnidirectional liquid column vibration absorber with rapid frequency adjustment capability
Semi-active omnidirectional liquid column vibration absorber with rapid frequency adjustment capability
Feedforward neural network-assisted parameter identification and tuning for uniaxial superelastic shape memory alloy models under dynamic loads
Feedforward neural network-assisted parameter identification and tuning for uniaxial superelastic shape memory alloy models under dynamic loads
Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices
Multiscale fluid–structure coupled real-time hybrid simulation of monopile wind turbines with vibration control devices
