Design and interpretation of experiments: Signal Processing, Design of Experiments and System Identification - Einzelansicht

Students will be familiar with following: Design and setup as well as evaluation and interpretation of experimental testing in structural engineering. Provision of techniques linking experimental and mathematical / numerical modelling. Parallel assessment of steps being part of any verification and validation procedure. Discussion of common techniques of optimal experimental designs

Internal lecture notes Farrar, Worden: Structural Health Monitoring: A Machine Learning Perspective, WILEY

Darius Ucinsky: Optimal Measurement Methods for Distributed Parameter System Identification, Taylor and Francis Further Textbooks (to be announced)

The course gives an overview on experiments and their evaluation regarding different tasks and scopes of structural engineering. Next to different testing techniques applied for diverse aims, the equipment and measuring devices employed for testing are treated as well.

Besides the experiment itself, it is an important question, how we can use the experimental data for the calibration and validation of models in engineering. In this course, we give insights to techniques called parameter and system identification.

As often signals are not useable directly, transforms are necessary, like filtering, Fourier Transform, Wavelet Transform and, in particular for signals with noise, averaging techniques. Having models at hand, the experiment can be designed virtually by means of nonlinear optimization.

1 written exam / 120 min / WiSe + SuSe including

“Experiments in Structural Engineering” and

“Signal Processing, Design of Experiments and System Identification”