CivE 789F
Sensing Techniques & Data Analytics for Engineering Systems
Description
The course provides students with a full view of the monitoring and data analysis process, from designing a sensing plan to interpreting results for reliable decisions. Lectures and labs combine theory with practicalexamples in bridge health monitoring applications. The course is an important part of the program because it connects fundamental engineering knowledge in mathematics, statistics, and dynamics with modern tools for sensing and data-driven analysis. It prepares graduate students to apply advanced techniques to their own research and professional practice, and supports interdisciplinary learning across civil, mechanical, and
aerospace engineering. Python programming is used throughout the course to connect theory with practice, giving students direct experience with data handling, modeling, and algorithm development.
Learning Outcomes
1. Plan and justify sensing strategies for civil structures, including sensor types and placement.2. Clean, filter, and prepare time-series data for analysis.
3. Analyze data in the frequency domain using Fourier transforms, spectral analysis, and wavelets.
4. Design and evaluate damage-sensitive features, and perform statistical anomaly detection.
5. Formulate structural health monitoring tasks as optimization problems and apply appropriate solution methods.
6. Apply machine learning approaches such as clustering, and autoencoders for anomaly detection and data augmentation.
7. Develop parametric time-series models and apply Kalman filtering for state estimation.
| Lecture | Seminar | Lab | Credits | Total AU |
|---|---|---|---|---|
| 3 | 0/1 | 0/1 | 3 | 37.8 |
| M % | NS % | CS % | ES % | ED % |
|---|---|---|---|---|
None defined
None defined
Undergraduate Program(s)
Sections & Respective Instructors
| A - 2026/2027 - Fall - Mohamed Talebi Kalaleh |
| A - 2025/2026 - Fall - Mohamed Talebi Kalaleh |