🎓 Register now to the 2026 edition!
Title: Machine Learning for Wireless Structural Health Monitoring (MLSHM)
Date: 23 February ‐ 05 June 2026 (II Semester)
Teachers: SALAS SANCHEZ Aaron Angel, SALUCCI Marco
Website: https://www.eledia.org/eledia-unitn/course/2025-2026.LM.MLSHM.UniTN.TRENTO.IT
The Master Course is taught in 🇬🇧️ ENGLISH and is offered
- On-site
- On-line (synchronous and asynchronous)
Video recordings, hand-outs, etc. of the lectures will be available off-line!
Understand the strengths and limitations of both model-driven and data-driven approaches — and learn how to combine them for smarter, more reliable structural health monitoring.
What you will learn:
- Core principles and taxonomy of Machine Learning techniques
- The “Three-Steps Learning-by-Examples” (LBE) framework
- Interpolation, dimensionality reduction & sampling strategies
- Classification and regression methods for structural diagnostics
- Data-driven modeling and AI-based damage forecasting
Applications in in Wireless Structural Health Monitoring (SHM):
- Analytical models for structural behavior interpretation
- Machine Learning approaches powered by real-world sensor data
- Comparison and integration of physics-based and data-driven methodologies
💡 With case studies and lab simulations, you’ll build the skills to apply ML to real-world structural health monitoring challenges.
Teaching Activities:
- Theoretical Lessons
- e-Xam Self Assessment (each teaching class or periodically)
- MATLAB Hands-On
- e-Xam Final Assessment
Fees:
UniTN Students: FREE
EXTERNAL Students:
- 216€ : First course
- 180€ : Every course from the second one
The fees include the course teaching, slides/material, and video recordings.
Discover our didactic offer at:
https://www.eledia.org/eledia-unitn/course_degree/degree-master/
Questions? Reach us at: didattica@eledia.org
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