Machine Learning for Wireless Structural Health Monitoring

Name (Acronym)

Machine Learning for Wireless Structural Health Monitoring (MLSHM)
ECTS

6
Contact E-mail

marco.salucci@unitn.it
Teacher

SALUCCI Marco
Syllabus

ESSE3 Page
Institution

DICAM, University of Trento
Study Program

M.S. Ingegneria Civile [LM CIV]
Degree

Master
Language

English
Tracks

Artificial Intelligence, Structural Health Monitoring

Master the fundamentals of machine learning algorithms for structural health monitoring using wireless technologies

The course provides fundamental knowledge about Machine Learning (ML) and its application to wireless Structural Health Monitoring (SHM). While being based on rigorous theoretical content, the course is oriented towards the most relevant applications for civil engineers. To complete the didactic offer, various numerical exercises (exploiting SW programs) will follow the theoretical lessons.

Course Content

PART 1: The "Three‐Steps Learning‐by‐Examples (LBE)" Framework

Overview, general concepts, and taxonomy of Machine Learning (ML) methodologies
Interpolation techniques
Dimensionality reduction methodologies
Space exploration/sampling methodologies
Classification and regression methodologies

PART 2: Applications to Wireless Structural Health Monitoring (SHM)

Overview of ML methodologies applied to wireless SHM
Model‐driven approaches vs. data‐driven approaches
Damage definition and identification
Data acquisition and processing
Damage prognosis
Modes/technologies for wireless SHM and their integration with ML

Further Readings

J.‐A. Goulet, Probabilistic Machine Learning for Civil Engineers. Cambridge, MA: The MIT Press, 2020.
A. Cury, Structural Health Monitoring Based on Data Science Techniques. Cham, Switzerland: Springer, 2022.
A. I. J. Forrester, A. Sobester, and A. J. Keane, Engineering Design via Surrogate Modelling: A Practical Guide. Hoboken, N.J.: John Wiley & Sons, 2008.
T. Hastie, J. Friedman, and R. Tisbshirani, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, 2017.
V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 2000.