Machine Learning for Wireless Structural Health Monitoring

ABSTRACT

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

TEACHING ACTIVITIES

• Theoretical Lessons
• e-Xam Self Assessment (each teaching class or periodically)
• MATLAB Hands-On
• e-Xam Final Assessment

FURTHER READINGS

1. J.‐A. Goulet, Probabilistic Machine Learning for Civil Engineers. Cambridge, MA: The MIT Press, 2020.
2. A. Cury, Structural Health Monitoring Based on Data Science Techniques. Cham, Switzerland: Springer, 2022.
3. 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.
4. T. Hastie, J. Friedman, and R. Tisbshirani, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, 2017.
5. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 2000.