New Publication in Machine Learning: Engineering

I am pleased to share our new publication in Machine Learning: Engineering:

"Unsupervised Structural Health Monitoring Using Physics-Based Vibration Features Derived from an Instrumented Bridge Mockup"

In this work, we investigate an unsupervised structural health monitoring framework using physics-based vibration features obtained from experimental testing of an instrumented bridge mockup in the SEHM Lab at LMU. The study combines vibration-based damage-sensitive features with unsupervised learning to identify structural changes without requiring labeled damage data.

I am especially pleased that LMU students Preston Guinto and Wayne Mutuku, who worked on this project through the SOAR and SURP summer research programs, are co-authors on the paper.

The paper is published in Machine Learning: Engineering and is available through the link below:

View the paper