Mechanics-Informed Autoencoder Enables Automated Detection and
  Localization of Unforeseen Structural Damage
Xuyang Li Hamed Bolandi Mahdi Masmoudi Talal Salem Nizar Lajnef Vishnu Naresh Boddeti
Structural health monitoring (SHM) is vital for ensuring the safety and
longevity of structures like buildings and bridges. As the volume and scale of
structures and the impact of their failure continue to grow, there is a dire
need for SHM techniques that are scalable, inexpensive, operate passively
without human intervention, and customized for each mechanical structure
without the need for complex baseline models. We present a novel
"deploy-and-forget" approach for automated detection and localization of
damages in structures. It is based on a synergistic combination of fully
passive measurements from inexpensive sensors and a mechanics-informed
autoencoder. Once deployed, our solution continuously learns and adapts a
bespoke baseline model for each structure, learning from its undamaged state's
response characteristics. After learning from just 3 hours of data, it can
autonomously detect and localize different types of unforeseen damage. Results
from numerical simulations and experiments indicate that incorporating the
mechanical characteristics into the variational autoencoder allows for up to
35\% earlier detection and localization of damage over a standard autoencoder.
Our approach holds substantial promise for a significant reduction in human
intervention and inspection costs and enables proactive and preventive
maintenance strategies, thus extending the lifespan, reliability, and
sustainability of civil infrastructures.