  Structural health monitoring (SHM) ensures 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, can operate passively without human
intervention, and are customized for each mechanical structure without the need
for complex baseline models. We present MIDAS, a novel "deploy-and-forget"
approach for automated detection and localization of damage in structures. It
is a synergistic integration of entirely passive measurements from inexpensive
sensors, data compression, and a mechanics-informed autoencoder. Once deployed,
MIDAS 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
autoencoder allows for up to a 35% improvement in the detection and
localization of minor damage over a standard autoencoder. Our approach holds
significant promise for reducing human intervention and inspection costs while
enabling proactive and preventive maintenance strategies. This will extend the
lifespan, reliability, and sustainability of civil infrastructures.
