Structural Vibration Monitoring with Diffractive Optical Processors

TL;DR

This paper introduces a low-power, cost-effective diffractive optical system combined with neural networks for real-time 3D structural vibration monitoring, significantly improving accuracy over traditional methods.

Contribution

It presents a novel integrated optical-neural system that encodes 3D displacements into light and decodes them with minimal sensors, enabling scalable and efficient structural health monitoring.

Findings

Achieved over an order-of-magnitude accuracy improvement

Demonstrated effectiveness with millimeter-wave on a lab-scale model

Established a new computational sensing modality for various applications

Abstract

Structural Health Monitoring (SHM) is vital for maintaining the safety and longevity of civil infrastructure, yet current solutions remain constrained by cost, power consumption, scalability, and the complexity of data processing. Here, we present a diffractive vibration monitoring system, integrating a jointly optimized diffractive layer with a shallow neural network-based backend to remotely extract 3D structural vibration spectra, offering a low-power, cost-effective and scalable solution. This architecture eliminates the need for dense sensor arrays or extensive data acquisition; instead, it uses a spatially-optimized passive diffractive layer that encodes 3D structural displacements into modulated light, captured by a minimal number of detectors and decoded in real-time by shallow and low-power neural networks to reconstruct the 3D displacement spectra of structures. The diffractive system's efficacy was demonstrated both numerically and experimentally using millimeter-wave illumination on a laboratory-scale building model with a programmable shake table. Our system achieves more than an order-of-magnitude improvement in accuracy over conventional optics or separately trained modules, establishing a foundation for high-throughput 3D monitoring of structures. Beyond SHM, the 3D vibration monitoring capabilities of this cost-effective and data-efficient framework establish a new computational sensing modality with potential applications in disaster resilience, aerospace diagnostics, and autonomous navigation, where energy efficiency, low latency, and high-throughput are critical.