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 three-dimensional (3D) structural vibration spectra, offering a low-power, cost-effective, and scalable solution. Unlike prior diffractive processors designed primarily for static image classification or reconstruction tasks, this framework establishes a dynamic computational sensing modality where the optical front-end is co-optimized to encode time-varying mechanical vibrations into distinct spatiotemporal optical patterns. 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 distinct computational sensing modality with potential applications in disaster resilience, aerospace diagnostics, and autonomous navigation—where energy efficiency, low latency, and high-throughput are critical.
Wang et al. (Wed,) studied this question.