Mechanical in-sensor computing: a programmable meta-sensor for structural damage classification without external electronic power

2025-05-24

AI Summary

Conventional structural health monitoring (SHM) systems rely on wired sensors and electronic computation, suffering from high energy consumption, low throughput, and poor adaptability to resource-constrained environments. Method: This work proposes a passive, programmable locally resonant metamaterial plate (LRMP) sensor that enables fully mechanical, on-chip vibration sensing and binary damage classification—without electronics. Leveraging bandgap-based physical filtering and inverse geometric optimization, the LRMP is tailored to cover the fundamental engineering structural frequency range of 9.54–81.86 Hz, enabling in situ, real-time damage detection. Contribution/Results: The system operates without external power, eliminating electronic data transmission and post-processing, achieving near-zero power consumption. Experimental validation confirms the feasibility of passive physical computing for SHM, establishing a new paradigm for ultra-low-power, high-robustness intelligent infrastructure monitoring.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Structural health monitoring (SHM) involves sensor deployment, data acquisition, and data interpretation, commonly implemented via a tedious wired system. The information processing in current practice majorly depends on electronic computers, albeit with universal applications, delivering challenges such as high energy consumption and low throughput due to the nature of digital units. In recent years, there has been a renaissance interest in shifting computations from electronic computing units to the use of real physical systems, a concept known as physical computation. This approach provides the possibility of thinking out of the box for SHM, seamlessly integrating sensing and computing into a pure-physical entity, without relying on external electronic power supplies, thereby properly coping with resource-restricted scenarios. The latest advances of metamaterials (MM) hold great promise for this proactive idea. In this paper, we introduce a programmable metamaterial-based sensor (termed as MM-sensor) for physically processing structural vibration information to perform specific SHM tasks, such as structural damage warning (binary classification) in this initiation, without the need for further information processing or resource-consuming, that is, the data collection and analysis are completed in-situ at the sensor level. We adopt the configuration of a locally resonant metamaterial plate (LRMP) to achieve the first fabrication of the MM-sensor. We take advantage of the bandgap properties of LRMP to physically differentiate the dynamic behavior of structures before and after damage. By inversely designing the geometric parameters, our current approach allows for adjustments to the bandgap features. This is effective for engineering systems with a first natural frequency ranging from 9.54 Hz to 81.86 Hz.

Problem

• Develops a programmable metamaterial sensor for structural damage classification
• Eliminates need for external electronic power in structural health monitoring
• Uses physical computation to process vibration data in-situ

Innovation

• Programmable metamaterial sensor for damage classification
• Physical computation without external electronic power
• Locally resonant metamaterial plate for in-situ analysis