Li Xuyang, Bolandi Hamed, Masmoudi Mahdi, Salem Talal, Jha Ankush, Lajnef Nizar, Boddeti Vishnu Naresh
Michigan State University, East Lansing, MI, USA.
Nat Commun. 2024 Oct 25;15(1):9229. doi: 10.1038/s41467-024-52501-4.
Structural health monitoring 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 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, the model 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.
结构健康监测可确保建筑物和桥梁等结构的安全性和使用寿命。随着结构的体积和规模以及其失效影响的不断增加，迫切需要可扩展、低成本、无需人工干预即可被动运行且无需复杂基线模型即可针对每个机械结构进行定制的结构健康监测技术。我们提出了一种用于自动检测和定位结构损伤的新颖“部署即遗忘”方法。它是廉价传感器的完全被动测量、数据压缩和力学信息自动编码器的协同集成。一旦部署，该模型会持续学习并为每个结构适配定制的基线模型，从其未受损状态的响应特征中学习。仅从3小时的数据中学习后，它就能自主检测和定位不同类型的意外损伤。数值模拟和实验结果表明，将力学特性纳入自动编码器可使微小损伤的检测和定位比标准自动编码器提高多达35%。我们的方法在减少人工干预和检查成本的同时，有望实现主动和预防性维护策略，这将延长土木基础设施的使用寿命、可靠性和可持续性。
