Abstract
Currently, many countries are investing significantly in the maintenance of civil infrastructures, viewing them as valuable assets at the national and international levels. Based on the recorded dynamic response, Structural Health Monitoring (SHM) is in a position to ensure the performance and safety of such structures. SHM is currently focusing on the usage of Wireless Sensor Networks (WSN) for damage detection because it has proven to be the best substitute for traditional visual inspection and wired/tethered sensor networks. In SHM, live tracking the structural response anytime anywhere is a predominant challenge. This work proposed and implemented an end-to-end live structural health monitoring framework based on the Internet of Things (IoT) to prevent the structure from collapsing unexpectedly. Furthermore, IoT assists in the live monitoring of structures at any time and from any location. Accelerometer sensors (40), signal conditioners (40), Data Acquisition Cards with an integrated Wi-Fi/Ethernet module (10), Access Point (1), and other components are included in the proposed framework. The accelerometer sensors are utilized to record the structural response, which is then analyzed by proposed Artificial Intelligence (AI) methods to identify the incipient damage and localize the damage. The AI methods are established using two datasets: one from a three-story building frame designed and realized in our laboratory, and the other from Los Alamos laboratory’s three-story bookshelf structure. It is a unique framework that offers three user interfaces (standalone, web-based, and mobile-based) to enable live monitoring locally and globally. The results demonstrate very good damage identification and localization accuracy, as well as the ability to live-monitor via interfaces.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Kumar, P., Kota, S.R. IoT enabled diagnosis and prognosis framework for structural health monitoring. J Ambient Intell Human Comput 14, 11301–11318 (2023). https://doi.org/10.1007/s12652-023-04646-1
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DOI: https://doi.org/10.1007/s12652-023-04646-1