Adaptive edge intelligence for rapid structural condition assessment using a wireless smart sensor network

Combining artificial intelligence and edge computing, edge intelligence is a promising computing paradigm for the Internet-of-Things-based Structural Health Monitoring (SHM), showing great potential to improve system responsiveness by reducing communication latency. Previously, very limited studies proposed, optimized, or verified edge intelligence approaches for SHM applications, where the overhead and efficiency of algorithms to manage limited onboard resources are the main gaps. In this study, an adaptive edge intelligence strategy is proposed to facilitate autonomous structural condition assessment, involving reference-free displacement estimation algorithm, Gaussian Process Regression, and stochastic process control. To facilitate algorithm deployment, both effective single-node independent computing and multi-node coordination are explored to deal with the limited onboard resources, utilizing the computing capacity of each node to speed up computation. Using the Xnode, a MEMS-based wireless sensor platform, lab tests and full-scale applications in railroad bridge monitoring were conducted to verify the proposed strategy, demonstrating the potential and suitability of the developed approach for rapid adaptive structural condition assessment in SHM practice.