Wireless structural health monitoring (SHM) represents an emerging paradigm in non-destructive testing evaluation, which is regularly performed within the framework of structural maintenance of critical infrastructure. While traditional cable-based SHM strategies have largely relied on centralized structural response data collection and processing, in wireless SHM the sensor nodes essentially operate as stand-alone processing units. Furthermore, the on-board processing capabilities of wireless sensor nodes have been widely exploited for decentralizing SHM tasks, thus avoiding power-consuming wireless transmission of entire structural response data sets. Evidently, on-board processing requires approaches tailored to the limited computational resources of wireless sensor nodes, particularly for computationally intensive, state-of-the-art strategies for SHM that rely on artificial intelligence (AI). Specifically, in model-based SHM/AI strategies, AI algorithms are trained by running the so-called what-if scenarios using numerical models of monitored structures. However, the use of numerical models in a decentralized wireless SHM scheme might be prohibitive from a computational resources point of view. To overcome this limitation, analytical modeling techniques using elastic waveguides are developed here that carry a low computational burden. These are specifically tailored for flexible structures of variable cross section, which comprise typical components of critical infrastructure such as masts, antennas and pylons, and can be used for simulating axial, torsional and flexural vibrations. The accuracy and efficiency of the proposed analytical modeling approach are then demonstrated through comparisons with conventional numerical models based on the finite element method.
