Computational engine for finite element digital twins of structural dynamics via motion data
Youqi Zhang, Rui Hao, Jarkko Niiranen, Yaohua Yang, Eugen Brühwiler, Di Su, Tomonori Nagayama
Abstract
Typical structural health monitoring systems employ limited numbers of sensors capable of measuring discrete local behaviours. However, practical challenges arise as these sensor arrays cannot cover all local areas of interest. To address this challenge, this article introduces a novel method for twinning structural dynamic behaviour by constructing a finite-element-model-based digital twin, enabling the observation of non-sensor positions crucial for downstream tasks. The approach utilises streaming monitoring data, e.g., displacement and acceleration, as external dynamic loads to reproduce the dynamic response of the entire physical structure. Subsequently, the dynamic behaviour of specific non-sensor locations can be extracted from the digital twin. The method is formulated as a local-global-local procedure. To validate the proposed approach, two virtual experiments were conducted on: 1) a simply supported Euler-Bernoulli beam subjected to static loads and 2) a high-fidelity finite element model of a composite bridge carrying dynamic traffic loads. The results demonstrate remarkable accuracy in reproducing both global and local behaviours, facilitating observations at non-sensor positions for downstream estimations.