A Reduced-Order Digital Twin for Structural Health Monitoring of Steel Bridges

Abstract

Conventional simulation methods, such as the Finite Element method, are less suitable for efficient and adaptive digital twins of large-scale structures needed for structural health monitoring (SHM). The complexity and scale of such structures require new simulation techniques that can handle the increasing computational demands and provide accurate results in real-time. This paper presents a novel approach for the implementation of a digital twin for SHM of steel bridges using reduced order modelling. For this purpose, the commercial software Akselos originally developed for digital twining in the aerospace industry is herein adapted to a structural engineering context. The parametric and component-based approach allows a modular structure of the digital twin with the possibility for quick adaptions according to the SHM data. The method is applied to a steel arch bridge as a case study. The example bridge was monitored over the period of a month using strain gauges at five critical locations together with temperature sensors. In addition, a targeted loading test with a truck was performed. The collected monitoring sensor data is processed and merged into the digital twin. This integration enables precise predictions about the bridge’s structural integrity, maintenance and repair planning as well as possible future damage locations. The proposed approach demonstrates the potential of the digital twin for real-time monitoring and prediction of changes in the structural integrity of large-scale structures, providing a promising solution for efficient and effective SHM of steel bridges.