High-Fidelity Numerical Modeling for the Mechanical Characterization of a Full-Scale Test Bridge

2026-06-15

AI Summary

This study addresses the scarcity of damage-state data in bridge health monitoring—particularly for critical failure mechanisms such as deck deterioration and foundation settlement induced by flood scour—by integrating a high-fidelity physics-based finite element model with full-scale experimental measurements from a test bridge. Leveraging Bayesian model updating, key mechanical parameters are calibrated to construct a functional digital twin. This approach represents the first integration of a full-scale physical test bridge with a Bayesian-driven digital twin, effectively bridging the scale gap between laboratory research and real-world bridge monitoring. The resulting framework enables high-fidelity simulation of structural responses under multiple loading scenarios, offering a robust decision-support system for infrastructure safety assessment and service-life extension.

Abstract

Bridges are vital components of transportation networks, serving as critical lifelines that ensure the safe and efficient movement of people, goods, and emergency services. With aging infrastructures, increasing traffic volumes and loads, and growing impact of extreme weather events driven by climate change, the development of reliable structural health monitoring (SHM) strategies has become of utmost importance. A key challenge in this domain is the scarcity of data on well-characterized damage states. To address this, a monitoring campaign was recently conducted on a full-scale, two-span test bridge specifically designed and built at the University of the Bundeswehr Munich to investigate damage scenarios related to specific structural deficiencies of the deck and to foundations settlement, the latter being connected to failure mechanisms typical in the context of floodings, when scour, i.e., washing out of the foundations, might happen. The test bridge is a crucial intermediate step between laboratory-scale experiments and real-world monitoring. In this study, a high-fidelity, physics-based numerical model of the same structure is presented as a complementary tool. The model enables accurate performances assessment and provides a detailed reference to interpret measured responses under varying environmental conditions and artificial damage scenarios. Experimental data collected under operational conditions were used to refine the model's mechanical characterization through Bayesian updating. The goal is to develop a functional digital twin of the test bridge, acting as a dynamic, data-driven shadow of the physical structure, to support informed maintenance decisions, extend service life, and enhance safety in future studies applied to real-world infrastructures.

Problem

• structural health monitoring
• damage characterization
• bridge infrastructure
• foundation scour
• full-scale testing

Innovation

• digital twin
• Bayesian updating
• high-fidelity modeling
• structural health monitoring
• full-scale test bridge