Comprehensive Bayesian FE Model Updating of Bridges Using Measurement Data

Ageing bridges face increasing challenges in maintaining safety, performance, and sustainability. Many bridges deteriorate over time due to corrosion, fatigue, and growing traffic loads, while repair or replacement is costly and carbon intensive. Structural Health Monitoring (SHM) provides valuable data to assess bridge condition, but existing methods often struggle to use this information effectively. This research develops a systematic Bayesian approach to update bridge models using SHM data to maximise information gain. The framework enhances reliability in model updating. Applied to a real bridge, the method improves uncertainty reduction and prediction accuracy. This leads to informed and sustainable decision-making for the maintenance and management of ageing bridge infrastructure.