A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal characteristics of the bridge from ambient acceleration time histories obtained from field measurements exploiting a network of reference and roving sensors. The Transitional Markov Chain Monte Carlo algorithm is used to perform the model updating by drawing samples from the posterior distribution of the model parameters. The proposed framework yields reasonable uncertainty bounds for the model parameters, insensitive to the redundant information contained in the measured data due to closely spaced sensors. In contrast, conventional Bayesian formulations which use probabilistic models to characterize the components of the discrepancy vector between the measured and model-predicted mode shapes result in unrealistically thin uncertainty bounds for the model parameters for a large number of sensors.

Long-term deterioration of concrete bridges is a natural process that requires prudent maintenance actions throughout the bridge’s life-cycle. Nowadays, there is an ongoing effort to simulate such processes into practical models. One primary element for the model’s accuracy is the datasets used for its development. The gap between underestimated or overestimated and actual values could be narrowed by utilizing real-world datasets on bridge deterioration and rehabilitation obtained from systematic inspections over time in similar environments. Therefore, the present study aims to develop an empirical probabilistic model for precisely predicting the bridge’s future performance and suitably implementing maintenance strategies that facilitate sustainable management during bridge service life based on real data. Actual records of 72 concrete bridges from motorways in Northern Greece were collected, documenting different detected defect types, condition states, and associated maintenance costs over time. Two discrete-time Markov-chain models for the bridge’s superstructure and substructure were produced, allowing for the prediction of maintenance costs that align with the given structural condition throughout its operational life. A Chi-square test demonstrated the model’s applicability to similar datasets. This enables bridge managers to obtain a comprehensive overview of the bridge’s longitudinal performance and maintenance expenditures and adopt economically sustainable solutions for the bridge’s management.