This paper addresses the current issues of the design, operation and monitoring of bridge structures in the context of digitalisation within the transport industry. Purpose: To develop digital twins to improve the reliability and safety of operational bridge structures. Method: The case study of an operational railway bridge highlights the limitations of traditional infrastructure management approaches. The authors propose a solution based on digital twins: a comprehensive system comprising interconnected virtual models and physical structures, as well as automated and periodic monitoring systems and supervisory measures. This solution allows the technical condition of facilities to be monitored in real time. Results: This paper describes a digital information model of the bridge that has been adapted for monitoring individual indicators such as deformation and frequency. A notable feature of the model is its ability to dynamically detail elements, thereby reducing the computational load. Additionally, it proposes an automated assessment of not only the monitoring system sensor readings with calculated boundary parameters, but also of the existing regulatory requirements. This approach enables rapid identification of deviations that significantly reduce the safety of rolling stock movement. Practical significance: This study confirms that digital twins enhance the reliability of bridges by predicting defects and enabling a rapid response. The authors emphasize the importance of developing a domestic regulatory framework and implementing machine learning to analyse long-term trends in the technical condition of structures. The study’s findings suggest that digital twins have the potential to ensure the sustainability of transport infrastructure in the face of increasing loads.
Chaplin et al. (Fri,) studied this question.