Project completed in 2023
Level: M.A.Sc.
Areas of research : Digital twin.

Implementation of a digital twin for predictive assessment of the structural health of existing infrastructures


In recent years, the built assets industry has undergone a transformative change with the use of predictive modeling and digital twin technology. The use of data, statistical algorithms and digital twins has become more important to optimize asset management strategies, reduce costs and improve overall asset performance. These advanced techniques can predict asset failure or deterioration, optimize maintenance schedules and minimize downtime, by predicting the future state of an infrastructure and enabling real-time data integration, calculation and decision-making via digital twin platforms. In bridge engineering, the accuracy of deterioration and predictive maintenance models, implemented with digital twin technology, is essential for the efficiency of bridge management systems (BMS), where decisions are made to ensure infrastructure safety while minimizing costs. My research focuses on the study of predictive maintenance models and their integration with digital twin technology in the construction industry, with a particular emphasis on bridge engineering. The research focuses on the lateral bending of the floor beams of an existing aging bridge, where strain and temperature sensors have been installed. Predictive methods, including historical analysis, will be employed on the data collected by these sensors, to forecast structural conditions and the necessary maintenance actions.

Areas of research : Digital twin.

Project results

A digital twin platform for a bridge span is generated, and real-time data from sensors is integrated into the DT platform. Predictive models let us know when a bridge’s superstructure needs to be maintained or rebuilt. The best model will be identified by comparing performance indicators. The predictive model will be connected to the bridge’s digital matching platform, contributing to informed, real-time infrastructure decision-making.

Research team

The project team :

Partners : CIMA+.

The project team


This project was supported by :

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