Digital twin implementation for predictive structural health assessment of existing infrastructure
In recent years, the built asset industry has undergone a transformative shift with the employment of predictive modeling and digital twin technology. The utilization of data, statistical algorithms, and digital twins has become more important in optimizing asset management strategies, reducing costs, and improving overall asset performance. These advanced techniques enable the prediction of asset failure or deterioration, optimize maintenance schedules, and minimize downtime, by predicting the future state of an infrastructure and enable real-time data integration, calculation, and decision-making through digital twin platforms. Within bridge engineering, the accuracy of deterioration and predictive maintenance models, implemented with digital twin technology, is pivotal for effective Bridge Management Systems (BMS), where decisions are made to ensure infrastructure safety while minimizing costs. My research provides the study of predictive maintenance models and their integration within digital twin technology in construction industry, with a specific focus on bridge engineering. The research concentrates on the lateral bending of floor beams of an existing aging bridge, where strain and temperature sensors have been installed. Predictive methods, including time history analysis will be employed on the data collected from these sensors, to foresee the structural conditions and maintenance actions needed.
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