Assisting Load Rating Testing of Precast Reinforced Concrete Bridge Slab through Digital Twins and Field Monitoring Data

Abstract

Abstract Bridges deteriorate over time because of factors like rebar corrosion and concrete damage, reducing their load-bearing capacity. Traditional load grading processes are costly and time-consuming, often resulting in traffic disruptions of one to four days because of lane closures. This study introduced a load rating technique leveraging digital twins technology. Data related to crack evolution and inherent strain during loading were obtained by monitoring the laboratory bridge slabs using potentiometers, strain gauges, and fiber optic sensors. Subsequently, a calibrated three-dimensional finite element model, representing different loading scenarios, was crafted, forming the foundation for the digital twins model of bridge slabs. The slab model was then integrated into a bridge span, constituting a digital twins model of the bridge span for load rating tests. The developed digital twins model was applied to a bridge in Abbeville, South Carolina, and its accuracy was validated with field monitoring data.