Innovative Bridge Asset Management Through Data-Driven Digital Twins Built Using Dual Sensing Technology: From Rapid AVT-Based Identification to Continuous SHM-Based Tracking

The growing need for reliable and efficient bridge performance assessment has motivated the development of digital-twin frameworks that fuse high-quality field measurements with advanced data analytics. This study presents an innovative approach to bridge asset management through the creation of data-driven digital twins built using dual sensing technology, combining rapid Ambient Vibration Testing (AVT) with customized real-time structural health monitoring (SHM). The proposed framework provides a sensing-based approach based on the as-is condition of the bridge, not on assumptions, and presents a scalable, non-intrusive, and actionable methodology suitable for short- and medium-span bridges. The sensing-based digital twin workflow begins with a rapid AVT campaign, in which a network of wireless, high-sensitivity sensors (triaxial velocimeters and triaxial accelerometers integrated within the same unit) captures the bridge’s full dynamic behaviour within a few hours. These synchronized measurements enable precise extraction of modal parameters, including natural frequencies, high-resolution 3D mode shapes, and damping ratios. The identified dynamic properties form the baseline for the data-driven twin and are compared with expected behaviour from a database of healthy structures to detect anomalies related to stiffness loss, boundary condition changes, or localized structural deterioration. As a case study, a bridge with a span of 45 meters is presented. The initial AVT results revealed concerning structural anomalies in several mode shapes, indicating potential issues in the supporting systems, including the foundations and soil conditions. Based on these findings, a customized real-time monitoring system was developed and deployed, strategically utilizing the same dual-sensing technology to capture long-term behaviour under traffic and environmental loading. Amplitudes of vibrations, frequencies, damping ratios, and local slopes are being monitored, and by removing the effects of temperature, the system is capable of providing reliable tracking, automated event detection, and automated SHM reports reflecting the as-is condition of the bridge. The integration of rapid AVT with tailored continuous monitoring created a living data-driven digital twin, capable of supporting targeted maintenance planning, validating rehabilitation needs, and improving decision-making for bridge owners