Many bridges in operation today are aging, highlighting the need for advanced structural health monitoring solutions to ensure continued safety and performance. In this study, we propose an integrated framework that leverages Distributed Acoustic Sensing (DAS) with self-deployed fibers to monitor vehicle-induced dynamic response of bridges and to convert DAS-observed strain rate data into displacement—allowing direct alignment with standard engineering evaluation metrics used in structural codes. Validation against Laser Doppler Vibrometer (LDV) measurements shows high accuracy of the DAS-estimated bridge displacements, with an Average Percentage Variation Error (APVE) below 4.98%. Additionally, vehicle speeds are estimated from DAS data to demonstrate the feasibility of real-time and anonymous traffic monitoring. The observed vehicle speed distribution closely aligns with the tiered penalty thresholds defined in traffic regulations, indicating that DAS holds strong potential for future traffic monitoring and management applications. This study offers a practical and innovative approach to dynamic bridge monitoring and displacement estimation using DAS technology. • SSI-DATA and modal stacking identify stable high-order modes from DAS coda waves. • Physics-guided inference reconstructs bridge displacement from DAS strain-rate. • Vehicle trajectory tracking infers traffic behavior from DAS bridge vibrations.
Zhong et al. (Sat,) studied this question.