This study presents an ambient vibration–based dynamic assessment of the Ghatte Khola steel truss bridge using a smartphone accelerometer and advanced vibration-based system identification techniques. Acceleration data were collected through the Phyphox mobile application under natural excitations such as vehicular traffic and wind. The recorded responses were analyzed in both time and frequency domains to extract key modal parameters, including natural frequencies, mode shapes, and damping ratios. To ensure accuracy and reliability, multiple system identification methods—Peak Picking (PP), Enhanced Frequency Domain Decomposition (EFDD), Autoregressive Moving Average (ARMA), and Stochastic Subspace Identification (SSI)—were employed, and their results showed strong consistency. A finite element model of the bridge was developed in CSI Bridge to simulate its dynamic behavior, and the numerical results were compared with experimental findings. The comparison revealed good agreement, with natural frequency discrepancies within 10%, indicating that the model effectively represents the actual structural behavior. The estimated damping ratios were approximately 3%, which is consistent with the typical range for steel truss bridges and reflects realistic energy dissipation characteristics. Overall, the findings demonstrate that smartphone-based vibration monitoring, when combined with robust modal identification techniques, provides a reliable, efficient, and cost-effective approach for structural health monitoring. This method is particularly beneficial for resource-limited regions like Nepal, offering a practical solution for bridge assessment and establishing a foundation for future development of low-cost structural monitoring systems.
Bajagain et al. (Fri,) studied this question.