Vibration

In this work, a time–frequency analysis of two railway bridges included in the InBridge4EU project database is presented. The study focuses on the identification of modal parameters from free responses after train passages and their comparison with estimations obtained from ambient vibration data. The wavelet transform is introduced as a valuable tool for detecting both free and forced bridge responses due to different train passages, as well as for conducting time–frequency analysis. This approach is particularly relevant for the identification of structural damping, given its dependence on vibration amplitude, as it enables the estimation of realistic values representative of bridge behavior under operational conditions. Additionally, the paper examines the complementary use of free vibrations for identifying natural frequencies and comparing them with results from ambient vibration tests. Wavelet analysis further reveals the predominant frequencies in the structural response before, during, and after train crossings, thereby capturing the influence of the moving vehicle on bridge dynamics.

This paper proposes a sequential bidirectional gated recurrent unit (BGRU) model to predict construction-induced ground vibrations. The ground vibration time histories for twelve real construction projects in Toronto, Canada, are collected and used to develop the BGRU model. A single time-step method is used to predict the vibrations, and the time window is swept continuously over the whole training data. In addition to the BGRU method, and for comparison, two other methods, autoregressive integrated moving average (ARIMA) and random forest (RF), are used to predict the ground vibrations. The results show that the BGRU method performs much better than ARIMA and RF methods in forecasting construction-induced ground vibrations. The BGRU method captures the construction-induced and background vibrations very well, and this method remains accurate when the training data includes both background and construction vibrations. Therefore, this method can be used to predict ground vibrations in real projects where there is always a potential for missing some parts of the ground vibration data due to the malfunction of the vibration recording units.

The high cost of traditional structural health monitoring systems limits their application to only a few major bridges, leaving most structures unmonitored between manual inspections. To address this issue, this study proposes a UAV mobile detection device (UMD) system that integrates a Raspberry Pi, data acquisition module, and accelerometer for rapid, contact-based vibration measurement. A vibration transmission model between the UMD and the bridge deck is developed to guide hardware design and quantify the influence of isolator stiffness and damping. The UMD’s performance is validated through both laboratory floor tests and field bridge experiments, demonstrating reliable identification of modal frequencies in the range of 0.00–51.95 Hz with a maximum acceleration error below 0.01 g and a relative modal frequency deviation within 3.4%. The analysis further determines that an accelerometer resolution of $0.02\times {10}^{-1}$ g is required for accurate frequency domain measurement. These findings establish the UMD as a fast, low-cost, and accurate tool for rapid bridge vibration assessment and lay the groundwork for future multi-UAV synchronized monitoring.