Non-contact measurement technology based on computer vision has been recognized as a critical approach in bridge lightweight monitoring due to its low cost and strong environmental adaptability. To address the sub-millimeter accuracy and real-time requirements of bridge displacement monitoring, this study proposes a visual monitoring method that integrates a connected-domain segmentation matching algorithm with an automatic binarization threshold adjustment mechanism. This combination significantly improves adaptability and robustness under complex lighting conditions. Moreover, the method introduces the SRCNN (Super-Resolution Convolutional Neural Network) as a lightweight super-resolution module, the method achieves a better balance between computational efficiency and measurement precision. The proposed method was validated through model testing and successfully applied to real-bridge displacement monitoring and structural damping ratio identification. These findings demonstrate the practical potential of the method as a reliable reference for static and dynamic performance evaluation and condition assessment of bridges.

Non-structural elements have been demonstrated to be essential for the dynamic performance of large-span structures. However, how to quantify their effect has not yet been fully understood. In this study, the contribution of non-structural elements to dynamic properties of large-span structures is systematically investigated via both field measurement and numerical simulation methods. Modal testing of an indoor stadium and an elevated highway bridge was conducted during different construction phases, and the corresponding modal characteristics were identified. Results show that the traditional capacity-based models are incapable of reflecting the actual dynamic characteristics of in-service structures since neglecting the effect of non-structural elements would result in remarkable discrepancies in modal properties. A general modeling framework incorporating the contribution of slab/deck pavement, infill walls (or crash barriers), and joints/connections for large-span structures is developed to quantitatively consider the effect of non-structural elements based on the principle of equivalence of stiffness and mass to the actual structure. The effectiveness of the method is validated by vibration measurement results.

The noise pollution issue along elevated urban rail transit lines has attracted considerable attention; however, research on the sound insulation characteristics of buildings during operational train noise remains limited. In this study, a field testing campaign was conducted in a building featuring facades constructed from aerated concrete blocks embedded with single-layer glass sliding windows along an elevated rail transit line. The sound insulation characteristics of the building were obtained based on measured data. By neglecting the influence of lateral sound transmission, a window-wall assembly model was proposed for simulating the building’s sound insulation capability. To ensure the accuracy of the prediction results, initial simulations were first performed on individual elements and compared against the experimental sound insulation curves to identify key parameters. Subsequently, simulations of the window-wall assembly model were carried out and compared with test data to determine the acoustic boundary treatment. The results showed that the amount of sound insulation measured in the buildings ranged from 11.16 dB (A) to 19.85 dB (A) during train passage, exhibiting a notable dip in sound insulation within the frequency range of 80 Hz to 100 Hz. Conversely, the predicted sound insulation of 26.38 dB was much higher, also showing a significant dip at around 100 Hz. The simulated values aligned closely with the measured values below 500 Hz; however, discrepancies were observed in mid-to-high frequency ranges above this threshold. To this end, further simulations were performed on a window-wall assembly model incorporating air gaps to take into account air leakage effects. The results indicated that the presence of air gaps considerably diminished high-frequency sound insulation; when accounting for window air leakage effects, the simulated results approached closer alignment with the measured ones.