In this study, a fundamental application of a wireless acceleration sensor network system was carried out by conducting two bridge vibration experiments in autumn and winter. Seasonal effects on the dynamic characteristics of a multispan ballasted prestressed concrete railway bridge were investigated by employing a wireless acceleration sensor network system as a basic study of structural health monitoring technology. The dynamic parameters of every single span, such as natural frequencies, damping ratios, and mode shapes, were determined from free damped vibration, which was caused by human jumping excitation. Owning to the excitation pattern limitation, three modes were obtained in the experiments. A three-dimensional (3D) finite element (FE) model was made to show the rationality of the experimental result. Comparison of the autumn and winter experimental results showed that the natural frequencies of the bridge were significantly higher in winter than in autumn. The frozen ballast and frost on the deck and walkway were revealed to be reasonable explanations for this phenomenon. Meanwhile, the variations of damping ratios were not as simple as those of the natural frequencies. No variation regularity of damping ratios was obvious.
<p>In this study, we conducted fundamental study of structural health monitoring (SHM) by using 3- dimensional acceleration measurement and high resolution FE model analysis of a simple pedestrian overpass having an actual damage. Vibration measurement is performed on the pedestrian bridge by using Imote2 smart sensors. Plural vibration modes are identified from damped free vibration experiments. To reproduce the measured vibration characteristics with satisfactory accuracy for SHM in the analytical model, it is necessary to appropriately model the boundary conditions and secondary members. In this study, modelling of connecting part of main girder and stairs is examined in several patterns. Then, appropriate method of detailed FE modelling is discussed by comparing natural vibration characteristics from eigenvalue analysis.</p>
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