pp. 165-178
S&M1309 Research Paper of Special Issue https://doi.org/10.18494/SAM.2017.1421 Published: February 15, 2017 Investigation of Seasonal Variations of Dynamic Characteristics of a Concrete Bridge by Employing a Wireless Acceleration Sensor Network System [PDF] Youqi Zhang, Yasunori Miyamori, Takanori Kadota, and Takehiko Saito (Received June 20, 2016; Accepted November 28, 2016) Keywords: wireless acceleration sensor, structural health monitoring, seasonal effects, dynamic characteristics, multispan prestressed concrete railway bridge
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.
Corresponding author: Yasunori MiyamoriCite this article Youqi Zhang, Yasunori Miyamori, Takanori Kadota, and Takehiko Saito, Investigation of Seasonal Variations of Dynamic Characteristics of a Concrete Bridge by Employing a Wireless Acceleration Sensor Network System, Sens. Mater., Vol. 29, No. 2, 2017, p. 165-178. |