Advanced sensor dynamic measurement and heuristic data analysis model for bridge health monitoring system

Abstract

Presently the health and safety monitoring of a bridge is considered as a significant area of research where the attention has been paid by many researchers. In this article the bridge structural damages due to environmental fluctuations and other parameters has been analyzed using cutting-edge technologies. In this research the technology of advanced Intelligent Internet of Things (IIoT) sensors with signal processing systems is designed and developed to monitor the health condition of the bridge using data analytic techniques. In the recent past these sensor systems has been used collect the vibration signal sets caused by the vehicles movement on the bridge. Further, these collected data sets are analyzed with the help data analytic approach using traditional independent analysis models which fails to produce optimum results in terms of reliability, efficiency, stability, corrosion and crack of the bridge. In this article to overcome this issue an improved heuristic nonlinear model has been developed to analyze the data sets using non-linear and linear separation analogy. This optimized data analytics technique with advanced sensing mechanisms is validated experimentally and the outcomes shows promising solutions to monitor bridge health in effective manner than traditional strategies.