Real-time Online Identification System Of Weigh-in-motion Of Vehicles On Bridge

Data acquisition terminal of real-time online identification system of weigh-in-motion of vehicles on bridge described in this article uses fiber bragg grating sensors.There are a great of advantages compared to traditional weigh-in-motion recognition. It is an important part of the bridge health monitoring.It is becoming a focus on domestic and international research in this field. During traditional load check, vehicles need to be stopped and acceptd static weighting.It is likely to cause a traffic jam; sensors used here are mainly based on the electrical and mechanical principles, there are many specific drawbacks in performance. During the identification device, first,acquisition sensors use fiber Bragg grating are superior to traditional sensors based on the electrical and mechanical principles in many aspects,for example, anti-interference, small size and easy to install, resolving accuracy of the scope of the measured values, linear range; during real-time online identification, vehicles Dynamically are checked which is not likely to cause traffic jam. During Dynamic check,many factors can cause changing of result outside of the static weighting:speed, flatness of Bridge deck on check, vibration of the vehicle, dynamic recognition need overcoming the shortcuts of traditional load check. It is important to ensure that accuracy of this identification system.Real-time online identification system of weigh-in-motion of vehicles on bridge consists of two parts:the collection,display and storage of relevant data, such as dynamic strain,speed and other information, the identification of overweight vehicles.In the part of data acquisition, display and storage, acquisition of data is through Ethernet port.Data of fiber grating demodulator machine device is transmissed into sevice.Real-time curve shows data collected through the database.The collected data are saved to database for preparing for data query in future. Acquisition relates to the programming of the Ethernet port. Storage relates to the use of ADO accessing to database. Due to the high frequency of collection and the large volume of data acquisition,display and store at the same time, the system has opened up multiple threads to improve CPU utilization.Final result of identification part of weigh-in-motion of vehicle is affected by many factors. the BP neural network algorithm is introducted to identify the part of this article. Due to traditional BP neural network can only reach a local optimum and convergent slower, genetic algorithms is used to optimize the traditional BP neural network.Optimized BP neural network can not only improve the convergence speed but also reach the global optimum.The experimental results show that this article recognition system can overcome shortcut of the traditional static weighing.lt meets some field applications with error requirement under5%, improvimg the accuracy relativs to a simple dynamic recognition.