Research On Wireless Monitoring System Of Durability For Reinforced Concrete Bridge Structure

Reinforced concrete structure,as the main structure of the bridge,is a large numberof applications.however,during the design phase we emphasis on the safety andperformance of the structure and underestimate the long-term durability problemscaused by the action of the environment.With the extension of useful life of the bridge,these durability issues continue to appear in the existing reinforced concrete bridgestructure, resulting in increasing maintenance costs,even serious economic losses andsocial impacts.Real-time,fast,accurate grasp of in-service bridges durability and itschange is fundamental to the design of bridge maintenance program.Therefore,to playthe advantage of the low-powera and real-time in wireless network,developpingwireless monitoring system of durability for reinforced concrete bridge structure,hasimportant basic significance for improving the level of monitoring for the durability ofconcrete bridge, extending the life of the structure.The dissertation,based on the research of the monitoring the status quo ofdurability for reinforced concrete bridge structure，analyzes the impact of environmentalfactors on the durability of concrete bridge,realizes the construction of wireless sensornetwork of the bottom of the concrete bridge durability.It includes hardwaredesign,software design of wireless monitoring node,network debugging, and theaccomplishment of the basic durability database creation,the design of monitoringmanagement platform.Secondly,the dissertation,which LabVIEW as the core softwareplatform,develop the Access database and the Matlab algorithm model interfaceprogram,to achieve the systematic requirements of the wireless monitoring andmanagement of durability for reinforced concrete bridge structure.Finally,in the dissertation,concrete carbonation,which is one of the durabilitydeterioration characteristics in the general atmospheric environment,is made as the object of study.According to the related durability theoretical and experimental research,the main affect factors of the reinforced concrete carbonation are determined.To applyPSO(Particle Swarm Optimization) to optimize BP neural network model for concretecarbonation depth prediction,overcomes the defects of slow convergence,the badstability of the solution,easy to fall into local minima of artificial neural network andeffectively improve the model calculation precision and the accuracy for durabilityprediction of bridge.