Study Of Remote Structural Damage Monitoring System

An automatic remote structural damage monitoring system is needed for the purpose of long-term monitoring of the structural health condition and identifying the structural damage. As the basic technology of health monitoring, research of structural damage identification based on artificial neural network has the strong background of engineering and the great value for the practical application. By the rapid development of Internet, how to build a remote intelligent diagnostic system is becoming a focus area of research. This thesis discusses the present state and tendency of structural damage identification research in remote monitoring system. And then a remote intelligent diagnostic system and the computing methods are developed by Java.In this thesis, the main research contents include: (1) A framework of remote wireless structural damage monitoring system including sensor system, local processors and central facilities and the main technologies it involves; (2) Methods most used in structural damage identification at present, especially in methods based on neural network. And the structural damage identification using BP and RBF network for unilateral damage plates and multiple beams are demonstrated; (3) Based on the B/S model which sets client as computing centre, a remote intelligent diagnostic system is developed by Java. The main advantages of this system include: (1) Main computing process implements at the client site in order to enhance the efficiency of the network; (2) It is convenient for the users to load and upgrade the program. Users need not to download any other programs but the Applet to invoke Matlab engine at background; (3) Two neural network technologies of BP and RBF now integrated in this system. And it is convenient to integrate other new AI technologies to adapt different applications; (4) The results of the identification will be presented in graphics.The studies mentioned above show this remote structural damage identification system based on Java is very suitable in the case of remote structural damage monitoring application. The structural damage identification system base on BP and RBF neural network technologies can satisfy different demands. And it is valuableand worthy of further research to integrate other new AI technologies in this system.