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The focus of this paper is to build the damage identify system, which performs “system identification” to detect the positions and extents of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies.
The focus of this paper is to build the damage identification system, which performs “system identification” to detect the positions and extents of structural damages. The identification of structural damage can be characterized as a nonlinear process which linear prediction models such as linear regression are not suitable. However, neural network techniques may provide an effective tool for system identification. The method of damage identification using the radial basis function neural network (RBFNN) is presented in this paper. Using this method, a simple reinforced concrete structure has been tested Both in the absence and presence of noise. The results show that the RBFNN identification technology can be used with related success for the solution of dynamic damage identification problems, even in the presence of a noisy identify data. Furthermore, a remote identification system based on that is set up with Java Technologies.