A Structural Damage Neural Network Monitoring System

Abstract: Traditional methods for structural monitoring and damage assessment have been implemented largely through visual inspection and on-site tests. A system for automating this process should be able to record the various signatures of the structure to be monitored and issue a warning signal if there is a damage-related change in those signatures. In this paper, a general system for structural damage monitoring is proposed based on observations of other researchers and the results obtained from a case study of a physical and analytical model of a five-story steel frame. The proposed diagnostic system utilizes neural networks for identifying the damage associated with changes in structural signatures. The system is independent of the type of signatures used for monitoring. Two sets of neural networks were developed. The first set was trained with the results of a series of shaking-table experiments, while the second set was trained with the output produced from a finite-element model of the same test structure. The results show that the proposed system provides a suitable framework for automatic structural monitoring.