抄録
In this paper, an attempt is made to develop a structural health monitoring system that can adapt to the structural systems and environments, by introducing the learning ability. This learning ability facilitates a monitoring paradigm without a need for preliminary investigation of the underlying structure and environment. In other words, it is not necessary to use the precise modeling and analysis methods before conducting the health monitoring. The proposed system learns the vibration response by using AdaBoost. By using AdaBoost technique, the network can respond to various types of external forces and the prediction accuracy increases. Previously, a health monitoring system that can adapt to the structural systems and environments through the learning ability was developed with the recognition rate of over 80% using numerical simulations. However, experimental verification is needed before real life application of the proposed system. In this paper, results laboratory experiments are presented to show the effectiveness of the methodology. It is observed that the proposed system can recognize the change of structural characteristics and condition states of a steel grid type large scale bridge model.
