Hattori, H. Primary Author mixed material bibliography Taylor & Francis 2012 en English

Computer Software

Recently, a vast amount of research has been conducted on health monitoring of existing structures such as buildings, bridges and other civil structures. Furthermore, in Japan, natural disasters like typhoons and earthquakes occur frequently increasing the importance of the damage assessment of the existing structures. In order to evaluate the damage state of structures, health monitoring technology is quite promising to provide useful information. However, there are still some research needs in modeling, analysis and experimental examination before routine applications of health monitoring systems. In this paper, an attempt is made to develop a damage detection approach system by 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 technique that uses fuzzy-neural networks as a weak learner. By using AdaBoost technique, the network can respond to various types of external forces and the prediction accuracy increases. The fuzzy reasoning predicts the next state of structural behavior such as displacement, velocity and acceleration from the current state of structural behavior and external force. 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 from 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 large scale steel grid type laboratory structure. BRIDGE MONITORING 624.21(063) Perpustakaan Direktorat Bina Teknik Jalan dan Jembatan Direktorat Jenderal Bina Marga - Kementerian Pekerjaan Umum (NPP: 3273244A00000001) 0000020779 My Library 20056 machine generated