확률신경망을 이용한 철도 판형교의 손상평가 = Damage Assessment of Plate Gider Railway Bridge Based on the Probabilistic Neural Network

다국어 초록 (Multilingual Abstract)

Artificial neural network has been used for damage assessment by many researchers, but there are still some barriers that must be overcome to improve its accuracy and efficiency. The major problems associated with the conventional artificial neural network, especially the Back Propagation Neural Network(BPNN), are on the need of many training patterns and on the ambiguous relationship between neural network architecture and the convergence of solution. Therefore, the number of hidden layers and nodes in one hidden layer would be determined by trial and error. Also, it takes a lot of time to prepare many training patterns and to determine the optimum architecture of neural network. To overcome these drawbacks, the PNN can be used as a pattern classifier. In this paper, the PNN is used numerically to detect damage in a plate girder railway bridge. Also, the comparison between mode shapes and natural frequencies of the structure is investigated to select the appropriate training pattern for the damage detection in the railway bridge.