확률신경망에 기초한 교량구조물의 손상평가

Abstract

This paper presents an efficient algorithm for estimation of damage location and severity in structure using Probabilistic Neural Network(PNN). Artificial neural network has been being used for damage assessment by many researchers, but there are still some battiers that must be overcome to improve its accuracy and efficiency. The mahor problems with the conventional neural network are the necessity of many training data for neural network learning and ambiguity in the relation of neural network architecture with convergence of solution. In this paper, PNN is used as a pattern classsifier to overcome those problems in the conventional neural network. The basic idea of damage assessment algorithm proposed in this paper is that modal characteristics from a damaged structure are compared with the training patterns which represent the damage in specific element used in producing the training pattern is consideed as a damaged one. The proposed damage assessment algorithm using PNN is applied to a 2-span continuous beam model structure to verify the algorithm.