Structural Parameter Estimation of Bridges Using Neural Networks

신경망을 사용한 교량구조의 미지계수 추정

Procedures for estimation of axial or flexural rigidities of bridge members by neural networks are shown. To treat large scale structures containing many unkwon parameters, substructuring concept is introduced. The measurement points are selected considering the sensitivity of the element stiffnesses of interest. Utilization of relative mode vectors is found to be very effective for the local parameter estimation. Then, the study focuses on the method to obtain the training set enough to represent structures. It is shown that noise injection is effective to reduce the estimation errors caused by measurement noise. Verification of the present method is carried out using a cable-stayed bridge model.