APPLICATIONS OF SOFT COMPUTING TECHNIQUES IN STRUCTURAL SYSTEM IDENTIFICATION
The seismic behaviour of masonry structures strengthened with fibre-reinforced polymer (FRP) materials has received very little attention experimentally and theoretically. The non-linear nature of these systems often results in mechanical responses that are difficult to predict via classic analytical methods. A neural network (NN) approach for dynamic system identification is presented here. This method addresses aspects such as system non-linearity, dependence on past loading history and noise contamination. Full-scale seismic tests conducted at Bristol University provided a large dataset of measured and computed dynamic state variables.The NN is capable of predicting the system response under a wide range of seismic inputs and for various user-specified reinforcement ratios. The results indicate that the NN non-parametric approach has an important potential in dynamic system identification.
neural network reinforced masonry FRP dynamic system identification
L. Dihoru C.A. Taylor A.J. Crewe N. Alexander
Department of Civil Engineering, University of Bristol, UK
国际会议
14th World Conference on Earthquake Engineering(第十四届国际地震工程会议)
北京
英文
2008-10-12（万方平台首次上网日期，不代表论文的发表时间）