000009877 001__ 9877
000009877 005__ 20141205153202.0
000009877 04107 $$aeng
000009877 046__ $$k2008-10-12
000009877 100__ $$aDihoru, L.
000009877 24500 $$aApplications of Soft Computing Techniques in Structural System Identification

000009877 24630 $$n14.$$pProceedings of the 14th World Conference on Earthquake Engineering
000009877 260__ $$b
000009877 506__ $$arestricted
000009877 520__ $$2eng$$aThe 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.

000009877 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000009877 653__ $$aneural network, reinforced masonry, FRP, dynamic system identification

000009877 7112_ $$a14th World Conference on Earthquake Engineering$$cBejing (CN)$$d2008-10-12 / 2008-10-17$$gWCEE15
000009877 720__ $$aDihoru, L.$$iTaylor, C. A.$$iCrewe, A. J.$$iAlexander, N.
000009877 8560_ $$ffischerc@itam.cas.cz
000009877 8564_ $$s345542$$uhttps://invenio.itam.cas.cz/record/9877/files/14-0083.pdf$$yOriginal version of the author's contribution as presented on CD, Paper ID: 14-0083.
000009877 962__ $$r9324
000009877 980__ $$aPAPER