000010723 001__ 10723
000010723 005__ 20141205155757.0
000010723 04107 $$aeng
000010723 046__ $$k2008-10-12
000010723 100__ $$aLin, Chu-Chieh Jay
000010723 24500 $$aStructural Health Diagnosis Using Soft Computing Technique for Cable-Stayed Bridge after Earthquake

000010723 24630 $$n14.$$pProceedings of the 14th World Conference on Earthquake Engineering
000010723 260__ $$b
000010723 506__ $$arestricted
000010723 520__ $$2eng$$aThe cable-stayed bridge is generally a highly statically indeterminate structure. The structural performance of the cable-stayed bridge is highly sensitive to the load distribution among major components such as the pylon, the stayed cables and the girders of the bridge. Therefore, the stayed cables of the cable–stayed bridge should be monitored to prevent bridge damage due to earthquake, strong wind, differential settlement, fatigue/defect of the material or loose of tension within the cables. Since the cable-stayed bridge with long span is usually on the critical path of transportation net and plays very important role on hazard mitigation. To assure the cable-stayed bridge remains functional after the moderate earthquake become increasing important. That makes the rapid structure health diagnosis of the cable-stayed bridge very necessary in a maintenance procedure. This study proposes a fast structural health diagnosis method for cable-stayed bridges using soft computing techniques (i.e. Neural Networks, Genetic Algorithm, etc.) and field measurement data. The neural networks were used to determine the type and degree of the damaged bridge with ease and efficiency. Based on the cable force evaluated, the structural behavior including the deformation and stress state of the bridge can be traced successfully. Also, the damage state of the cable-stayed bridge can be identified using neural networks through the measured cable forces within stayed-cables. The validity of the proposed method is confirmed by the numerical studies using SAP2000 on several bridge models. A few cases were studied and the results obtained could benefit the rapid structural health diagnosis of the cable-stayed bridges after earthquake.

000010723 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000010723 653__ $$aneural network, structural health diagnosis, cable-stayed bridge, earthquake 

000010723 7112_ $$a14th World Conference on Earthquake Engineering$$cBejing (CN)$$d2008-10-12 / 2008-10-17$$gWCEE15
000010723 720__ $$aLin, Chu-Chieh Jay$$iChen, Chien-Chou
000010723 8560_ $$ffischerc@itam.cas.cz
000010723 8564_ $$s133359$$uhttps://invenio.itam.cas.cz/record/10723/files/14-0067.pdf$$yOriginal version of the author's contribution as presented on CD, Paper ID: 14-0067.
000010723 962__ $$r9324
000010723 980__ $$aPAPER