Structural Health Diagnosis Using Soft Computing Technique for Cable-Stayed Bridge after Earthquake

Abstract eng:
The 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.

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