Abstract
This paper presents long-term condition assessment approaches of cables and bridge decks under in-service loads based on Structural Health Monitoring (SHM) technique. Aiming to the critical structural components, three important issues related to the long term performance evaluation, including loads and responses modelling, resistance degradation and fatigue life assessment, were investigated in this study. For vehicle loads and responses modelling, the general homogeneous stochastic processwas employed to approximately simulate the monitored events. And a straightforward and general extreme value distribution model is derived by the Laplace transform. For the resistance degradation of cables, the deterioration process of steel wires considers simultaneously the uniform corrosion, pitting corrosion and the corrosion fatigue propagation, in which the cyclic stress was obtained through monitored cable forces. Based on the extension from steel wire deterioration to cable degradation, the cable reliability in terms of safety and serviceability incorporating the monitored responses and resistance degradation is further estimated. For fatigue life evaluation of the bridge decks, the accurate stress analysis of hot spot should be performed ahead taking advantage of multi-scale finite element model. The presented long term condition assessment of critical structural components would provide basis and guideline for the future maintenance and repair decision.
| Original language | English |
|---|---|
| Journal | International Conference on Advances in Experimental Structural Engineering |
| Volume | 2015-August |
| State | Published - 2015 |
| Externally published | Yes |
| Event | Joint 6th International Conference on Advances in Experimental Structural Engineering, AESE 2015 and 11th International Workshop on Advanced Smart Materials and Smart Structures Technology, ANCRiSST 2015 - Urbana-Champaign, United States Duration: 1 Aug 2015 → 2 Aug 2015 |
Keywords
- Condition assessment
- Fatigue evaluation
- Resistance degradation
- Stochastic process
- Structural health monitoring
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