Aging Highway Bridges

The class-1 models are based on qualitative stochastic cumulative damage models, which are used to provide estimates of the life cycle performance of a bridge or a network of bridges, as well as long-term estimates of maintenance funding needs. These models are simple to use and can be developed fro...

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Veröffentlicht in:Canadian Consulting Engineer 2007-01, Vol.48 (1), p.30
1. Verfasser: Lounis, Zoubir
Format: Magazinearticle
Sprache:eng
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Zusammenfassung:The class-1 models are based on qualitative stochastic cumulative damage models, which are used to provide estimates of the life cycle performance of a bridge or a network of bridges, as well as long-term estimates of maintenance funding needs. These models are simple to use and can be developed from relatively limited historical data, considering the impacts of key parameters that affect bridge deterioration (e.g. environmental conditions, structural system type, traffic loading). Using the prediction capabilities of this model, the condition of a bridge - or of a network - is rated using qualitative performance indicators obtained from a visual inspection and non-destructive evaluation. The class-2 models are quantitative stochastic performance prediction models, which are used for high-risk and safety-critical bridge components, as well as for the detailed and final analysis of a given bridge. These models will enable a quantitative assessment of the safety and serviceability of a bridge and will provide a more accurate assessment of the risk of failure and life cycle cost. Knowing the risk level of failure for aging bridges (e.g. fatalities and injuries, major traffic disruptions, high rehabilitation and/or user costs), enables owners to prioritize bridges for maintenance and rehabilitation. The risk of failure of a bridge is defined as the consequences of its failure weighted by its likelihood of failure. The use of the risk of failure as a criterion for making maintenance decisions will enable owners to maximize public safety and the reliability of its bridges.
ISSN:0008-3267
1923-3337