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Implementation of a Mathematical Model for the Prediction of the Future Condition Rating for Bridge Components

Autor(en):

ORCID
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Transportation Research Record: Journal of the Transportation Research Board, , n. 3, v. 2677
Seite(n): 1700-1714
DOI: 10.1177/03611981221127003
Abstrakt:

Bridges are continuously exposed to environmental changes and dynamic loading effects caused by moving loads. As a result, bridge deterioration is a critical problem in the U.S.A. The National Bridge Inventory (NBI) database contains historical bridge condition information for bridges in the U.S.A. and currently it is the best available database for describing the historical condition of bridges in the U.S.A. The objective of this paper is to develop a mathematical model that can be used to predict the future condition ratings of each bridge component and, more specifically, to estimate the probability of each bridge component being at any condition rating at any future year. Two different types of future condition rating prediction models, namely the current practice model (CPM) and the deterioration prediction model (DPM), were developed. The CPM is capable of simulating the effects of historical maintenance activities when predicting the future condition rating probabilities, whereas the DPM does not consider the effects of historical maintenance activities when predicting the future condition rating probabilities. Both models were illustrated and validated using most current NBI data. The performance of both models was evaluated on hundreds of bridges in the states of Iowa and Wisconsin. The results indicated that the CPMs tend to converge to condition rating 6 within 15 years, whereas the DPMs tend to converge to condition rating 4 with 15 years. This suggests that conducting current maintenance activities helps to keep the nation’s bridges in at least “satisfactory condition.” However, a lack of performing any maintenance could lead to bridges being structurally deficient within 15 years.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1177/03611981221127003.
  • Über diese
    Datenseite
  • Reference-ID
    10777865
  • Veröffentlicht am:
    12.05.2024
  • Geändert am:
    12.05.2024
 
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