Development of an Integrated Method for Probabilistic Bridge-Deterioration Modeling
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Author(s)
Bu, Guoping
Lee, Jaeho
Guan, Hong
Blumenstein, Michael
Loo, Yew-Chaye
Year published
2014
Metadata
Show full item recordAbstract
Probabilistic deterioration models such as state-based and time-based models are only capable of predicting future bridge condition ratings when a sufficient amount of condition data and reasonable data distribution are available. However, such are usually difficult to acquire from limited bridge inspection records. As a result, these probabilistic models cannot guarantee reliable long-term prediction for each of the bridge elements concerned. To minimise this shortcoming, this paper proposes an advanced integrated method to construct workable transition probabilities for predicting long-term bridge performance. A selection ...
View more >Probabilistic deterioration models such as state-based and time-based models are only capable of predicting future bridge condition ratings when a sufficient amount of condition data and reasonable data distribution are available. However, such are usually difficult to acquire from limited bridge inspection records. As a result, these probabilistic models cannot guarantee reliable long-term prediction for each of the bridge elements concerned. To minimise this shortcoming, this paper proposes an advanced integrated method to construct workable transition probabilities for predicting long-term bridge performance. A selection process within this method automatically chooses a suitable prediction procedure for a given situation in terms of available inspection data. The Backward Prediction Model (BPM) is also incorporated to effectively predict the bridge performance when sufficient inspection data is unavailable. Four different situations in regard to the available inspection data are predefined in this study to demonstrate the capabilities of the proposed integrated method. The outcomes show that the method can help develop an effective prediction model for various situations in terms of the quantity and distribution of available condition rating data. CE Database subject headings: Bridges; Deterioration; Performance; Predictions.
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View more >Probabilistic deterioration models such as state-based and time-based models are only capable of predicting future bridge condition ratings when a sufficient amount of condition data and reasonable data distribution are available. However, such are usually difficult to acquire from limited bridge inspection records. As a result, these probabilistic models cannot guarantee reliable long-term prediction for each of the bridge elements concerned. To minimise this shortcoming, this paper proposes an advanced integrated method to construct workable transition probabilities for predicting long-term bridge performance. A selection process within this method automatically chooses a suitable prediction procedure for a given situation in terms of available inspection data. The Backward Prediction Model (BPM) is also incorporated to effectively predict the bridge performance when sufficient inspection data is unavailable. Four different situations in regard to the available inspection data are predefined in this study to demonstrate the capabilities of the proposed integrated method. The outcomes show that the method can help develop an effective prediction model for various situations in terms of the quantity and distribution of available condition rating data. CE Database subject headings: Bridges; Deterioration; Performance; Predictions.
View less >
Journal Title
Journal of Performance of Constructed Facilities
Volume
28
Issue
2
Copyright Statement
© 2014 American Society of Civil Engineers (ASCE). This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Subject
Civil engineering
Infrastructure engineering and asset management