Development of an Integrated Method for Probabilistic Bridge-Deterioration Modeling
Publication: Journal of Performance of Constructed Facilities
Volume 28, Issue 2
Abstract
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 minimize 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 are 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.
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Acknowledgments
The authors are grateful for the financial support provided by the Australian Research Council through an ARC Linkage Project Grant (LP0883807). The authors also wish to express their sincere thanks to the Industry Partners, Queensland Department of Transport and Main Roads and the Gold Coast City Council, for their generous financial and in-kind support.
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© 2014 American Society of Civil Engineers.
History
Received: Jan 17, 2012
Accepted: Nov 13, 2012
Published online: Nov 15, 2012
Published in print: Apr 1, 2014
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