Abstract
Estimation of the time that a bridge or bridge component stays in a specific condition can guide decision-making on bridge maintenance and preservation. Statistical models of the time-in-condition rating (TICR) for bridges or bridge components are good candidates for this purpose. Typically, these models are calibrated using existing inspection data. Current practice tends to trim a large portion of the data that are deemed incomplete. However, there is actually a lot of useful information in these data (e.g., lower bounds for TICR), which should also be incorporated to establish better estimation of TICR. To address this, within the Bayesian framework, this paper proposes the adoption of a modified likelihood function to explicitly incorporate both complete and incomplete inspection data for model calibration. In addition, Bayesian model class selection is used to select the most appropriate models out of several candidate statistical models. The proposed approach is applied to establish TICR models for different types of bridges and bridge components in Colorado using National Bridge Inventory (NBI) data. The results and comparisons show the importance and necessity of explicitly incorporating incomplete inspection data in the model calibration and class selection for bridge condition deterioration models.
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Acknowledgments
This work was supported by Mountain-Plains Consortium (MPC) under Grant No. 69A3551747108. This support is gratefully acknowledged.
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Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 25 • Issue 3 • March 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Mar 26, 2019
Accepted: Sep 30, 2019
Published online: Jan 16, 2020
Published in print: Mar 1, 2020
Discussion open until: Jun 16, 2020
ASCE Technical Topics:
- Analysis (by type)
- Architectural engineering
- Bayesian analysis
- Bridge components
- Bridge engineering
- Bridge management
- Building management
- Calibration
- Construction engineering
- Construction management
- Deterioration
- Engineering fundamentals
- Inspection
- Maintenance and operation
- Materials characterization
- Materials engineering
- Mathematics
- Measurement (by type)
- Statistical analysis (by type)
- Statistics
- Structural engineering
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