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https://hdl.handle.net/11681/39601| Title: | Estimating Bridge Reliability by Using Bayesian Networks |
| Authors: | Groeneveld, Andrew B. Wood, Stephanie G. Ruiz, Edgardo Roberts, Jeffery M. |
| Keywords: | Bridges Bayesian networks Bayesian statistical decision theory Reliability Material defects Load rating Concrete bridges--Design and construction Reliability (Engineering)--Statistical methods Bridges--Maintenance and Repair Bridges--Inspection |
| Publisher: | Geotechnical and Structures Laboratory (U.S.) Engineer Research and Development Center (U.S.) |
| Series/Report no.: | Technical Report (Engineer Research and Development Center (U.S.));no.ERDC/GSL TR-21-1 |
| Abstract: | As part of an inspection, bridge inspectors assign condition ratings to the main components of a bridge’s structural system and identify any defects that they observe. Condition ratings are necessarily somewhat subjective, as they are influenced by the experience of the inspectors. In the current work, procedures were developed for making inferences on the reliability of reinforced concrete girders with defects at both the cross section and the girder level. The Bayesian network (BN) tools constructed in this work use simple structural m echanics to model the capacity of girders. By using expert elicitation, defects observed during inspection are correlated with underlying deterioration mechanisms. By linking these deterioration mechanisms with reductions in mechanical properties, inferences on the reliability of a bridge can be made based on visual observation of defects. With more development, this BN tool can be used to compare conditions of bridges relative to one another and aid in the prioritization of repairs. However, an extensive survey of bridges affected by deterioration mechanisms is needed to confidently establish valid relationships between deterioration severity and mechanical properties. |
| Description: | Technical Report |
| Gov't Doc #: | ERDC/GSL TR-21-1 |
| Rights: | Approved for public release; distribution is unlimited |
| URI: | https://hdl.handle.net/11681/39601 http://dx.doi.org/10.21079/11681/39601 |
| Size: | 67 pages / 7.76 MB |
| Types of Materials: | |
| Appears in Collections: | Technical Report |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ERDC GSL TR-21-1.pdf | 7.76 MB | Adobe PDF |  View/Open |