Predicting the probability of failure of timber bridges using fault tree analysis
Article
Article Title | Predicting the probability of failure of timber bridges using fault tree analysis |
---|---|
ERA Journal ID | 41113 |
Article Category | Article |
Authors | Lokuge, Weena (Author), Wilson, Matthew (Author), Tran, Huu (Author) and Setunge, Sujeeva (Author) |
Journal Title | Structure and Infrastructure Engineering: maintenance, management, life-cycle design and performance |
Journal Citation | 15 (6), pp. 783-797 |
Number of Pages | 15 |
Year | 2019 |
Publisher | Taylor & Francis |
Place of Publication | United Kingdom |
ISSN | 1573-2479 |
1744-8980 | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/15732479.2019.1569069 |
Abstract | The resilience of a community in an extreme event depends mainly on the robustness of the critical infrastructures. Road bridges are a critical link of the road network, which plays a focal role in Australia’s economy, prosperity, social well-being and quality of life. Timber bridges are a weaker link of the Australian road network and they often provide critical access to the rural communities. This research uses a number of bridge inspection reports to develop a method to predict the probability of failure of a timber bridge. The inspected condition states of the elements in the timber bridge are used to develop a Markov chain based model and Gamma process model to predict the deterioration of each element. The probability of condition state movement for each element thus calculated were used in fault tree analysis to estimate likelihood of failure of a bridge in a given time period. Although the developed method is based on limited data and it has several limitations, model can be further refined with the availability of more inspection reports. The method developed is demonstrated using an inspection report for a timber bridge, which was not used in the development of the models. |
Keywords | timber structures; rehabilitation; Markov process; bridge failure; inspection |
ANZSRC Field of Research 2020 | 400508. Infrastructure engineering and asset management |
400510. Structural engineering | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Centre for Future Materials |
School of Civil Engineering and Surveying | |
Royal Melbourne Institute of Technology (RMIT) | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5158/predicting-the-probability-of-failure-of-timber-bridges-using-fault-tree-analysis
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