Rapidly changing climates and increased traffic significantly impact on the health of structures in our road network. Catastrophic failures are becoming increasingly common; Northern Ireland alone witnessed 5 bridge failures in August 2017. In a time of reduced government funding and uncertain European support, a transfer from the current reactive-based method of maintenance to more reliable predictive capabilities is critical. An investigation into bridge management systems across the UK road network identified alarming disparities. Existing systems are focused specifically on the collection of data, rather than interpretation. There is little consideration given to the interdependency of assets and the cascading effects of changing traffic patterns and extreme incidents, such as flooding, bridge collapse, terrorist attacks. This paper provides a review of the current bridge management systems in operation including details of a number of current research based system developments. The paper introduces a potential new approach to bridge asset management which will be implemented across the Northern Ireland road network in collaboration with the Northern Ireland Department of Infrastructure.
|Title of host publication||Irish Transport Research Network 05/09/2019 → 06/09/2019 Belfast, United Kingdom|
|Publication status||Published - 05 Sep 2019|
|Event||Irish Transport Research Network - Queen's University Belfast, Belfast, United Kingdom|
Duration: 05 Sep 2019 → 06 Sep 2019
|Conference||Irish Transport Research Network|
|Period||05/09/2019 → 06/09/2019|
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Development of a time-synchronised multi-input computer vision system for structural monitioring utilising deep learning for vehicle identificationAuthor: Lydon, D., Jul 2020
Student thesis: Doctoral Thesis › Doctor of PhilosophyFile