Many older bridges and tunnels were constructed using standards by now many decades out-of-date, at a time when trucks and other large vehicles were smaller. A bridge or tunnel strike is an incidence in which a vehicle, typically a lorry (truck) or double-decker bus, tries to pass under a bridge or tunnel that is lower than its height, subsequently colliding with the structure. These strikes lead to an increased cost of bridge repairs, clogged up roadways and increased potential for catastrophic events: hazardous spillage and/or total collapse. Today, Network Rail reports on average a strike every 4.5 hours. There are a number of reasons why strikes occur, and why drivers of heavy goods vehicles sometimes fail to recognise the warning signs, consequently striking the bridge or tunnel. At first glance, it may seem like the problem is a fairly easy one to solve; however, no matter how well planned the road system, human error is an ever-present risk. The research proposes to address the problem of bridge and tunnel strike prevention and management. The intent of the research is to develop an affordable, reliable and robust early warning over-height detection system bridge-owners can implement at locations with high strike occurrences. The research aims to test and validate a novel vision-based system using a single camera to accurately detect over-height vehicles using a set of optimised parameters. The system uses a camera installed at the offending height, which acts as an “over-height plane” formed by the averages of the maximum allowable heights across all lanes in a given traffic direction. Any vehicle exceeding this plane is analysed within a region of interest using a trigger-based approach for accurate detection and driver warning. If the vehicle is deemed to be over-height, a warning is issued to the driver. As a result, prolonging life expectancy of structures while decreasing the cost of repairs, maintenance and inspections.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:744533 |
| Date | January 2018 |
| Creators | Nguyen, Bella |
| Contributors | Brilakis, Ioannis |
| Publisher | University of Cambridge |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.repository.cam.ac.uk/handle/1810/271894 |
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