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dc.contributor.advisorSmith, Simon
dc.contributor.authorCampbell, Jennifer Mary
dc.date.accessioned2009-11-03T11:46:50Z
dc.date.available2009-11-03T11:46:50Z
dc.date.issued2008
dc.identifier.urihttp://hdl.handle.net/1842/3170
dc.description.abstractInfrastructure such as transportation networks improves the condition of everyday lives by facilitating public services and systems necessary for economic activity and growth. However, constructing and maintaining transportation infrastructure poses safety hazards and risks to those working at the sharp end, leading to serious injuries and fatalities. Therefore, the identification of hazards and managing the risks they create is integral towards continually improving safety levels in Infrastructure Management. This work seeks to fully understand this problem and highlight past, present and future issues concerning safety in a comprehensive literature review. A decision support tool is proposed to improve the safety of transportation workers by facilitating hazard identification and management of associated control measures. This Tool facilitates the extraction of safety knowledge from real paper-based safety documents, capturing existing worker’s knowledge and experiences from industrial ‘corporate memory’. The Tool suggests the most appropriate control measures for new scenarios based on existing knowledge from previous work tasks. This is achieved by classifying work tasks using a new method based on unilateral UK legislation (Reporting of Injuries, Diseases and Dangerous Occurrences (1995) Regulations) and the innovative use of Artificial Intelligence method Case Based Reasoning. Case Based Reasoning (CBR) allows transparency in the Tool processes and has many benefits over other safety tools which may suffer from ‘black box’ stigmatism. The Tool is populated with knowledge extracted from a real transportation project and is hosted via the internet (www.Total-Safety.com). The end product of the Tool is the generation of bespoke method statements detailing appropriate control measures. These generated paper documents are shown to have financial and quality control benefits over traditional method statements. The Tool has undergone testing and analysis and is shown to be robust. Finally, the overall conclusions and opportunities for further research are presented and progress of the work against each of the five research objectives is assessed.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.subjectEngineeringen
dc.subjectHealth and Safetyen
dc.titleSafety Hazard and Risk Identification and Management In Infrastructure Managementen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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