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dc.contributor.advisorPullan, Wayne
dc.contributor.advisorChai, Wing
dc.contributor.authorCancian, Glen Andrewen_US
dc.date.accessioned2018-01-23T02:45:52Z
dc.date.available2018-01-23T02:45:52Z
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/10072/366768
dc.description.abstractThe issue of effectively scheduling pavement maintenance and rehabilitation treatments over a multi-year planning horizon plagues road authorities around the world with the significance of this issue being amplified by both an ageing pavement network and the trend towards insufficient fund allocation. The scope of the problem can be quantified as follows: if only a single treatment is able to be applied to each individual road segment in a single year, then the total number of possible programmed maintenance and rehabilitation schedule alternatives for a moderate-sized network of 1,000 road segments, with eight different treatments possible, over a twenty year anaysis period is ((1.0 × 103)8)20 = 1.0 × 10480. Assuming that a computer can build and evaluate 100 complete maintenance and rehabilitation schedules a second, to identify the optimal schedule for this 1,000 segment road network would take 3.17 × 10471 years. The overall goal of this study is to investigate the benefits of applying modern heuristic optimisation techniques to the problem of pavement main- tenance and rehabilitation scheduling over a multi-year planning horizon. To address this goal, a four stage approach was utilised using a real road network with real pavement condition data as the test benchmark.en_US
dc.languageEnglishen_US
dc.publisherGriffith Universityen_US
dc.publisher.placeBrisbaneen_US
dc.rights.copyrightThe author owns the copyright in this thesis, unless stated otherwise.en_US
dc.subject.keywordsPavement maintenanceen_US
dc.subject.keywordsRoad maintenanceen_US
dc.titleHeuristic Based Optimisation of Pavement Management Schedulingen_US
dc.typeGriffith thesisen_US
gro.facultyScience, Environment, Engineering and Technologyen_US
gro.hasfulltextFull Text
dc.rights.accessRightsPublicen_US
gro.identifier.gurtIDgu1488847973318en_US
gro.source.ADTshelfnoADT0en_US
gro.source.GURTshelfnoGURTen_US
gro.thesis.degreelevelThesis (PhD Doctorate)en_US
gro.thesis.degreeprogramDoctor of Philosophy (PhD)en_US
gro.departmentGriffith School of Engineeringen_US
gro.griffith.authorCancian, Glen A.


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