Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8630
DC FieldValueLanguage
dc.contributor.authorTantele, Elia-
dc.contributor.authorVotsis, Renos-
dc.contributor.authorOnoufriou, Toula-
dc.date.accessioned2016-07-11T08:38:50Z-
dc.date.available2016-07-11T08:38:50Z-
dc.date.issued2014-01-
dc.identifier.citationOpen Journal of Civil Engineering, 2014, vol. 4, pp. 338-352en_US
dc.identifier.issn21643164-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/8630-
dc.description.abstractPreventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology; in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofOpen Journal of Civil Engineeringen_US
dc.rights© Scientific Researchen_US
dc.subjectPreventative maintenanceen_US
dc.subjectCorrosionen_US
dc.subjectGenetic algorithmen_US
dc.subjectOptimizationen_US
dc.subjectReinforced concrete bridgesen_US
dc.subjectSensitivity analysisen_US
dc.titleSensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategiesen_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.4236/ojce.2014.44029en_US
dc.dept.handle123456789/148en
dc.relation.volume4en_US
cut.common.academicyear2014-2015en_US
dc.identifier.spage338en_US
dc.identifier.epage352en_US
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-2666-8044-
crisitem.author.orcid0000-0002-4433-2184-
crisitem.author.orcid0000-0002-3361-1567-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
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