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dc.contributor.convenorKICEM and NTUen_US
dc.contributor.authorSon, Jungen_US
dc.contributor.authorLee, Jaehoen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorLoo, Yew-Chayeen_US
dc.contributor.authorGuan, Hongen_US
dc.contributor.authorPanuwatwanich, Kriengsaken_US
dc.contributor.editorSoon Wook Kwanen_US
dc.date.accessioned2017-05-03T14:06:20Z
dc.date.available2017-05-03T14:06:20Z
dc.date.issued2009en_US
dc.identifier.refuriwww.iccem-iccpm.orgen_US
dc.identifier.urihttp://hdl.handle.net/10072/31920
dc.description.abstractBridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherKICEM (Korea Institute of Construction Engineering and Management)en_US
dc.publisher.placeJeju, South Koreaen_US
dc.publisher.urihttps://www.kicem.or.kr/en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencenameICCEM-ICCPM 2009en_US
dc.relation.ispartofconferencetitleICCEM-ICCPM 2009en_US
dc.relation.ispartofdatefrom2009-05-27en_US
dc.relation.ispartofdateto2009-05-30en_US
dc.relation.ispartoflocationJeju, South Koreaen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchInfrastructure Engineering and Asset Managementen_US
dc.subject.fieldofresearchcode090505en_US
dc.titleImproving reliability of bridge deterioration model using generated missing condition ratingsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2015-06-02T05:43:32Z
gro.hasfulltextNo Full Text


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