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dc.contributor.authorLei, Xiang
dc.contributor.supervisorPeng Wuen_US
dc.date.accessioned2024-07-22T04:11:46Z
dc.date.available2024-07-22T04:11:46Z
dc.date.issued2023en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/95544
dc.description.abstract

lRoads are a critical component of transportation infrastructure, and their effective maintenance is paramount in ensuring their continued functionality and safety. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success road management projects, on the other hand improves information management practices in the construction industry.

en_US
dc.publisherCurtin Universityen_US
dc.titleImproving data management through automatic information extraction model in ontology for road asset managementen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Design and the Built Environmenten_US
curtin.accessStatusOpen accessen_US
curtin.facultyHumanitiesen_US
curtin.contributor.orcidLei, Xiang [0000-0003-2838-984X]en_US


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