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dc.contributor.authorIbrahim, A.
dc.contributor.authorZukri, N.A.Z.M.
dc.contributor.authorIsmail, B.N.
dc.contributor.authorOsman, M.K.
dc.contributor.authorYusof, N.A.M.
dc.contributor.authorIdris, M.
dc.contributor.authorRabian, A.H.
dc.contributor.authorBahri, Intan
dc.date.accessioned2021-06-29T03:23:33Z
dc.date.available2021-06-29T03:23:33Z
dc.date.issued2021-04-15
dc.identifier.issn1823-5514
dc.identifier.issn2550-164X
dc.identifier.urihttps://hdl.handle.net/10652/5353
dc.description.abstractEffective road maintenance program is vital to ensure traffic safety, serviceability, and prolong the life span of the road. Maintenance will be carried out on pavements when signs of degradation begin to appear and delays may also lead to increased maintenance costs in the future, when more severe changes may be required. In Malaysia, manual visual observation is practiced in the inspection of distressed pavements. Nonetheless, this method of inspection is ineffective as it is more laborious, time consuming and poses safety hazard. This study focuses in utilizing an Artificial Intelligence (AI) method to automatically classify pavement crack severity. Field data collection was conducted to allow meaningful verification of accuracy and reliability of the crack's severity prediction based on AI. Several important phases are required in research methodology processes including data collection, image labelling, image resizing, image enhancement, deep convolution neural network (DCNN) training and performance evaluation. Throughout the analysis of image processing results, the image ouqJUt was successfully classified using MATLAB software. The good agreement between field measurement data and DCNN prediction of crack's severity proved the reliability of the system. In conclusion, the established method can classify the crack's severity based on JKR guideline of visual assessment.en_NZ
dc.language.isoenen_NZ
dc.publisherCollege of Engineering, Universiti Teknologi MARA (UiTM)en_NZ
dc.rights© 2021 College of Engineering, Universiti Teknologi MARA (UiTM), Malaysiaen_NZ
dc.subjectcrack severityen_NZ
dc.subjectroad maintenanceen_NZ
dc.subjectflexible pavementen_NZ
dc.subjectdeep convolution neural network (DCNN)en_NZ
dc.titleFlexible pavement crack's severity identification and classification using deep convolution neural networken_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2021-06-25T14:30:03Z
dc.subject.marsden090507 Transport Engineeringen_NZ
dc.subject.marsden0801 Artificial Intelligence and Image Processingen_NZ
dc.identifier.bibliographicCitationIbrahim, A., Zukri, N.A.Z.M., Ismail, B.N., Osman, M.K., Yusof, N.A.M., Idris, M., Rabian, A.H., & Bahri, I. (2021). Flexible Pavement Crack's Severity Identification and Classification using Deep Convolution Neural Network. Journal of Mechanical Engineering, 18(2), 193-201.en_NZ
unitec.publication.spage193en_NZ
unitec.publication.lpage201en_NZ
unitec.publication.volume18en_NZ
unitec.publication.issue2en_NZ
unitec.publication.titleJournal of Mechanical Engineeringen_NZ
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
dc.contributor.affiliationUiTM Cawangan Pulau Pinang, Malaysiaen_NZ
dc.contributor.affiliationHenry Butcher Maintenance (Malaysia)en_NZ
unitec.identifier.roms66456en_NZ
unitec.publication.placeMalaysiaen_NZ
unitec.institution.studyareaConstruction + Engineeringen_NZ


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