Show simple item record

dc.contributor.authorHong, Ying
dc.contributor.authorXie, Hanyan
dc.contributor.authorBrilakis, Ioannis
dc.date.accessioned2022-05-02T23:30:27Z
dc.date.available2022-05-02T23:30:27Z
dc.date.issued2022
dc.identifier.issn1474-0346
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336701
dc.description.abstractConstruction schedules can mitigate delay risks and are essential to project success. Yet, creating a quality construction schedule is often the outcome of experienced schedulers, and what makes it harder is the fact that historic information including decision reasoning was not documented and disseminated for future use. This study proposes a graph-based method to find the most time-efficient construction sequence from historic projects to improve scheduling productivity and accuracy. The proposed method captured the textual, numerical, and graphical features of construction schedules, and was validated on 353 construction schedules obtained from a Tier-1 contractor in the UK. The results indicate that earthwork sequences can be finished in 4.0% of the project time on average, but earthwork sequences are the least time-efficient ones in a construction project (29% delayed), particularly in road construction (88% delayed). This study compared the time efficiency of sequences learned from previous projects with case study sequences. Results indicated that frequent sequences learned from past projects are 26.7% closer to the actual schedule than the planned ones. Results of this study could assist inexperienced schedulers to create more quality construction schedules and project managers to benchmark project performances.
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectConstruction schedules
dc.subjectConstruction sequences
dc.subjectGraph-based classification
dc.titleA graph-based approach for unpacking construction sequence analysis to evaluate schedules
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-04-30T15:04:21Z
prism.publicationNameAdvanced Engineering Informatics: the science of supporting knowledge-intensive activities
dc.identifier.doi10.17863/CAM.84124
dcterms.dateAccepted2022-04-26
rioxxterms.versionofrecord10.1016/j.aei.2022.101625
rioxxterms.versionAM
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
dc.identifier.eissn1873-5320
rioxxterms.typeJournal Article/Review
pubs.funder-project-idInnovate UK (104795)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Industrial Leadership (IL) (958398)
cam.orpheus.successWed Jun 08 08:57:17 BST 2022 - Embargo updated
cam.orpheus.counter1
cam.depositDate2022-04-30
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2023-04-01


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International