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dc.contributor.authorHong, Ying
dc.contributor.authorXie, Hanyan
dc.contributor.authorBrilakis, Ioannis
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.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.subjectConstruction schedules
dc.subjectConstruction sequences
dc.subjectGraph-based classification
dc.titleA graph-based approach for unpacking construction sequence analysis to evaluate schedules
dc.publisher.departmentDepartment of Engineering
prism.publicationNameAdvanced Engineering Informatics: the science of supporting knowledge-intensive activities
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
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
pubs.licence-display-nameApollo Repository Deposit Licence Agreement

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