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dc.contributor.authorFitzsimmons, John Patrick
dc.contributor.authorLu, Ruodan
dc.contributor.authorHong, Ying
dc.contributor.authorBrilakis, Ioannis
dc.date.accessioned2021-12-09T00:30:17Z
dc.date.available2021-12-09T00:30:17Z
dc.date.issued2022-01-13
dc.identifier.issn1400-6529
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331275
dc.description.abstract<jats:p>The UK commissions about £100 billion in infrastructure construction works every year. More than 50% of them finish later than planned, causing damage to the interests of stakeholders. The estimation of time-risk on construction projects is currently done subjectively, largely by experience despite there are many existing techniques available to analyse risk on the construction schedules. Unlike conventional methods that tend to depend on the accurate estimation of risk boundaries for each task, this research aims to proposes a hybrid method to assist planners in undertaking risk analysis using baseline schedules with improved accuracy. The proposed method is endowed with machine intelligence and is trained using a database of 293,263 tasks from a diverse sample of 302 completed infrastructure construction projects in the UK. It combines a Gaussian Mixture Modelling-based Empirical Bayesian Network and a Support Vector Machine followed by performing a Monte Carlo risk simulation. The former is used to investigate the uncertainty, correlated risk factors, and predict task duration deviations while the latter is used to return a time-risk simulated prediction. This study randomly selected 10 projects as case studies followed by comparing their results of the proposed hybrid method with Monte Carlo Simulation. Results indicated 54.4% more accurate prediction on project delays.</jats:p>
dc.description.sponsorshipLaing O'Rourke
dc.publisherInternational Council for Research and Innovation in Building and Construction
dc.rightsCreative Commons Attribution 4.0 International
dc.rights.urihttp://www.rioxx.net/licenses/CC BY 4.0
dc.titleConstruction schedule risk analysis – a hybrid machine learning approach
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2021-12-04T12:20:38Z
prism.publicationNameJournal of Information Technology in Construction
dc.identifier.doi10.17863/CAM.78722
dcterms.dateAccepted2021-12-04
rioxxterms.versionofrecord10.36680/j.itcon.2022.004
rioxxterms.versionVoR
dc.contributor.orcidBrilakis, Ioannis [0000-0003-1829-2083]
dc.identifier.eissn1874-4753
rioxxterms.typeJournal Article/Review
pubs.funder-project-idInnovate UK (104795)
cam.issuedOnline2022-01-13
cam.orpheus.success2022-04-28
cam.orpheus.counter6
cam.depositDate2021-12-04
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement


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