Formulation of Feature and Label Space Using Modified Delphi in Support of Developing a Machine-Learning Algorithm to Automate Clash Resolution

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dc.contributor.authorHarode, Ashiten
dc.contributor.authorThabet, Waliden
dc.contributor.authorLeite, Fernandaen
dc.date.accessioned2025-03-05T13:25:25Zen
dc.date.available2025-03-05T13:25:25Zen
dc.date.issued2023-12-30en
dc.description.abstractTo improve the current manual and iterative nature of clash resolution on construction projects, current research efforts continue to explore and test the utilization of machine-learning algorithms to automate the process. Though current research shows significant accuracy in automating clash resolution, many have failed to provide clear explanation and justification for the selection of their feature and label space. Since this is critical in developing an effective and explainable solution in machine learning, it is crucial to address this research gap. In this paper, the authors utilize an in-depth literature review and industry interviews to capture domain knowledge on how design clashes are resolved by industry experts. From analysis of the knowledge captured, we identified 23 factors considered by experts when resolving clashes and five alternative solutions/options to resolve a clash. Using a pool of industry experts, a modified Delphi approach was conducted to validate the factors and options and to determine a priority ranking. The authors identified 94 industry experts based on a predetermined qualification matrix to take part in the modified Delphi. Twelve participants responded and took part in the first round, and 11 completed the second round. A consensus was reached on all clash factors and resolution options. Factors including "clashing elements type,""constrained slope,""critical element in the clash,""location of the clash,""code compliance,"and "project stage clashing element is in"were ranked as the most important factors, while "clashing element material"and "insulation type"were considered the least important. Participants also showed more preference to the "moving the clashing element with low priority in/along x-y-z directions"option to resolve clashes. These identified factors and options will be utilized to collect specific clash data to train and test effective and explainable machine-learning algorithms toward automating clash resolution.en
dc.description.versionAccepted versionen
dc.format.extent11 page(s)en
dc.identifierARTN 04023173 (Article number)en
dc.identifier.doihttps://doi.org/10.1061/JCEMD4.COENG-14167en
dc.identifier.eissn1943-7862en
dc.identifier.issn0733-9364en
dc.identifier.issue3en
dc.identifier.orcidThabet, Walid [0000-0001-8832-5317]en
dc.identifier.urihttps://hdl.handle.net/10919/124781en
dc.identifier.volume150en
dc.language.isoenen
dc.publisherASCEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleFormulation of Feature and Label Space Using Modified Delphi in Support of Developing a Machine-Learning Algorithm to Automate Clash Resolutionen
dc.title.serialJournal of Construction Engineering and Managementen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-groupVirginia Techen
pubs.organisational-groupVirginia Tech/Engineeringen
pubs.organisational-groupVirginia Tech/All T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/COE T&R Facultyen
pubs.organisational-groupVirginia Tech/Engineering/Myers-Lawson School of Constructionen

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