Predicting construction schedule performance with last planner system and machine learning

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dc.contributor.authorLagos, Camilo I.
dc.contributor.authorHerrera, Rodrigo F.
dc.contributor.authorCawley, Alejandro F. Mac
dc.contributor.authorAlarcon, Luis F.
dc.date.accessioned2025-01-20T16:10:13Z
dc.date.available2025-01-20T16:10:13Z
dc.date.issued2024
dc.description.abstractConstruction project schedules deviate due to uncertainty and variability unless timely actions are implemented. While the limitations of traditional management practices help to facilitate such assessments have been widely covered, quantitative LPS research has shown empirical relations between its indicators, performance, and outcome. This paper creates a model to predict the schedule outcome during early execution using the LPS metrics and the Design Science approach. 18 solution artifacts were evaluated to predict three schedule outcome variables using 15 indicators in 1464 sample points collected from nine subsequent execution intervals from 164 projects. The selected artifact predicted the schedule outcome, which is a combination of the schedule performance at planned completion and the actual schedule deviation at completion, with a MAE of 1.24% and R2 = 0.96 averaged across the nine execution intervals, using solely LPS indicators. The model can be applied as an early warning mechanism in LPS IT-Support software.
dc.description.funderAgencia Nacional de Inves-tigacio <acute accent>
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.autcon.2024.105716
dc.identifier.eissn1872-7891
dc.identifier.issn0926-5805
dc.identifier.urihttps://doi.org/10.1016/j.autcon.2024.105716
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90197
dc.identifier.wosidWOS:001299302400001
dc.language.isoen
dc.revistaAutomation in construction
dc.rightsacceso restringido
dc.subjectSchedule
dc.subjectLast planner system
dc.subjectMachine learning
dc.subjectPrediction
dc.subjectDesign science research
dc.subject.ods17 Partnerships for the Goals
dc.subject.ods09 Industry, Innovation and Infrastructure
dc.subject.odspa17 Alianzas para lograr los objetivos
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titlePredicting construction schedule performance with last planner system and machine learning
dc.typeartículo
dc.volumen167
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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