Statistical Model for Schedule Prediction: Validation in a Housing-Cooperative Construction Database
Resumen:
There are often considerable differences between the planned schedule for a construction project and what later develops during actual construction. This paper introduces an innovativeapproach that uses MarkovChain models to support predictions during earned value analyses. A statistical model was developed to predict possible deviations in a project schedule and the future progress of a project. This model, based on Markov chains, uses data from the past to adjust future predictions. A case study was built from a database of 90 housing cooperative construction projects and was validated in 12 more projects. A cross validation of three interactions was also carried out, obtaininganerror of 2.38% inthe prediction offuture progressandanerror of 4.29% intheprediction of construction timing.Theinnovative prediction model presented in this paper contributes to the management body of knowledge by introducing a new tool for the management and control of construction timing. The method presented improves construction management because it predicts future deviations in scheduleswithreducederrorsanddeterminestotaldeviationfromaconstructionschedulewithgreatprecision.Thisallowsbettercontroloverwork timing and represents important input in determining strategies and future actions.
| 2017 | |
| Agencia Nacional de Investigación e Innovación | |
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Prediction Schedule Earned schedule Earned value management Cost and schedule Ingeniería y Tecnología Ingeniería Civil Ingeniería de la Construcción |
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| Español | |
| Agencia Nacional de Investigación e Innovación | |
| REDI | |
| http://hdl.handle.net/20.500.12381/213 | |
| Acceso abierto | |
| Reconocimiento 4.0 Internacional. (CC BY) |
| Sumario: | There are often considerable differences between the planned schedule for a construction project and what later develops during actual construction. This paper introduces an innovativeapproach that uses MarkovChain models to support predictions during earned value analyses. A statistical model was developed to predict possible deviations in a project schedule and the future progress of a project. This model, based on Markov chains, uses data from the past to adjust future predictions. A case study was built from a database of 90 housing cooperative construction projects and was validated in 12 more projects. A cross validation of three interactions was also carried out, obtaininganerror of 2.38% inthe prediction offuture progressandanerror of 4.29% intheprediction of construction timing.Theinnovative prediction model presented in this paper contributes to the management body of knowledge by introducing a new tool for the management and control of construction timing. The method presented improves construction management because it predicts future deviations in scheduleswithreducederrorsanddeterminestotaldeviationfromaconstructionschedulewithgreatprecision.Thisallowsbettercontroloverwork timing and represents important input in determining strategies and future actions. |
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