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Prediction Model For Construction Cost And Duration In Jordan

Submitted2008-08-27
Last Update2008-08-31
TitlePrediction Model For Construction Cost And Duration In Jordan
Author(s)Author #1
Author title:
Name: Ayman A. Abu Hammad
Org: Assistant Professor of Construction Engineering and Management, Department of Civil Engineering, College of Engineering, Applied Science University, Amman, Jordan.
Country: Jordan
Email: dr-abuhammad@asu.edu.jo

Author #2
Author title:
Name: Souma M. Alhaj Ali
Org: Assistant Professor, Industrial Engineering Department, The Hashemite University, Zerqa, Jordan.
Country: Jordan
Email: souma_alhajali@hu.edu.jo

Author #3
Author title:
Name: Ghaleb J. Sweis
Org: Assistant Professor, Civil Engineering Department, College of Engineering, University of Jordan, Amman, Jordan
Country: Jordan
Email: gsweis@ju.edu.jo

Author #4
Author title:
Name: Adnan Bashir
Org: Assistant Professor, Industrial Engineering Department, The Hashemite University, Zerqa, Jordan.
Country: Jordan
Email: abashir@hu.edu.jo

Other Author(s)
Contact AuthorAuthor #1
Alt Email: dr-abuhammad@asu.edu.jo
Telephone:
KeywordsPrediction model, Construction projects, Time, Cost, Jordan.
AbstractRisk is mitigated in the course of reliable prediction. A probabilistic model is proposed to predict the risk effects on time and cost of construction projects. Project managers and consultants can use the model in estimating project cost and duration based on historic data. Statistical regression models and sample tests are developed using real data of 140 projects. The research objective is to develop a model to predict project cost and duration based on historic data of similar projects. The model result can be used by project managers in the planning phase to validate the schedule critical path time and project budget. Research methodology is steered per the following progression: i) Conduct nonparametric test for project cost and time performance. ii) Develop generic multiple-regression models to predict project cost and duration using historic performance data. iii) The percent prediction error is statistically analyzed; and found to be substantial; thus, iv) Custom multiple regression models are developed for each project type to obtain statistically reliable results. In conclusion, the 95% point estimation of error margin= �0.035%. Therefore, at a probability of 95%, the proposed model predicts the project cost and duration with a precision of �0.035% of the mean cost and time.
Paperview paper 54.pdf (218KB)

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