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Cost Prediction of Roads Construction Projects Using OLS Regression Method
Escalation of completed cost than contract cost of road construction project causes disputes between road agencies and contractors and lead to deserted or delay the same projects or others projects due to stealing from their budget to overlay the shortfall. Consistent occurrences cause losing credibility and reputation of road agencies in addition to political complications. Therefore, this study aims to build a model to predict the completed cost of road construction projects in the early stage of the project's life cycle to avoid cost overruns. The ordinary least square regression method (OLS) has been applied for cost prediction models based on data extracted from 52 projects that have been obtained from the Roads and Bridges Directorate of Construction and Housing Ministry in Iraq from 2011 to 2019. After projects data are divided for training and testing, then strict assumptions of the OLS method are applied, the optimal model has been developed based on the variables of contract cost (CC), contract time (CT), projects downtime (DT), road length (L) and road width (W) of the projects. The values of the assessment criteria of the optimal model are 0.96 for the coefficient of determination (R2), 0.95 for adjusted R2, and 1897.8 for Schwarz's Bayesian information criterion (BIC). The mean absolute percentage error (MAPE) is 7.93%. These values are indicating that the developed model success in illustrating the pattern between predictor's parameter and project cost and well performance.
Cost Prediction of Roads Construction Projects Using OLS Regression Method
Escalation of completed cost than contract cost of road construction project causes disputes between road agencies and contractors and lead to deserted or delay the same projects or others projects due to stealing from their budget to overlay the shortfall. Consistent occurrences cause losing credibility and reputation of road agencies in addition to political complications. Therefore, this study aims to build a model to predict the completed cost of road construction projects in the early stage of the project's life cycle to avoid cost overruns. The ordinary least square regression method (OLS) has been applied for cost prediction models based on data extracted from 52 projects that have been obtained from the Roads and Bridges Directorate of Construction and Housing Ministry in Iraq from 2011 to 2019. After projects data are divided for training and testing, then strict assumptions of the OLS method are applied, the optimal model has been developed based on the variables of contract cost (CC), contract time (CT), projects downtime (DT), road length (L) and road width (W) of the projects. The values of the assessment criteria of the optimal model are 0.96 for the coefficient of determination (R2), 0.95 for adjusted R2, and 1897.8 for Schwarz's Bayesian information criterion (BIC). The mean absolute percentage error (MAPE) is 7.93%. These values are indicating that the developed model success in illustrating the pattern between predictor's parameter and project cost and well performance.
Cost Prediction of Roads Construction Projects Using OLS Regression Method
Karkush, Mahdi O. (editor) / Choudhury, Deepankar (editor) / Rasheed, Ruqayah H. (author) / Rezouki, Sedqi E. (author)
Geotechnical Engineering and Sustainable Construction ; Chapter: 53 ; 671-680
2022-03-20
10 pages
Article/Chapter (Book)
Electronic Resource
English
Road construction cost , Completed cost prediction , Ordinary least square (OLS) , Regression assumption , Best subset Engineering , Geoengineering, Foundations, Hydraulics , Geotechnical Engineering & Applied Earth Sciences , Transportation Technology and Traffic Engineering , Environment, general , Building Construction and Design
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