Using Multivariable Linear Regression Technique for Modeling Productivity Construction in Iraq

Faiq Mohammed Sarhan Al-Zwainy, Mohammed Hashim Abdulmajeed, Hadi Salih Mijwel Aljumaily

Department of Civil Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq.
Department of Environmental Engineering, College of Engineering, Al-Mustansiriya University, Baghdad, Iraq.
Ministry Industry and Minerals, State Industrial Design and Consultation Company (Sidcco), Baghdad, Iraq.

DOI: 10.4236/ojce.2013.33015                

Abstract

Productivity is a very important element in the process of construction project management especially with regard to the estimation of the duration of the construction activities, this study aims at developing construction productivity estimating model for marble finishing works of floors using Multivariable Linear Regression technique (MLR). The model was developed based on 100 set of data collected in Iraq for different types of projects such as residential, commercial and educational projects. Which these are used in developing the model and evaluating its performance. Ten influencing factors are utilized for productivity forecasting by MLR model, and they include age, experience, number of the assist labor, height of the floor, size of the marbles tiles, security conditions, health status for the work team, weather conditions, site condition, and availability of construction materials. One model was built for the prediction of the productivity of marble finishing works for floors. It was found that MLR have the ability to predict the productivity for finishing works with excellent degree of accuracy of the coefficient of correlation (R) 90.6%, and average accuracy percentage of 96.3%. This indicates that the relationship between the independent and independent variables of the developed models is good and the predicted values from a forecast model fit with the real-life data.

Keywords

Multivariable Linear Regression Techniques; Construction Productivity; Finishing Work; Coefficient of Correlation

Conflicts of Interest

The authors declare no conflicts of interest.

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