Volume 1, Issue 3, October 2016, Page: 39-48
Application Intelligent Predicting Technologies in Construction Productivity
Faiq Mohammed Sarhan Al-Zwainy, Department of Civil Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq
Ali Abed-Alla. Eiada, Building and Construction Engineering Department, University of Technology, Baghdad, Iraq
Tareq Abed-Almajed. Khaleel, Building and Construction Engineering Department, University of Technology, Baghdad, Iraq
Received: Sep. 5, 2016;       Accepted: Sep. 19, 2016;       Published: Oct. 10, 2016
DOI: 10.11648/j.ajetm.20160103.13      View  2208      Downloads  62
Abstract
In this paper, it’s reviewed the concept and methods of measuring productivity construction and the most important factors affecting on productivity. In addition, the most important applications of techniques (Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and support vector machine techniques (SVM)) in the construction productivity field. Most of the previous studies are interested in identifying the factors affecting the construction productivity so as to achieve control and improve construction productivity and find a mathematical model to estimation construction productivity. Use several techniques to analyze the data of which was used to Identify factors affecting such as (relative importance, quantitative engineering project scope definition, Severity index, sensitivity analysis), and to use them for the development of predictive models such as (Linear Regression, Fuzzy models, Support Vector Machine and Artificial Neural Network).
Keywords
Labor Productivity, Multiple Linear Regressions (MLR), Artificial Neural Network (ANN), Support Vector Machine Techniques (SVMT)
To cite this article
Faiq Mohammed Sarhan Al-Zwainy, Ali Abed-Alla. Eiada, Tareq Abed-Almajed. Khaleel, Application Intelligent Predicting Technologies in Construction Productivity, American Journal of Engineering and Technology Management. Vol. 1, No. 3, 2016, pp. 39-48. doi: 10.11648/j.ajetm.20160103.13
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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