Application Intelligent Predicting Technologies in Construction Productivity

Faiq Mohammed Sarhan Al-Zwainy; Ali Abed-Alla. Eiada; Tareq Abed-Almajed. Khaleel

doi:doi:10.11648/j.ajetm.20160103.13

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Application Intelligent Predicting Technologies in Construction Productivity

Faiq Mohammed Sarhan Al-Zwainy, Ali Abed-Alla. Eiada, Tareq Abed-Almajed. Khaleel

Published in American Journal of Engineering and Technology Management (Volume 1, Issue 3)

Received: 5 September 2016 Accepted: 19 September 2016 Published: 10 October 2016

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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).

Published in	American Journal of Engineering and Technology Management (Volume 1, Issue 3)
DOI	10.11648/j.ajetm.20160103.13
Page(s)	39-48
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Labor Productivity, Multiple Linear Regressions (MLR), Artificial Neural Network (ANN), Support Vector Machine Techniques (SVMT)

References

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APA Style

Faiq Mohammed Sarhan Al-Zwainy, Ali Abed-Alla. Eiada, Tareq Abed-Almajed. Khaleel. (2016). Application Intelligent Predicting Technologies in Construction Productivity. American Journal of Engineering and Technology Management, 1(3), 39-48. https://doi.org/10.11648/j.ajetm.20160103.13

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ACS Style

Faiq Mohammed Sarhan Al-Zwainy; Ali Abed-Alla. Eiada; Tareq Abed-Almajed. Khaleel. Application Intelligent Predicting Technologies in Construction Productivity. Am. J. Eng. Technol. Manag. 2016, 1(3), 39-48. doi: 10.11648/j.ajetm.20160103.13

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AMA Style

Faiq Mohammed Sarhan Al-Zwainy, Ali Abed-Alla. Eiada, Tareq Abed-Almajed. Khaleel. Application Intelligent Predicting Technologies in Construction Productivity. Am J Eng Technol Manag. 2016;1(3):39-48. doi: 10.11648/j.ajetm.20160103.13

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@article{10.11648/j.ajetm.20160103.13,
  author = {Faiq Mohammed Sarhan Al-Zwainy and Ali Abed-Alla. Eiada and Tareq Abed-Almajed. Khaleel},
  title = {Application Intelligent Predicting Technologies in Construction Productivity},
  journal = {American Journal of Engineering and Technology Management},
  volume = {1},
  number = {3},
  pages = {39-48},
  doi = {10.11648/j.ajetm.20160103.13},
  url = {https://doi.org/10.11648/j.ajetm.20160103.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajetm.20160103.13},
  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).},
 year = {2016}
}

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TY  - JOUR
T1  - Application Intelligent Predicting Technologies in Construction Productivity
AU  - Faiq Mohammed Sarhan Al-Zwainy
AU  - Ali Abed-Alla. Eiada
AU  - Tareq Abed-Almajed. Khaleel
Y1  - 2016/10/10
PY  - 2016
N1  - https://doi.org/10.11648/j.ajetm.20160103.13
DO  - 10.11648/j.ajetm.20160103.13
T2  - American Journal of Engineering and Technology Management
JF  - American Journal of Engineering and Technology Management
JO  - American Journal of Engineering and Technology Management
SP  - 39
EP  - 48
PB  - Science Publishing Group
SN  - 2575-1441
UR  - https://doi.org/10.11648/j.ajetm.20160103.13
AB  - 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).
VL  - 1
IS  - 3
ER  -

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Author Information

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

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APA Style

Faiq Mohammed Sarhan Al-Zwainy, Ali Abed-Alla. Eiada, Tareq Abed-Almajed. Khaleel. (2016). Application Intelligent Predicting Technologies in Construction Productivity. American Journal of Engineering and Technology Management, 1(3), 39-48. https://doi.org/10.11648/j.ajetm.20160103.13

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ACS Style

Faiq Mohammed Sarhan Al-Zwainy; Ali Abed-Alla. Eiada; Tareq Abed-Almajed. Khaleel. Application Intelligent Predicting Technologies in Construction Productivity. Am. J. Eng. Technol. Manag. 2016, 1(3), 39-48. doi: 10.11648/j.ajetm.20160103.13

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AMA Style

Faiq Mohammed Sarhan Al-Zwainy, Ali Abed-Alla. Eiada, Tareq Abed-Almajed. Khaleel. Application Intelligent Predicting Technologies in Construction Productivity. Am J Eng Technol Manag. 2016;1(3):39-48. doi: 10.11648/j.ajetm.20160103.13

Copy | Download

@article{10.11648/j.ajetm.20160103.13,
  author = {Faiq Mohammed Sarhan Al-Zwainy and Ali Abed-Alla. Eiada and Tareq Abed-Almajed. Khaleel},
  title = {Application Intelligent Predicting Technologies in Construction Productivity},
  journal = {American Journal of Engineering and Technology Management},
  volume = {1},
  number = {3},
  pages = {39-48},
  doi = {10.11648/j.ajetm.20160103.13},
  url = {https://doi.org/10.11648/j.ajetm.20160103.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajetm.20160103.13},
  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).},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Application Intelligent Predicting Technologies in Construction Productivity
AU  - Faiq Mohammed Sarhan Al-Zwainy
AU  - Ali Abed-Alla. Eiada
AU  - Tareq Abed-Almajed. Khaleel
Y1  - 2016/10/10
PY  - 2016
N1  - https://doi.org/10.11648/j.ajetm.20160103.13
DO  - 10.11648/j.ajetm.20160103.13
T2  - American Journal of Engineering and Technology Management
JF  - American Journal of Engineering and Technology Management
JO  - American Journal of Engineering and Technology Management
SP  - 39
EP  - 48
PB  - Science Publishing Group
SN  - 2575-1441
UR  - https://doi.org/10.11648/j.ajetm.20160103.13
AB  - 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).
VL  - 1
IS  - 3
ER  -

Copy | Download