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

| Peer-Reviewed

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2016. Published by Science Publishing Group

Keywords

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

References

[1]	Abdul Kadir, M., Lee, W., Jaafar, M., Sapuan, S. and Ali, A. (2005). “Factors Affecting Construction Labour Productivity for Malaysian Residential Projects”. Structural Survey. 2005. Vol. 23, no. 1, p. 42-54. DOI 10.1108/02630800510586907. Emerald.
[2]	Allison, L. Huang; Robert, E. Chapman; and David T. Butry, (2009), “Metrics and Tools for Measuring Construction Productivity, Technical and Empirical Considerations”, US Department of Commerce, National Institute of Standards and Technology.
[3]	Al-Zwainy, F. M., (2012). “the use of Artificial Neural Networks for Productivity Estimation of Finishing Stone Works for Building Projects”. Journal of Engineering and Development, Vol. 16, no. 2, pp. 42-60.
[4]	Al-Zwainy, F. M., Hatem A Rasheed, Huda F Ibraheem, (2012). “Development of the Construction Productivity Estimation Model Using Artificial Neural Network for Finishing Works For Floors With Marble”. ARPN Journal of Engineering and Applied Sciences. Vol. 7, No. 6, pp. 714-722.
[5]	Al-Zwainy, F. M., Abdulmajeed, M. and Aljumaily, H. (2013). “Using Multivariable Linear Regression Technique for Modeling Productivity Construction in Iraq”. Open Journal of Civil Engineering. Vol. 03, no. 03, pp. 127-135. DOI 10.4236/ojce.2013.33015. Scientific Research Publishing, Inc.
[6]	Al-Zwainy, F. M., (2014). “Development of the Mathematical Model for Predicating the Construction Productivity in IRAQ Using the Artificial Neural Perceptron Network”. Journal of Engineering and Development, Vol.18, no. 2, pp. 1-21.
[7]	Al-Zwainy F. and Neran T. Hadhal (2016). “Building a Mathematical Model for Predicting the Cost of the Communication Towers Projects Using Multifactor Linear Regression Technique”. International Journal of Construction Engineering and Management, Vol. 05, no. 1, pp 25-29.
[8]	Al-Zwainy F. and Saja H. Rasool (2016). “Estimating Productivity of Brickwork item using Logistic and Multiple Regression Approaches”. Scholars Journal of Engineering and Technology (SJET), Vol. 04, No. 5, pp 234-243.
[9]	Building Futures Council, BFC (2006) “Measuring productivity and Evaluating Innovation in the U.S. construction industry”, Building Futures. Council, Alexandria, Va.
[10]	Chan, P. and Kaka, A. (2007) “Productivity Improvements: Understand the Workforce Perceptions of Productivity First” Personnel Review. Vol. 36, no. 4, p p. 564-584. DOI 10.1108/00483480710752803. Emerald.
[11]	Choi, K., Haque, M., Lee, H., Cho, Y. and Kwak, Y. (2012) “Macroeconomic Labour Productivity and Its Impact on Firm's Profitability”, Journal of the Operational Research Society, Vol. 64, no. 8, pp. 1258-1268. DOI 10.1057/jors.2012.157. Springer Nature.
[12]	Dawood, S. (2002) “Standard Productivity of Labor in The Implementation Of Reinforced Concrete Structures of the Buildings and the Factors Influencing Them”, a thesis submitted to the Civil Engineering Department Project Management, University of Technology – Iraq, MSc.
[13]	Dai, J., Goodrum, P. M. and Maloney, W. F. (2009) “Construction Craft Workers’ Perceptions of the Factors Affecting their Productivity”. Journal of Construction Engineering and Management. Vol. 135, no. 3, pp. 217-226. DOI 10.1061/(asce)0733-9364(2009)135:3(217). American Society of Civil Engineers (ASCE).
[14]	Dozzi, S. P., and AbouRizk, S. M. (1993) “Productivity in Construction", Ottawa, ON: Institute for Research in Construction, National Research Council.
[15]	Ellis, R. D., and Lee, S. (2006) “Measuring Project Level Productivity on Transportation Projects”, Journal of Construction Engineering and Management. Vol. 132, no. 3, p. 314-320. DOI 10.1061/(asce)0733-9364(2006)132:3(314). American Society of Civil Engineers (ASCE).
[16]	Ezeldin, A. and Sharara, L. (2006) “Neural Networks for Estimating the Productivity of Concreting Activities”, Journal of Construction Engineering and Management, 132(6), pp. 650-656.
[17]	Graham, D. and Smith, S. (2004) “Estimating the Productivity of Cyclic Construction Operations Using Case-Based Reasoning”. Advanced Engin- eering Informatics. Vol. 18, no. 1, p. 17-28. DOI10.1016/j.aei.2004.03.001. Elsevier BV.
[18]	Park, Hee-Sung, Thomas, Stephen R. and Tucker, Richard L., (2005), “Benchmarking of Construction Productivity”. Journal of Construction Engineering and Management. 2005. Vol. 131, no. 7, p. 772-778. DOI 10.1061/(asce)07339364(2005)131:7(772). American Society of Civil Engineers (ASCE)
[19]	Heravi, G. and Eslamdoost, E. (2015) “Applying Artificial Neural Networks for Measuring and Predicting Construction-Labor Productivity”, Journal of Construction Engineering and Management. 2015. Vol. 141, no. 10, pp. 04015032,1-11. DOI 10.1061/(asce)co.1943-7862.0001006. American Society of Civil Engineers (ASCE)
[20]	Hickson, B. and Ellis, L. (2014) “Factors affecting Construction Labour Productivity in Trinidad and Tobago” The Journal of the Association of Professional engineers of Trinidad and Tobago Vol. 42. no 1, pp 4-11.
[21]	HM Treasury. (2000). “Productivity in the UK: The Evidence and the Government's Approach” Published: November 2000. Available from: http://webarchive.nationalarchives.gov.uk/+/http:/www.hmtreasury.gov.uk/ent_prodevi_index.htm
[22]	Humphreys, K. Kenneth, (1984), “Project and Cost Engineers' Handbook”, book 2nd ed, Marcel Dekker, Inc., New York.
[23]	Humphreys, Kenneth King, 1991, “Jelen's Cost and Optimization Engineering”. book 3rd ed, New York: McGraw-Hill.
[24]	Khaleel T., (2015) “Development of the Artificial Neural Network Model for Prediction of Iraqi Expressways Construction Cost”, International Journal of Civil Engineering and Technology, Vol.6.no10, pp. 62-76.
[25]	Lema, N. M. (1995) “Construction of Labour Productivity Modeling”, University of Dar Elsalaam. Journal of Project Management, Vol. 16, no. 2, pp. 107-113
[26]	Mady, M. (2013) “Prediction Model of Construction Labor Production Rates in Gaza Strip using Artificial Neural Networks”, a thesis submitted to the Civil Engineering Department Project Management, The Islamic University – Gaza, MSc, pp. 9-11.
[27]	Mahfouz T. (2012). “A Productivity Decision Support System For Construction Projects Through Machine Learning (Ml)” Proceedings of the CIB W78 2012: 29th International Conference –Beirut, Lebanon, 17-19 October.
[28]	Michael, J. M., and Sami, Q., (2000), “System Thinking and Construction Productivity”, Joint research between Civil engineering, University of Nottingham U. K. Department of Civil Engineering and Architecture of University Bahrain.
[29]	Mirahadi F. and Zayed T. (2016). “Simulation-Based Construction Productivity Forecast Using Neural-Network-Driven Fuzzy Reasoning” Automation in Construction. Vol. 65, p. 102-115. DOI10.1016/j. autcon.2015.12.02 1. Elsevier BV.
[30]	Moselhi, Osama, Assem, Ihab and El-Rayes, Khaled, 2005, “Change Orders Impact on Labor Productivity”. Journal of Construction Engineering and Management. 2005. Vol. 131, no. 3, p. 354-359. DOI 10.1061/(asce) 0733-9364(2005)131:3(354). American Society of Civil Engineers (ASCE)
[31]	OECD. (2001), “Measurement of Aggregate and Industry-Level Productivity Growth”, In Measuring productivity, OECD manual.
[32]	Oglesby, C. H., Parker, H. W. and Howell, G. A. (1989) “Productivity Improvement in Construction”. Book, New York: McGraw-Hill.
[33]	Park, H., Thomas, S. R., and Tucker, R. L. (2005) “Benchmarking of Construction Productivity.” Journal of Construction Engineering and Management. 2005. Vol. 131, no. 7, p. 772-778. DOI 10.1061/(asce)0733-9364(2005)131:7(772). American Society of Civil Engineers (ASCE).
[34]	Petruseva, S., Zileska, V. and Zujo, V. (2013) “Predicting construction project Duration With Support Vector Machine”. International Journal of Research in Engineering and Technology. Vol. 02, no. 11, p. 12-24. DOI 10.15623/ijret.2013.0211003. eSAT Publishing House.
[35]	Rojas, E. M. and Aramvareekul, P. (2003) “Labor Productivity Drivers and Opportunities in the Construction Industry”. Journal of Management in Engineering.Vol. 19, no. 2, p. 78-82.DOI10.1061/(asce)0742-597x(2003)19:2 (78). American Society of Civil Engineers (ASCE)
[36]	Smith, A. (1987), “Measuring on Site Production”. Transaction of the American Association of Cost Engineers, 311t annual meeting, Atlanta, U.S.A.
[37]	Song, L. and AbouRizk, S. (2008) “Measuring and Modeling Labor Productivity Using Historical Data”. Journal of Construction Engineering and Management. 2008. Vol. 134, no. 10, p. 786-794. DOI 10.1061/(asce)0733-9364(2008)134: 10(786). American Society of Civil Engineers (ASCE).
[38]	Telsang M., (2000) “Industrial Engineering and Production Management”, S. Chand & Company LTD. Ram Nager, New Delhi.
[39]	Thomas, A. and Sudhakumar, J. (2014) “Factors Influencing Construction Labour Productivity: An Indian Case Study”. Journal of Construction in Developing Countries, 19 (1), pp. 53–68.
[40]	Thomas, H. R. (2009) “Construction learning Curves”. Practice Periodical on Structural Design and Construction, 14 (1), 14-20.
[41]	Tsehayae, Abraham Assefa and Fayek, Aminah Robinson, 2016, “System Model for Analyzing Construction Labour Productivity”. Construction Innovation. 2016. Vol. 16, no. 2, p. 203-228. DOI 10.1108/ci-07-2015-0040. Emerald.
[42]	Ulubeyli, S., Kazaz, A. and Er, B. (2014) “Planning Engineers’ Estimates on Labor Productivity: Theory and Practice”, Procedia - Social and Behavioral Sciences, 119, pp. 12-19.
[43]	Yan, K. and Shi, C. (2010) “Prediction of Elastic Modulus of Normal and High Strength Concrete By Support Vector Machine”, Construction and Building Materials, Science Direct, Elsevier, 24(8), pp. 1479-1485.
[44]	Zakeri, M., Olomolaige, P. O., Holt, G. D., and Harris, F. C. (1996), “A Survey of Construction on Iranian Construction Operative’s Productivity” Construction Management and Economic, Vol. 14, pp. 417-26.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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