Estimating construction labor productivity for concreting activity using artificial neural network

Arazi Idrus; Sana Muqeem; Mohd Faris Khamidi; M. Saiful Bin Zakariya

doi:10.1115/1.859926.paper80

Estimating construction labor productivity for concreting activity using artificial neural network

Arazi Idrus^*, Sana Muqeem, Mohd Faris Khamidi, M. Saiful Bin Zakariya

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Due to insufficient standard productivity measurement system, the Construction labor productivity has been declining over a decade. In addition, the influences of various qualitative factors on labor productivity have not been incorporated accurately during the scheduling and estimation of the project durations. Therefore the objective of the study is to estimate the labor production rates by using Artificial Neural Network (ANN). Qualitative factors influencing the rates such as weather, project location, site conditions, etc. have been identified on project sites during the measurement of production rates values for concreting activities. Data obtained from seven building project sites have been used in the ANN for estimating labor production rates. The results obtained with the least error can be used as reliable and valid production rates for the Malaysian construction industry.

Original language	English
Title of host publication	Proceedings of the 2011 International Conference on Computer and Computational Intelligence, December 2-3, 2011, Bangkok, Thailand
Editors	Yi Xie
Publisher	American Society of Mechanical Engineers
Pages	507-511
Number of pages	5
ISBN (Print)	9780791859926
DOIs	https://doi.org/10.1115/1.859926.paper80
Publication status	Published - 2011
Event	International Conference on Computer and Computational Intelligence - Bangkok, Thailand Duration: 2 Dec 2011 → 3 Dec 2011

Conference

Conference	International Conference on Computer and Computational Intelligence
Abbreviated title	ICCCI 2011
Country/Territory	Thailand
City	Bangkok
Period	2/12/11 → 3/12/11

Keywords

Production rates
Influencing factors
Artificial Neural Networks

Access to Document

10.1115/1.859926.paper80

Cite this

Idrus, A., Muqeem, S., Khamidi, M. F., & Bin Zakariya, M. S. (2011). Estimating construction labor productivity for concreting activity using artificial neural network. In Y. Xie (Ed.), Proceedings of the 2011 International Conference on Computer and Computational Intelligence, December 2-3, 2011, Bangkok, Thailand (pp. 507-511). American Society of Mechanical Engineers. https://doi.org/10.1115/1.859926.paper80

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title = "Estimating construction labor productivity for concreting activity using artificial neural network",

abstract = "Due to insufficient standard productivity measurement system, the Construction labor productivity has been declining over a decade. In addition, the influences of various qualitative factors on labor productivity have not been incorporated accurately during the scheduling and estimation of the project durations. Therefore the objective of the study is to estimate the labor production rates by using Artificial Neural Network (ANN). Qualitative factors influencing the rates such as weather, project location, site conditions, etc. have been identified on project sites during the measurement of production rates values for concreting activities. Data obtained from seven building project sites have been used in the ANN for estimating labor production rates. The results obtained with the least error can be used as reliable and valid production rates for the Malaysian construction industry.",

keywords = "Production rates, Influencing factors, Artificial Neural Networks",

author = "Arazi Idrus and Sana Muqeem and Khamidi, {Mohd Faris} and {Bin Zakariya}, {M. Saiful}",

year = "2011",

doi = "10.1115/1.859926.paper80",

language = "English",

isbn = "9780791859926",

pages = "507--511",

editor = "Yi Xie",

booktitle = "Proceedings of the 2011 International Conference on Computer and Computational Intelligence, December 2-3, 2011, Bangkok, Thailand",

publisher = "American Society of Mechanical Engineers",

address = "United States",

note = "International Conference on Computer and Computational Intelligence, ICCCI 2011 ; Conference date: 02-12-2011 Through 03-12-2011",

}

Idrus, A, Muqeem, S, Khamidi, MF & Bin Zakariya, MS 2011, Estimating construction labor productivity for concreting activity using artificial neural network. in Y Xie (ed.), Proceedings of the 2011 International Conference on Computer and Computational Intelligence, December 2-3, 2011, Bangkok, Thailand. American Society of Mechanical Engineers, pp. 507-511, International Conference on Computer and Computational Intelligence, Bangkok, Thailand, 2/12/11. https://doi.org/10.1115/1.859926.paper80

Estimating construction labor productivity for concreting activity using artificial neural network. / Idrus, Arazi; Muqeem, Sana; Khamidi, Mohd Faris et al.
Proceedings of the 2011 International Conference on Computer and Computational Intelligence, December 2-3, 2011, Bangkok, Thailand. ed. / Yi Xie. American Society of Mechanical Engineers, 2011. p. 507-511.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Estimating construction labor productivity for concreting activity using artificial neural network

AU - Idrus, Arazi

AU - Muqeem, Sana

AU - Khamidi, Mohd Faris

AU - Bin Zakariya, M. Saiful

PY - 2011

Y1 - 2011

N2 - Due to insufficient standard productivity measurement system, the Construction labor productivity has been declining over a decade. In addition, the influences of various qualitative factors on labor productivity have not been incorporated accurately during the scheduling and estimation of the project durations. Therefore the objective of the study is to estimate the labor production rates by using Artificial Neural Network (ANN). Qualitative factors influencing the rates such as weather, project location, site conditions, etc. have been identified on project sites during the measurement of production rates values for concreting activities. Data obtained from seven building project sites have been used in the ANN for estimating labor production rates. The results obtained with the least error can be used as reliable and valid production rates for the Malaysian construction industry.

AB - Due to insufficient standard productivity measurement system, the Construction labor productivity has been declining over a decade. In addition, the influences of various qualitative factors on labor productivity have not been incorporated accurately during the scheduling and estimation of the project durations. Therefore the objective of the study is to estimate the labor production rates by using Artificial Neural Network (ANN). Qualitative factors influencing the rates such as weather, project location, site conditions, etc. have been identified on project sites during the measurement of production rates values for concreting activities. Data obtained from seven building project sites have been used in the ANN for estimating labor production rates. The results obtained with the least error can be used as reliable and valid production rates for the Malaysian construction industry.

KW - Production rates

KW - Influencing factors

KW - Artificial Neural Networks

U2 - 10.1115/1.859926.paper80

DO - 10.1115/1.859926.paper80

M3 - Conference contribution

SN - 9780791859926

SP - 507

EP - 511

BT - Proceedings of the 2011 International Conference on Computer and Computational Intelligence, December 2-3, 2011, Bangkok, Thailand

A2 - Xie, Yi

PB - American Society of Mechanical Engineers

T2 - International Conference on Computer and Computational Intelligence

Y2 - 2 December 2011 through 3 December 2011

ER -

Idrus A, Muqeem S, Khamidi MF, Bin Zakariya MS. Estimating construction labor productivity for concreting activity using artificial neural network. In Xie Y, editor, Proceedings of the 2011 International Conference on Computer and Computational Intelligence, December 2-3, 2011, Bangkok, Thailand. American Society of Mechanical Engineers. 2011. p. 507-511 doi: 10.1115/1.859926.paper80

Estimating construction labor productivity for concreting activity using artificial neural network

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