Golnaraghi, Sasan, zangenehmadar, zahra 
ORCID: https://orcid.org/0000-0002-9508-4440, Moselhi, Osama and Alkass, Sabah
(2019)
Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity.
Advances in Civil Engineering, 2019
.
pp. 1-11.
ISSN 1687-8086
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Official URL: http://dx.doi.org/10.1155/2019/5972620
Abstract
Productivity is described as the quantitative measure between the number of resources used and the output produced, generally referred to man-hours required to produce the final product in comparison to planned man-hours. Productivity is a key element in determining the success and failure of any construction project. Construction as a labour-driven industry is a major contributor to the gross domestic product of an economy and variations in labour productivity have a significant impact on the economy. Attaining a holistic view of labour productivity is not an easy task because productivity is a function of manageable and unmanageable factors. Compound irregularity is a significant issue in modeling construction labour productivity. Artificial Neural Network (ANN) techniques that use supervised learning algorithms have proved to be more useful than statistical regression techniques considering factors like modeling ease and prediction accuracy. In this study, the expected productivity considering environmental and operational variables was modeled. Various ANN techniques were used including General Regression Neural Network (GRNN), Backpropagation Neural Network (BNN), Radial Base Function Neural Network (RBFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) to compare their respective results in order to choose the best method for estimating expected productivity. Results show that BNN outperforms other techniques for modeling construction labour productivity.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering |
|---|---|
| Item Type: | Article |
| Refereed: | Yes |
| Authors: | Golnaraghi, Sasan and zangenehmadar, zahra and Moselhi, Osama and Alkass, Sabah |
| Journal or Publication: | Advances in Civil Engineering |
| Date: | 2019 |
| Funders: |
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| Digital Object Identifier (DOI): | 10.1155/2019/5972620 |
| ID Code: | 985060 |
| Deposited By: | Krista Alexander |
| Deposited On: | 13 Mar 2019 20:10 |
| Last Modified: | 13 Mar 2019 20:10 |
References:
[1] Z. U. Khan, “Modeling and parameter ranking of construction labor productivity,” Doctoral Dissertation, Concordia University, Montreal, Canada, 2005.[2] W. Yi and A. P. C. Chan, “Critical review of labor productivity research in construction journals,” Journal of Management in Engineering, vol. 30, no. 20, pp. 214–225, 2014.
[3] M. Lu, S. M. AbouRizk, and U. H. Hermann, “Estimating labor productivity using probability inference neural network,” Journal of Computing in Civil Engineering, vol. 14, no. 4, pp. 241–248, 2000.
[4] S. AbouRizk, P. Knowles, and U. R. Hermann, “Estimating labor production rates for industrial construction activities,” Journal of Construction Engineering and Management, vol. 127, no. 6, pp. 502–511, 2001.
[5] O. Moselhi, I. Assem, and K. El-Rayes, “Change orders impact on labor productivity,” Journal of Construction Engineering and Management, vol. 131, no. 3, pp. 354–359, 2005.
[6] A. S. Ezeldin and L. M. Sharara, “Neural networks for estimating the productivity of concreting activities,” Journal of Construction Engineering and Management, vol. 132, no. 6, pp. 650–656, 2006.
[7] S. C. Ok and S. K. Sinha, “Construction equipment productivity estimation using artificial neural network model,” Construction Management and Economics, vol. 24, no. 10, pp. 1029–1044, 2006.
[8] L. Song and S. M. AbouRizk, “Measuring and modeling labor productivity using historical data,” Journal of Construction Engineering and Management, vol. 134, no. 10, pp. 786–794, 2008.
[9] E. L. Oral and M. Oral, “Predicting construction crew productivity by using self organizing maps,” Automation in Construction, vol. 19, pp. 791–797, 2010.
[10] S. Muqeem, M. Idrus, and F. Khamidi, “Construction labor production rates modeling using artificial neural network,” Journal of Information Technology in Construction, vol. 16, pp. 713–725, 2011.
[11] S. Mohammed and A. Tofan, “Neural networks for estimating the ceramic productivity of walls,” Journal of Engineering, vol. 17, no. 2, pp. 200–217, 2011.
[12] F. M. S. AL-Zwainy, H. A. Rasheed, and H. F. Ibraheem, “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, 2012.
[13] O. Moselhi and Z. Khan, “Significance ranking of parameters impacting construction labour productivity,” Construction Innovation, vol. 12, no. 3, pp. 272–296, 2012.
[14] G. Heravi and E. Eslamdoost, “Applying artificial neural networks for measuring and predicting construction-labor productivity,” Journal of Construction Engineering and Management, vol. 141, no. 10, article 04015032, 2015.
[15] G. Aswed, “Productivity estimation model for bricklayer in construction projects using neural network,” Al-Qadisiyah Journal for Engineering Sciences, vol. 9, no. 2, pp. 183–199, 2016.
[16] K. M. El-Gohary, R. F. Aziz, and H. A. Abdel-Khalek, “Engineering approach using ANN to improve and predict construction labor productivity under different influences,” Journal of Construction Engineering and Management, vol. 143, no. 8, article 04017045, 2017.
[17] O. Moselhi, T. Hegazy, and P. Fazio, “Neural networks as tools in construction,” Journal of Construction Engineering and Management, vol. 117, no. 4, pp. 606–625, 1991.
[18] M. T. Musavi, W. Ahmed, K. H. Chan, K. B. Faris, and D. M. Hummels, “On the training of radial basis function classifiers,” Neural Networks, vol. 5, no. 4, pp. 595–603, 1992.
[19] P. Sherrod, “DTREG predictive modeling software,” 2018, http://www.dtreg.com.
[20] S. Chen, X. Hong, and C. J. Harris, Orthogonal Forward Selection for Constructing the Radial Basis Function Network 10 Advances in Civil Engineering with Tunable Nodes, in Lecture Notes in Computer Science, Vol. 3644, 2005.
[21] H.-l. Yip, H. Fan, and Y.-h. Chiang, “Predicting the maintenance cost of construction equipment: comparison between general regression neural network and Box-Jenkins time series models,” Automation in Construction, vol. 38, pp. 30–38, 2014.
[22] D. F. Specht, “Probabilistic neural networks,” Neural Networks, vol. 3, no. 1, pp. 109–118, 1990.
[23] H. Alasha’ary, B. Moghtaderi, A. Page, and H. Sugo, “A neuro–fuzzy model for prediction of the indoor temperature in typical Australian residential buildings,” Energy and Buildings, vol. 41, no. 7, pp. 703–710, 2009.
[24] A. Subasi, A. S. Yilmaz, and H. Binici, “Prediction of early heat of hydration of plain and blended cements using neuro-fuzzy modelling techniques,” Expert Systems with Applications, vol. 36, no. 3, pp. 4940–4950, 2009.
[25] L.-C. Ying and M.-C. Pan, “Using adaptive network based fuzzy inference system to forecast regional electricity loads,” Energy Conversion and Management, vol. 49, no. 2, pp. 205–211, 2008.
[26] M. Negnevitsky, ANFIS: Adaptive Neruo-Fuzzy Inference System, Artificial Intelligence-A Guide to Intelligent Systems, Pearson Education Limited, Essex, UK, 2nd edition, 2005.
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