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Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity

Title:

Application of Artificial Neural Network(s) in Predicting Formwork Labour Productivity

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:
  • Concordia Open Access Author Fund
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

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