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

Auteur(s):



Médium: article de revue
Langue(s): en 
Publié dans: Advances in Civil Engineering, , v. 2019
Page(s): 1-11
DOI: 10.1155/2019/5972620
Abstrait:

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.

Copyright: © 2019 Sasan Golnaraghi et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

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    sur cette fiche
  • Reference-ID
    10263655
  • Publié(e) le:
    03.01.2019
  • Modifié(e) le:
    02.06.2021