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Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

Mojtaba Maghrebi
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  • The University of New South Wales (UNSW), School of Civil and Environmental Engineering, Sydney, Australia
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/ Claude Sammut
  • The University of New South Wales (UNSW), School of Computer Science Engineering Sydney, Australia
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/ Travis S. Waller
  • The University of New South Wales (UNSW), School of Civil and Environmental Engineering, Sydney, Australia
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Published Online: 2014-06-17 | DOI: https://doi.org/10.2478/brj-2014-0001

Abstract

Being able to precisely predict the duration of concrete operations can help construction managers to organize sites and machineries more efficiently, especially when there is limited space for equipment on site. Currently there is no theoretical method for estimating the duration of the concrete pouring process. Normally, the maximum capacity of pumping facilities on construction sites is not used, and concrete pumps are idle for a considerable time as a result of the arrival of concrete trucks being delayed. In the light of this issue, this paper considers the supply chain parameters of Ready Mixed Concrete (RMC) as a means of solving this problem. Artificial Neural Network (ANN) is hired for modelling/predicting the productivity of a concrete operation. The proposed model is tested with a real database of an RMC in the Sydney metropolitan area that has 17 depots and around 200 trucks. Results show that there is an improvement in the achieved results when these are compared to the results of relevant studies that only considered the construction parameters for predicting the productivity of concrete operations

Keywords: RMC; productivity; supply chain

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About the article

Published Online: 2014-06-17

Published in Print: 2014-06-01


Citation Information: Building Research Journal, Volume 61, Issue 1, Pages 1–14, ISSN (Online) 1339-682X, DOI: https://doi.org/10.2478/brj-2014-0001.

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