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Mathematical-neural Model for Assessing Productivity of Earthmoving Machinery

Author(s):

Medium: journal article
Language(s): Latvian
Published in: Journal of Civil Engineering and Management, , n. 1, v. 13
Page(s): 47-54
DOI: 10.3846/13923730.2007.9636418
Abstract:

Many construction processes are carried out by machines working together and forming technological systems, eg earthmoving machinery made up of excavators and haulers (trucks). Productivity (W(N) ) is a key to valuate the process design purposes. The paper presents the results obtained by applying artificial neural networks to predict productivity (W(N),S ) for earthmoving machinery systems, consisting of c excavators and N haulers. Experimentally determined productivity values can form a standard basis for designing construction earthworks. Possessing the data set consisting of the technical parameters of earthmoving machinery systems and the corresponding productivities for different output hauling distances, one can train artificial neural networks and use subsequently for the reliable prediction of W(N),S .

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/13923730.2007.9636418.
  • About this
    data sheet
  • Reference-ID
    10363252
  • Published on:
    12/08/2019
  • Last updated on:
    12/08/2019
 
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