0
  • DE
  • EN
  • FR
  • Internationale Datenbank und Galerie für Ingenieurbauwerke

Anzeige

Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

Autor(en):

Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Australasian Journal of Construction Economics and Building, , n. 3, v. 13
Seite(n): 51-64
DOI: 10.5130/ajceb.v13i3.3363
Abstrakt:

Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective (LCCA) comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE); and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver). The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.5130/ajceb.v13i3.3363.
  • Über diese
    Datenseite
  • Reference-ID
    10338650
  • Veröffentlicht am:
    05.08.2019
  • Geändert am:
    05.08.2019
 
Structurae kooperiert mit
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine