Comparisons between two types of neural networks for manufacturing cost estimation of piping elements

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Abstract

The objective of this paper is to develop and test a model of manufacturing cost estimating of piping elements during the early design phase through the application of artificial neural networks (ANN). The developed model can help designers to make decisions at the early phases of the design process. An ANN model would allow obtaining a fairly accurate prediction, even when enough and adequate information is not available in the early stages of the design process. The developed model is compared with traditional neural networks and conventional regression models. This model proved that neural networks are capable of reducing uncertainties related to the cost estimation of shell and tube heat exchangers.

Original languageEnglish
Pages (from-to)7788-7795
Number of pages8
JournalExpert Systems with Applications
Volume39
Issue number9
DOIs
StatePublished - 1 Jul 2012

Keywords

  • Cost estimation
  • Multi layer perceptron
  • Neural networks
  • Piping
  • Radial basis function

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