article

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

Authors:

Nibaldo RodriguezAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 39, Issue 9

Pages 7788 - 7795

https://doi.org/10.1016/j.eswa.2012.01.095

Published: 01 July 2012 Publication History

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.

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Cited By

Hashemi SEbadati E. OKaur H(2019)A hybrid conceptual cost estimating model using ANN and GA for power plant projectsNeural Computing and Applications10.1007/s00521-017-3175-531:7(2143-2154)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00521-017-3175-5
Islam MNepal MSkitmore MAttarzadeh M(2017)Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projectsAdvanced Engineering Informatics10.1016/j.aei.2017.06.00133:C(112-131)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.aei.2017.06.001
Tkáč MVerner R(2016)Artificial neural networks in businessApplied Soft Computing10.1016/j.asoc.2015.09.04038:C(788-804)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.09.040

Index Terms

Comparisons between two types of neural networks for manufacturing cost estimation of piping elements
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
  2. Modeling and simulation
    1. Model development and analysis

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 39, Issue 9

July, 2012

920 pages

ISSN:0957-4174

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2012.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 July 2012

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Cited By

Hashemi SEbadati E. OKaur H(2019)A hybrid conceptual cost estimating model using ANN and GA for power plant projectsNeural Computing and Applications10.1007/s00521-017-3175-531:7(2143-2154)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00521-017-3175-5
Islam MNepal MSkitmore MAttarzadeh M(2017)Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projectsAdvanced Engineering Informatics10.1016/j.aei.2017.06.00133:C(112-131)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.aei.2017.06.001
Tkáč MVerner R(2016)Artificial neural networks in businessApplied Soft Computing10.1016/j.asoc.2015.09.04038:C(788-804)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.09.040

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