Comparing Cost Prediction Methods for Apartment Housing Projects

CBR versus ANN

Sang Yong Kim, Jae Won Choi, Gwang Hee Kim, Kyung In Kang

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Prediction of the cost estimation of apartment house is an important task in the management of construction projects. This study aims at illustrating the compared results of the application of two different approaches which are used case-based reasoning (CBR) and artificial neural networks (ANN) techniques. This study is conducted by using the same 540 cases which are obtained in Korea. 30 cases among the data are used for testing. Testing error rates of 3.69% in the CBR and 6.52% in the ANN were obtained. Results showed that CBR can produce slightly more accurate results and achieve higher computational efficiency than ANN. If the use of CBR and ANN is understood better, as a result, cost estimation can be predicted with reasonability, all parties involved in the construction process could save considerable money.

Original languageEnglish
Pages (from-to)113-120
Number of pages8
JournalJournal of Asian Architecture and Building Engineering
Volume4
Issue number1
DOIs
Publication statusPublished - 2005

Fingerprint

Case based reasoning
apartment
neural network
housing
Neural networks
costs
Costs
Apartment houses
apartment house
Testing
Computational efficiency
Korea
money
efficiency
Prediction
Case-based Reasoning
Artificial Neural Network
Apartment
management

Keywords

  • Apartment house
  • Artificial neural networks
  • Case-based reasoning
  • Cost estimation

ASJC Scopus subject areas

  • Architecture
  • Civil and Structural Engineering
  • Cultural Studies
  • Building and Construction
  • Arts and Humanities (miscellaneous)

Cite this

Comparing Cost Prediction Methods for Apartment Housing Projects : CBR versus ANN. / Kim, Sang Yong; Choi, Jae Won; Kim, Gwang Hee; Kang, Kyung In.

In: Journal of Asian Architecture and Building Engineering, Vol. 4, No. 1, 2005, p. 113-120.

Research output: Contribution to journalArticle

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