Comparing Cost Prediction Methods for Apartment Housing Projects: CBR versus ANN

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

doi:10.3130/jaabe.4.113

Comparing Cost Prediction Methods for Apartment Housing Projects: CBR versus ANN

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

School of Civil, Environmental and Architectural Engineering

Research output: Contribution to journal › Article › peer-review

21 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 language	English
Pages (from-to)	113-120
Number of pages	8
Journal	Journal of Asian Architecture and Building Engineering
Volume	4
Issue number	1
DOIs	https://doi.org/10.3130/jaabe.4.113
Publication status	Published - 2005

Keywords

Apartment house
Artificial neural networks
Case-based reasoning
Cost estimation

ASJC Scopus subject areas

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

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10.3130/jaabe.4.113

Cite this

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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.",

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T1 - Comparing Cost Prediction Methods for Apartment Housing Projects

T2 - CBR versus ANN

AU - Kim, Sang Yong

AU - Choi, Jae Won

AU - Kim, Gwang Hee

AU - Kang, Kyung In

PY - 2005

Y1 - 2005

N2 - 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.

AB - 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.

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Comparing Cost Prediction Methods for Apartment Housing Projects: CBR versus ANN

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Keywords

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