Neural network model incorporating a genetic algorithm in estimating construction costs

Gwang Hee Kim; Jie Eon Yoon; Sung Hoon An; Hun Hee Cho; Kyung In Kang

doi:10.1016/j.buildenv.2004.03.009

Neural network model incorporating a genetic algorithm in estimating construction costs

Gwang Hee Kim, Jie Eon Yoon^*, Sung Hoon An, Hun Hee Cho, Kyung In Kang

^*Corresponding author for this work

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

119 Citations (Scopus)

Abstract

This paper applies the back-propagation network (BPN) model incorporating genetic algorithms (GAs) to cost estimation. GAs were adopted in the BPN to determine the BPN's parameters and to improve the accuracy of construction cost estimation. Previously, there have been no appropriate rules to determine these parameters. The construction cost data for 530 residential buildings constructed in Korea between 1997 and 2000 were used for training and evaluating the performance of the model. This study showed that a BPN model incorporating a GA was more effective and accurate in estimating construction costs than the BPN model using trial and error.

Original language	English
Pages (from-to)	1333-1340
Number of pages	8
Journal	Building and Environment
Volume	39
Issue number	11
DOIs	https://doi.org/10.1016/j.buildenv.2004.03.009
Publication status	Published - 2004 Nov

Keywords

Construction cost estimating
Genetic algorithms
Neural networks

ASJC Scopus subject areas

Environmental Engineering
Civil and Structural Engineering
Geography, Planning and Development
Building and Construction

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10.1016/j.buildenv.2004.03.009

Cite this

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title = "Neural network model incorporating a genetic algorithm in estimating construction costs",

abstract = "This paper applies the back-propagation network (BPN) model incorporating genetic algorithms (GAs) to cost estimation. GAs were adopted in the BPN to determine the BPN's parameters and to improve the accuracy of construction cost estimation. Previously, there have been no appropriate rules to determine these parameters. The construction cost data for 530 residential buildings constructed in Korea between 1997 and 2000 were used for training and evaluating the performance of the model. This study showed that a BPN model incorporating a GA was more effective and accurate in estimating construction costs than the BPN model using trial and error.",

keywords = "Construction cost estimating, Genetic algorithms, Neural networks",

author = "Kim, \{Gwang Hee\} and Yoon, \{Jie Eon\} and An, \{Sung Hoon\} and Cho, \{Hun Hee\} and Kang, \{Kyung In\}",

year = "2004",

month = nov,

doi = "10.1016/j.buildenv.2004.03.009",

language = "English",

volume = "39",

pages = "1333--1340",

journal = "Building and Environment",

issn = "0360-1323",

publisher = "Elsevier B.V.",

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TY - JOUR

T1 - Neural network model incorporating a genetic algorithm in estimating construction costs

AU - Kim, Gwang Hee

AU - Yoon, Jie Eon

AU - An, Sung Hoon

AU - Cho, Hun Hee

AU - Kang, Kyung In

PY - 2004/11

Y1 - 2004/11

N2 - This paper applies the back-propagation network (BPN) model incorporating genetic algorithms (GAs) to cost estimation. GAs were adopted in the BPN to determine the BPN's parameters and to improve the accuracy of construction cost estimation. Previously, there have been no appropriate rules to determine these parameters. The construction cost data for 530 residential buildings constructed in Korea between 1997 and 2000 were used for training and evaluating the performance of the model. This study showed that a BPN model incorporating a GA was more effective and accurate in estimating construction costs than the BPN model using trial and error.

AB - This paper applies the back-propagation network (BPN) model incorporating genetic algorithms (GAs) to cost estimation. GAs were adopted in the BPN to determine the BPN's parameters and to improve the accuracy of construction cost estimation. Previously, there have been no appropriate rules to determine these parameters. The construction cost data for 530 residential buildings constructed in Korea between 1997 and 2000 were used for training and evaluating the performance of the model. This study showed that a BPN model incorporating a GA was more effective and accurate in estimating construction costs than the BPN model using trial and error.

KW - Construction cost estimating

KW - Genetic algorithms

KW - Neural networks

UR - https://www.scopus.com/pages/publications/3042646757

U2 - 10.1016/j.buildenv.2004.03.009

DO - 10.1016/j.buildenv.2004.03.009

M3 - Article

AN - SCOPUS:3042646757

SN - 0360-1323

VL - 39

SP - 1333

EP - 1340

JO - Building and Environment

JF - Building and Environment

IS - 11

ER -

Neural network model incorporating a genetic algorithm in estimating construction costs

Abstract

Keywords

ASJC Scopus subject areas

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