Abstract
This technical note applies hybrid models of neural networks (NN) and genetic algorithms (GA) to cost estimation of residential buildings to predict preliminary cost estimates. Data used in the study are for residential buildings constructed from 1997 to 2000 in Seoul, Korea. These are used in training each model and evaluating its performance. The models applied were Model I, which determines each parameter of a back-propagation network by a trial-and-error process; Model II, which determines each parameter of a back-propagation network by GAs; and Model III, which trains weights of NNs using genetic algorithms. The research revealed that optimizing each parameter of back-propagation networks using GAs is most effective in estimating the preliminary costs of residential buildings. Therefore, GAs may help estimators overcome the problem of the lack of adequate rules for determining the parameters of NNs.
| Original language | English |
|---|---|
| Pages (from-to) | 208-211 |
| Number of pages | 4 |
| Journal | Journal of Computing in Civil Engineering |
| Volume | 19 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2005 Apr 1 |
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Keywords
- Algorithms
- Construction industry
- Cost estimates
- Hybrid methods
- Neural networks
ASJC Scopus subject areas
- Civil and Structural Engineering
- Computer Science Applications
- Computational Theory and Mathematics
Cite this
Hybrid models of neural networks and genetic algorithms for predicting preliminary cost estimates. / Kim, G. H.; Seo, D. S.; Kang, Kyung In.
In: Journal of Computing in Civil Engineering, Vol. 19, No. 2, 01.04.2005, p. 208-211.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Hybrid models of neural networks and genetic algorithms for predicting preliminary cost estimates
AU - Kim, G. H.
AU - Seo, D. S.
AU - Kang, Kyung In
PY - 2005/4/1
Y1 - 2005/4/1
N2 - This technical note applies hybrid models of neural networks (NN) and genetic algorithms (GA) to cost estimation of residential buildings to predict preliminary cost estimates. Data used in the study are for residential buildings constructed from 1997 to 2000 in Seoul, Korea. These are used in training each model and evaluating its performance. The models applied were Model I, which determines each parameter of a back-propagation network by a trial-and-error process; Model II, which determines each parameter of a back-propagation network by GAs; and Model III, which trains weights of NNs using genetic algorithms. The research revealed that optimizing each parameter of back-propagation networks using GAs is most effective in estimating the preliminary costs of residential buildings. Therefore, GAs may help estimators overcome the problem of the lack of adequate rules for determining the parameters of NNs.
AB - This technical note applies hybrid models of neural networks (NN) and genetic algorithms (GA) to cost estimation of residential buildings to predict preliminary cost estimates. Data used in the study are for residential buildings constructed from 1997 to 2000 in Seoul, Korea. These are used in training each model and evaluating its performance. The models applied were Model I, which determines each parameter of a back-propagation network by a trial-and-error process; Model II, which determines each parameter of a back-propagation network by GAs; and Model III, which trains weights of NNs using genetic algorithms. The research revealed that optimizing each parameter of back-propagation networks using GAs is most effective in estimating the preliminary costs of residential buildings. Therefore, GAs may help estimators overcome the problem of the lack of adequate rules for determining the parameters of NNs.
KW - Algorithms
KW - Construction industry
KW - Cost estimates
KW - Hybrid methods
KW - Neural networks
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UR - http://www.scopus.com/inward/citedby.url?scp=18144382336&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)0887-3801(2005)19:2(208)
DO - 10.1061/(ASCE)0887-3801(2005)19:2(208)
M3 - Article
AN - SCOPUS:18144382336
VL - 19
SP - 208
EP - 211
JO - Journal of Computing in Civil Engineering
JF - Journal of Computing in Civil Engineering
SN - 0887-3801
IS - 2
ER -