Hybrid Models of Neural Networks and Genetic Algorithms for Predicting Preliminary Cost Estimates

Autor:	Kyung In Kang, Dongkwon Seo, Gwang-Hee Kim
Rok vydání:	2005
Předmět:	Mathematical optimization Cost estimate Artificial neural network Operations research Process (engineering) Computer science Estimator Technical note Train Computer Science Applications Civil and Structural Engineering
Zdroj:	Journal of Computing in Civil Engineering. 19:208-211
ISSN:	1943-5487 0887-3801
DOI:	10.1061/(asce)0887-3801(2005)19:2(208)
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3692742cd95e461c1fa88b0b015bd6ac https://doi.org/10.1061/(asce)0887-3801(2005)19:2(208) Zobrazit plný text záznamu Plný text ve formátu PDF