Using artificial neural network to predict power plant turbine hall key cost drivers

The wave of sudden electricity shortage owing to the economic booms worldwide recently had resulted the tremendous time cut in power plant project development. The usual steps in project life cycle, like bidding time in the procurement process is one of them that have not been spared. Despite it has...

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Main Author:	Ng, Choo Geon
Format:	Thesis
Language:	English
Published:	2007
Subjects:	HD28 Management Industrial Management
Online Access:	http://eprints.utm.my/id/eprint/6123/1/NgChooGeonMFKA2007.pdf
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id	my-utm-ep.6123
record_format	uketd_dc
spelling	my-utm-ep.61232018-08-26T04:43:19Z Using artificial neural network to predict power plant turbine hall key cost drivers 2007-05 Ng, Choo Geon HD28 Management. Industrial Management TA Engineering (General). Civil engineering (General) The wave of sudden electricity shortage owing to the economic booms worldwide recently had resulted the tremendous time cut in power plant project development. The usual steps in project life cycle, like bidding time in the procurement process is one of them that have not been spared. Despite it has been recognised that the current traditional practice in cost estimation of power plant project is reliable but it is also very time consuming. As such, it is clearly imperative need to find alternate approach in preparation of bids, to meet the odds against time pressure. The study has been formulated to address such issue. The main aim of the study is to use Artificial Neural Network (ANN) as the faster alternative method in predicting key quantities for power plant project. However, the study will only focus on the construction of turbine hall section only. These key quantities normally will be priced by vendors in supply chain, subsequently compiled as latest price at bidding time. The 15 years old historical databases of photographs, drawings, as-built bill of quantities, and bids bill of quantities, from renown power plant constructors, have been used to enable the identification of key cost drivers and key parameters in estimating turbine hall and used to train ANN models. As a validation process, the results from the ANN model has been compared with the statistical method of Multi Level Regression (MLR). The result of the study has determined the ANN regression model is reliable and expected can be used by the contractor in the estimating process of turbine hall construction 2007-05 Thesis http://eprints.utm.my/id/eprint/6123/ http://eprints.utm.my/id/eprint/6123/1/NgChooGeonMFKA2007.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:61995 masters Universiti Teknologi Malaysia, Faculty of Civil Engineering Faculty of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	HD28 Management Industrial Management HD28 Management Industrial Management
spellingShingle	HD28 Management Industrial Management HD28 Management Industrial Management Ng, Choo Geon Using artificial neural network to predict power plant turbine hall key cost drivers
description	The wave of sudden electricity shortage owing to the economic booms worldwide recently had resulted the tremendous time cut in power plant project development. The usual steps in project life cycle, like bidding time in the procurement process is one of them that have not been spared. Despite it has been recognised that the current traditional practice in cost estimation of power plant project is reliable but it is also very time consuming. As such, it is clearly imperative need to find alternate approach in preparation of bids, to meet the odds against time pressure. The study has been formulated to address such issue. The main aim of the study is to use Artificial Neural Network (ANN) as the faster alternative method in predicting key quantities for power plant project. However, the study will only focus on the construction of turbine hall section only. These key quantities normally will be priced by vendors in supply chain, subsequently compiled as latest price at bidding time. The 15 years old historical databases of photographs, drawings, as-built bill of quantities, and bids bill of quantities, from renown power plant constructors, have been used to enable the identification of key cost drivers and key parameters in estimating turbine hall and used to train ANN models. As a validation process, the results from the ANN model has been compared with the statistical method of Multi Level Regression (MLR). The result of the study has determined the ANN regression model is reliable and expected can be used by the contractor in the estimating process of turbine hall construction
format	Thesis
qualification_level	Master's degree
author	Ng, Choo Geon
author_facet	Ng, Choo Geon
author_sort	Ng, Choo Geon
title	Using artificial neural network to predict power plant turbine hall key cost drivers
title_short	Using artificial neural network to predict power plant turbine hall key cost drivers
title_full	Using artificial neural network to predict power plant turbine hall key cost drivers
title_fullStr	Using artificial neural network to predict power plant turbine hall key cost drivers
title_full_unstemmed	Using artificial neural network to predict power plant turbine hall key cost drivers
title_sort	using artificial neural network to predict power plant turbine hall key cost drivers
granting_institution	Universiti Teknologi Malaysia, Faculty of Civil Engineering
granting_department	Faculty of Civil Engineering
publishDate	2007
url	http://eprints.utm.my/id/eprint/6123/1/NgChooGeonMFKA2007.pdf
_version_	1747814631376683008

Using artificial neural network to predict power plant turbine hall key cost drivers

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