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Expert System-Based Predictive Cost Model For Building Works: Neural Network Approach

Amusan , L.M and Mosaku, T.O and Ayo, C. K. and Adeboye, J Expert System-Based Predictive Cost Model For Building Works: Neural Network Approach. In: INTERNATIONALCONFERENCE ON SUSTAINABILE DEVELOPMENT AND ENVIRONMENTAL PROTECTION: (STRATEGIES AND PROCEDURE FOR DEVELOPING NATIONS)., 21-22 SEPTEMBER 2010, BELLS DRIVE BELLS UNIVERSITY OF TECHNOLOGY OTA OGUN STATE NIGERIA..

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Abstract

Project managers need accurate estimate of building projects to be able to choose appropriate alternatives for their construction work. Estimated costs of building projects, which hitherto have been based on regression models, are usually left with gaps for high margin of errors and as well, lack the capacity to accommodate certain intervening variables as construction works progress. Data of past construction projects of the past 2 years were adjusted with factored cost escalator buffer (inflation and corruption escalator factor) and used for the study. This model is developed and tested as a predictive cost model for building projects based on Artificial Neural Networks (ANNs). This model will help professionals save time, make more realistic decisions, and help avoid underestimating and overestimating of project costs, which are some of the advantages over previously used Regression models.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Expert-System, Predictive cost, Neural-Network, Cost, Model and Regression.
Subjects: Q Science > Q Science (General)
T Technology > TH Building construction
Divisions: Faculty of Engineering, Science and Mathematics > School of Civil Engineering and the Environment
Depositing User: Mr L. M. Amusan
Date Deposited: 28 Jun 2011 14:39
Last Modified: 27 Nov 2012 14:39
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/309

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