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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, A. B. Expert System-Based Predictive Cost Model For Building Works: Neural Network Approach. Project Report. Unpublished. (Unpublished)

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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: Monograph (Project Report)
Subjects: T Technology > TH Building construction
Divisions: Faculty of Engineering, Science and Mathematics > School of Civil Engineering and the Environment
Depositing User: Mr Adewole Adewumi
Date Deposited: 19 Jun 2011 01:08
Last Modified: 17 Dec 2012 16:21
URI: http://eprints.covenantuniversity.edu.ng/id/eprint/167

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