Abstract
Ongoing research at The University of Manchester has resulted in the production of ProCost – an early stage building cost estimation tool that has the ability to produce single figure estimates following the description of the proposed building by the user. Recent research has indicated that single figure estimating is not of great use to the estimator and that an elemental cost estimating tool is required within the software’s interface. This realisation initiated the investigation into separating the ProCost output into elements. In order to do that a large database of 360 buildings with known characteristics and elemental costs was compiled. Regression analysis and artificial neural networks techniques were used to model the relationship between the building descriptive variables and their elemental costs. This paper examines the results of the regression analysis and proposes the application of neural networks due to their ability to model non-linear relationships.
Original language | English |
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Title of host publication | Proceedings of the First International Symposium on Commercial Management |
Editors | David Lowe, Margaret Emsley |
Place of Publication | United Kingdom |
Publisher | University of Manchester Institute of Science and Technology (UMIST) |
Pages | 186-194 |
Number of pages | 9 |
ISBN (Print) | 9547918-1-1 |
Publication status | Published - 1 Apr 2005 |
Event | 1st International Symposium on Commercial Management 2005 - Manchester, United Kingdom Duration: 7 Apr 2005 → 7 Apr 2005 |
Conference
Conference | 1st International Symposium on Commercial Management 2005 |
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Country/Territory | United Kingdom |
City | Manchester |
Period | 7/04/05 → 7/04/05 |