Modeling a Contractor’s Markup Estimation
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VIEW THE REPLYPublication: Journal of Construction Engineering and Management
Volume 131, Issue 4
Abstract
The estimation of markup is a difficult process for contractors in a changeable and uncertain construction environment. In this study, a fuzzy logic-based artificial neural network (ANN) model, called the fuzzy neural network (FNN) model, is constructed to assist contractors in making markup decisions. With the fuzzy logic inference system integrated inside, the FNN model provides users with a clear explanation to justify the rationality of the estimated markup output. Meanwhile, with the self-learning ability of ANN, the accuracy of the estimation results is improved. From a survey and interview with local contractors, the factors that affect markup estimation and the rules applied in the markup decision are identified. Based on the finding, both ANN and FNN models were constructed and trained in different project scenarios. The comparison of the two models shows that FNN will assist contractors with markup estimation with more accurate results and convincing user-defined linguistic rules inside.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 131 • Issue 4 • April 2005
Pages: 391 - 399
Copyright
© 2005 ASCE.
History
Received: Dec 6, 2001
Accepted: Jun 10, 2004
Published online: Apr 1, 2005
Published in print: Apr 2005
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