Modeling a Contractor’s Markup Estimation
by Min Liu, (Research Assistant, Dept. of Building, National Univ. of Singapore, 4 Architecture Dr., Singapore 117566; current address, Univ. of California at Berkeley, 2519 Ridge Rd., Berkeley, CA 94709. E-mail: liumin@berkeley.edu) and Yean Yng Ling, (Associate Professor, Dept. of Building, National Univ. of Singapore, 4 Architecture Dr., Singapore 117566. E-mail: bdglyy@nus.edu.sg)
Journal of Construction Engineering and Management, Vol. 131, No. 4, April 2005, pp. 391-399, (doi 10.1061/(ASCE)0733-9364(2005)131:4(391))
Download fulltext
Purchase Subscription
Permissions for Reuse 
View Issue Table of Contents
| Document type: |
Journal Paper |
| Discussion: |
by Joseph P. Connolly (See full record)
|
| Discussion: |
by Min Wu E-mail: drminwu@yahoo.com.au and et al. (See full record)
|
| Closure: | (See full record)
|
| 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. |
|
