Modeling a contractor's markup estimation

Liu, M and Ling, Y Y (2005) Modeling a contractor's markup estimation. Journal of Construction Engineering and Management, 131(4), pp. 391-399. ISSN 0733-9364

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.

Item Type: Article
Uncontrolled Keywords: bids; contractors; fuzzy sets; neural networks
Index terms: fuzzy logic, fuzzy set, estimation, modelling, rationality, interview, artificial neural network, survey, accuracy, neural network, local contractor
Subjects: decision-making and optimization, decision-making and reasoning, professional development, modelling and simulation, financial and cost management, analytical methods, artificial intelligence, data science, data collection methods, practitioner
Topics: Digital Applications, Cost Management, Research Practice, Engineering Principles, Information Management, Roles and Professions
Descriptive scope: 5 PCTEA

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