Analogy‐Based Solution to Markup Estimation Problem

Abstract

This paper presents a methodology for deriving analogy‐based solutions to a class of unstructured problems in civil engineering. Such problems have identifiable characteristics, including: (1) Problems frequently require simultaneous assessment of a large number of quantitative as well as qualitative factors that influence the solution; (2) traditional algorithmic and reasoning‐intensive techniques are not adequate to model the problem; (3) solutions are devised in practice primarily based on analogy with previous cases coupled with a mixture of intuition and experience; and (4) domain knowledge is mostly implicit and very difficult to be extracted and described. For this class of problems, artificial neural networks (ANNs) are most suited for developing decision aids with analogy‐based problem‐solving capabilities. A methodology is presented and used to develop a practical model for markup estimation using knowledge acquired from contractors in Canada and the U.S. The model design, training, and testing are described along with the generalization improvements made using the genetic algorithms technique.