Web-based conceptual cost estimates for construction projects using Evolutionary Fuzzy Neural Inference Model

Abstract

Conceptual cost estimates, the basis of project evaluation, engineering design, cost budgeting, and cost management, not only play an essential role in construction project feasibility studies, but are fundamental to a project's ultimate success. As practiced today, construction cost estimates generally rely on experts' intuitive experience. Scientific methods should be developed and employed during project planning and design stages in order to raise conceptual cost estimate accuracy. This study proposes the use of an artificial intelligence approach, the Evolutionary Fuzzy Neural Inference Model (EFNIM), to improve cost estimation accuracy. The advantages inherent in Genetic Algorithms, Fuzzy Logic and Neural Networks are incorporated into the EFNIM, making this model highly applicable to identifying optimal solutions for complex problems. Furthermore, this paper presents Evolutionary Web-based Conceptual Cost Estimators (EWCCE) obtained by integrating EFNIM, WWW, and historical construction data to assist in project management. The developed EWCCE provides two kinds of estimators that can be deployed to estimate conceptual construction cost more precisely during the early stages of projects.