Abstract
Cost estimation of highway projects with high accuracy at the conceptual phase of project development is crucial for planning and feasibility studies. However, a number of difficulties arise when conducting cost estimation during the conceptual phase. Major problems faced are lack of preliminary information, lack of database of road works costs, data missingness, lack of an appropriate cost estimation methods, and the involvement of uncertainties. Given its significance, conventional tools such as regression analysis have been widely employed to tackle the problem. However, recent statistical studies show that errors in cost estimation have not decreased. This paper focuses on the development of a more accurate estimation technique for highway projects in developing countries at the conceptual phase using artificial neural networks.
