2005 · 81 citations · 4 references
ManagementConstruction ManagementProject ManagementConstruction EconomicsCost ManagementMachine LearningConstruction EstimatingCost AnalysisCost EstimationCost MeasurementCost PlanningProject Cost ManagementAccuracyArtificial Intelligence In EstimatingConceptual Cost EstimationCost Estimation MethodologyDevelopment Cost EstimationHighway ConstructionHighway EngineeringPreliminary Cost Estimation
Accurate cost estimation of highway projects at the conceptual phase is essential, yet challenges such as limited data, missing information, inadequate cost models, and uncertainty hinder reliable predictions, and traditional regression methods have not reduced errors. This study aims to create a more precise cost estimation technique for highway projects in developing countries during the conceptual phase using artificial neural networks. The authors develop and train an artificial neural network model on available cost data to predict highway project expenses at the conceptual stage.
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.
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