Conceptual Cost Estimation Model for Engineering Services in Public Construction Projects

Abstract

Cost estimation for public projects includes, but is not limited to, construction costs and engineering services costs. The available cost estimation models for these projects focus on the construction phase, with little or no consideration given to engineering services. This paper presents an artificial neural network model for the conceptual cost estimation of engineering services for public construction projects that considers both design costs and construction supervision costs. In developing the model, the authors first identify the factors that influence the cost of engineering services, and then apply a suitable artificial neural network for a cost estimation model. The model predicts the cost of engineering services as a percentage of construction cost based on project type, engineering services category, project location, and project scope. The model is trained on a data set obtained from the Governmental Tenders Department in Jordan, and then tested on some core data samples that had not been seen by the network during training. The optimal network for the model was selected by conducting a systematic search among a large number of networks with different network architectures and parameter values. The findings demonstrate that the model is able to predict the cost of engineering services with an acceptable performance for conceptual cost estimation models. The present model complements existing models that focus on construction cost estimating by adding in the cost of engineering services. The authors expect that this work will contribute to supporting public construction planners in the early development of total cost estimates for public construction projects.