Neural Network Model for Parametric Cost Estimation of Highway Projects

• Published in: Journal of construction engineering and management Vol. 124; no. 3; pp. 210 - 218
• Main Authors: Hegazy, Tarek, Ayed, Amr
• Format: Journal Article
• Language: English
• Published: Reston, VA American Society of Civil Engineers 01.05.1998
• Subjects: Applied sciences; Building economics. Cost; Buildings. Public works; Exact sciences and technology; Project management. Process of design; Road construction. Pavements. Maintenance; TECHNICAL PAPERS; Transportation infrastructure; Construction cost; Case study; Canada; Sensitivity analysis; Simulation; Computer software; Neural networks; Road construction; Project management; Modeling; Optimization; Genetic algorithms
• ISSN: 0733-9364; 1943-7862
• DOI: 10.1061/(ASCE)0733-9364(1998)124:3(210)

This paper uses a neural network (NN) approach to effectively manage construction cost data and develop a parametric cost-estimating model for highway projects. Eighteen actual cases of highway projects constructed in Newfoundland, Canada, have been used as the source of cost data. Rather than using black-box NN software, a simple NN simulation has been developed in a spreadsheet format that is customary to many construction practitioners. As an alternative to NN training, two techniques were used to determine network weights: (1) simplex optimization; and (2) genetic algorithms (GAs). Accordingly, the weights that produced the best cost prediction for the historical cases were used to find the optimum NN. To facilitate the use of this NN on new projects, a user-friendly interface was developed using spreadsheet macros to simplify user input and automate cost prediction. For practicality, sensitivity analysis and adaptation modules have also been incorporated to account for project uncertainty and to reoptimize the model on new historical data. Details regarding model development and capabilities have been discussed in an attempt to encourage practitioners to benefit from the NN technique.