Neural Network Model for Parametric Cost Estimation of Highway Projects

TLDR

The study develops a parametric cost‑estimating model for highway projects using a neural network approach. Using cost data from 18 Newfoundland highway projects, the authors built a spreadsheet‑based neural network, trained it with simplex optimization and genetic algorithms, and created a user‑friendly interface with macros, sensitivity analysis, and adaptation modules. The resulting model’s capabilities are presented to demonstrate its practicality and encourage adoption by construction practitioners.

Abstract

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.