Parametric cost estimation system for light rail transit and metro trackworks

The main objective of this work is to develop early cost estimation models for light rail transit and metro trackworks using the multivariable regression and artificial neural network approaches. These two approaches were applied to a data set of 16 projects by using 17 parameters available at the early design phase. The regression analysis estimated the cost of testing samples with an error of 2.32%. On the other hand, artificial neural network estimated the cost with 5.76% error, which was slightly higher than the regression error. As a result, two successful cost estimation models have been developed depending on the findings of this paper. These models can effectively be utilized in the tender decision-making phase of projects with trackworks.