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The main objective of this book is to develop models
using multivariable regression and artificial neural
network approaches for cost estimation of the
construction costs of trackworks of light rail transit and metro projects at the early stages of
the construction process. These two approaches were
applied to a data set of 16 projects by using
seventeen parameters available at the early design
phase.
According to the results of each method, 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 is slightly higher than the regression error.
As a result, two successful cost estimation models
have been developed within the scope of this study.
These models can be beneficial while taking the
decision in the tender phase of projects that
includes trackworks.
using multivariable regression and artificial neural
network approaches for cost estimation of the
construction costs of trackworks of light rail transit and metro projects at the early stages of
the construction process. These two approaches were
applied to a data set of 16 projects by using
seventeen parameters available at the early design
phase.
According to the results of each method, 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 is slightly higher than the regression error.
As a result, two successful cost estimation models
have been developed within the scope of this study.
These models can be beneficial while taking the
decision in the tender phase of projects that
includes trackworks.