Özet:
In construction project management, there are several factors influencing the final
project cost. Among various approaches, estimate at completion (EAC) is an
essential approach utilized for final project estimation. The main merit of EAC is
including the probability of the project performance and risk. In addition, EAC is
extremely helpful for project managers to define and determine the critical
problems throughout the project progress and determine the appropriate solutions
to these problems. In this research, a relatively new intelligent model called deep
neural network (DNN) is proposed to calculate the EAC. The proposed DNN model
is authenticated against one of the predominated intelligent models conducted on
the EAC prediction, namely support vector regression model (SVR). In order to
demonstrate the capability of the model in the engineering applications, historical
project information obtained from fifteen projects in Iraq region is inspected in this
research. The second phase of this research is about the integration of two input
optimization algorithms hybridized with the proposed and the comparable
predictive intelligent models. These input optimization algorithms are genetic
algorithm (GA) and brute force algorithm (BF). The aim of integrating these input
optimization algorithms to approximate the input attributes and investigate the
highly influenced factors on the calculation of EAC. Overall, the enthusiasm of this
study is to provide a robust intelligent model that estimates the project cost
accurately over the traditional methods. Also, the second aim is to introduce a
reliable methodology that can provide efficient and effective project cost control.
The proposed GA-DNN is demonstrated as a reliable and robust intelligence model
for EAC calculation.
