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