Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation
Joint Authors
Kareem Kamoona, Karrar Raoof
Budayan, Cenk
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-02
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
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 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 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 is 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.
American Psychological Association (APA)
Kareem Kamoona, Karrar Raoof& Budayan, Cenk. 2019. Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation. Advances in Civil Engineering،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1116936
Modern Language Association (MLA)
Kareem Kamoona, Karrar Raoof& Budayan, Cenk. Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation. Advances in Civil Engineering No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1116936
American Medical Association (AMA)
Kareem Kamoona, Karrar Raoof& Budayan, Cenk. Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation. Advances in Civil Engineering. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1116936
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1116936