Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation
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 r...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/7081073 |
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| author | Karrar Raoof Kareem Kamoona Cenk Budayan |
| author_facet | Karrar Raoof Kareem Kamoona Cenk Budayan |
| author_sort | Karrar Raoof Kareem Kamoona |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5301fe8ae8f6468ab83f532d17788fca |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-5301fe8ae8f6468ab83f532d17788fca2025-02-03T01:09:01ZengWileyAdvances in Civil Engineering1687-80861687-80942019-01-01201910.1155/2019/70810737081073Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion SimulationKarrar Raoof Kareem Kamoona0Cenk Budayan1Ministry of Electricity, State Company of Electricity Production Al‐Furat Middle Region, Al Najaf Power Plant, Najaf, IraqDepartment of Civil Engineering, Yildiz Technical University, 34220 Esenler, Istanbul, TurkeyIn 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.http://dx.doi.org/10.1155/2019/7081073 |
| spellingShingle | Karrar Raoof Kareem Kamoona Cenk Budayan Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation Advances in Civil Engineering |
| title | Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation |
| title_full | Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation |
| title_fullStr | Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation |
| title_full_unstemmed | Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation |
| title_short | Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation |
| title_sort | implementation of genetic algorithm integrated with the deep neural network for estimating at completion simulation |
| url | http://dx.doi.org/10.1155/2019/7081073 |
| work_keys_str_mv | AT karrarraoofkareemkamoona implementationofgeneticalgorithmintegratedwiththedeepneuralnetworkforestimatingatcompletionsimulation AT cenkbudayan implementationofgeneticalgorithmintegratedwiththedeepneuralnetworkforestimatingatcompletionsimulation |