IJIMT 2018 Vol.9(1): 64-69 ISSN: 2010-0248
doi: 10.18178/ijimt.2018.9.1.789

Evaluation of Critical Success Factors of Construction Projects Using Soft Computing Methods

H. Naderpour, M. Asgari, and A. Kheyroddin
Abstract— Critical success factors of the project (CSFs) will help the employer, contractor and consultant and its users. Artificial neural networks are one of new methods which have been developed to estimate and predict parameters using the inherent relationship among data. In this research, through reviewing the key indicators of project success, CSFs factors among the main elements involved in the industry of macro-civil construction projects (employer, contractor and consultant) a model for determining with the success of the project, it has been tried to propose a model to determine the score of the project success using radial based neural networks. To achieve this goal based on conditions of the present research, firstly, ten CSFs key project success indicators, were recognized in five categories including financial, interaction processes, manpower, contract settings, and characteristic nature of the project. Then, by random sampling of projects operated during the last 5 years in the country's Ministry of Energy, project information was collected by managers of large projects. After training the designed neural network, the success model of the project was provided based on an assessment of project objectives, including factors of Scope, Time, Cost, and Quality of the projects, the applied equation of the model was also presented to facilitate use by other researchers. Outputs were calculated by the proposed model were in good agreement with the actual number of projects.

Index Terms— Construction, critical success factor, project, soft computing.

The authors are with the Semnan University, Semnan, Iran (e-mail: naderpour@semnan.ac.ir, m14202@yahoo.com, kheyroddin@semnan.ac.ir).

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Cite: H. Naderpour, M. Asgari, and A. Kheyroddin, " Evaluation of Critical Success Factors of Construction Projects Using Soft Computing Methods," International Journal of Innovation, Management and Technology vol. 9, no. 1, pp. 64-69, 2018.

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