Success Critical Factors on the Civil Construction Projects Management, Utilizing Artificial Neural Networks

Abstract

AbstractThis study aims to create a model, which determines the most influencers SCFs (Success Critical Factors) on the civil construction project management, utilizing artificial neural networks (ANNs). The usage of ANNs to originate a SCFs determining model in the Civil Construction Industry appears as the differential of this study, since it was observed, in the literature, the absence of studies which investigate extremely dynamic phenomenons similar as SCFs. The PRISMA Method was utilized for the questionnaire elaboration and the answers analysis was performed, initially, by the Relative Importance Index and, posteriorly, by the ANN usage with the Resilient Propagation algorithm. The most critical factor was “Unrealistic Inspection and Test Methods Proposed in Contract”, on the project management area. ANNs provide insights, which allows to know the adopted input variables relevance, and are efficient on the knowledge transferring, being characterized as a fast and accurate method of SCF identification. In theory and considering their interdependence, by proposing the most impactful SCF determination in projects management, the research provides important information, focused on processes improvement actions, in the project area. In a practical sight, the analysis contributes, in an applied way, with the project managers, since any civil construction company can use the resulting management information system.KeywordsGarson algorithmResilient propagationPMBOK guideSuccess