Capital construction projects management based on Bayesian networks integrated into the complexity model

Abstract

This article presents the author's results of the developments to assess the likelihood of problems when managing capital construction projects based on Bayesian networks. The research aim was to study the management decision-making procedure in framework the implementation of investment and construction projects, as complex models. The article relevance arises in connection with an increase in the percentage of capital projects that have not been fully implemented. As a rule, the main reasons for canceling or suspending construction projects are the lack of constant investment, delays in preparing design estimates, problems with connecting ready-to-use facilities to engineering systems, and the lack of a clear technology for developing and monitoring contracts. The article proposes an approach for occurrence probability predicting of problem situations in the framework of capital construction projects, based on the integration of the project complexity model and the method for assessing problem areas using Bayesian networks. The developed approach for analyzing the future project state on the basis available statistical data on analog facilities was tested on the example of construction a hotel chain.