Abstract

Every decision or action taken as part of a construction project involves risk. Unforeseen branch works that may occur during the construction investment are the so-called additional work. They cause risk, both for the contractor and the investor. Skilful management of this risk may lead to minimizing the change in the investment duration or minimizing the change in the cost of the contractual amount. The work proposes a method of analysing the risk of industrial works that may occur during additional works in railway construction investments. A constructed Bayesian network based on the risk component of industrial works was used for the analysis. Bayesian networks are listed as one of the 31 techniques suggested for risk analysis in accordance with the ISO 31010 standard, which enables the correct analysis of the examined problem with satisfactory accuracy. During the construction of the network, historical data was obtained from completed and settled railway infrastructure construction projects, and 125 unique records corresponding to additional works were identified. The created Bayesian network combines technological aspects resulting from the specificity of the implementation of branch works in railway construction projects with a practical assessment of their risk. The proposed network model allows for risk analysis by defining various event scenarios, and has high application capacity resulting from the ease of applying its results in practice in the implementation of railway investments.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.