Predicting construction delays using machine learning based on historical data on the actual duration of completed projects

V.V. Konkov, V.I. Shirokov, M.G. Zhabitsky

Abstract


This study examines the problem of exceeding planned construction deadlines, which is a significant obstacle to increasing the efficiency of the construction industry and its contribution to the economy of the Russian Federation. As part of the task defined by the development strategy of the construction industry and housing and communal services of Russia for the period until 2030, it is envisaged to reduce the duration of the investment and construction cycle by 30%. However, current planning methods based on outdated regulatory approaches have shown to be ineffective due to inattention to the statistical data of already completed projects.  To solve this problem, the authors proposed a hypothesis about creating a system of recommendations based on the analysis of historical data on the implementation of individual construction works and construction projects. The initial data was anonymized information about the planned and actual deadlines for completing work, processed by methods of exploratory data analysis and machine learning. The results obtained made it possible to identify patterns in the implementation of construction projects and develop a methodology for predicting delays in individual works, aimed at optimizing planning and reducing the duration of construction projects planned for implementation.


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References


Order of the Government of the Russian Federation of October 31, 2022 No. 3268-r

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