Predictive analytics for ensuring the autonomy of urban infrastructure socially significant elements

Moscow State University of Civil Engineering, Yaroslavskoye shosse, 26, Moscow, 129337, Russia

^* Corresponding author: AdamtsevichLA@gmail.com

Abstract

The article is devoted to the study of publications in the field of using predictive analytics in the construction industry, as well as to ensure the autonomy of urban infrastructure elements using Industry 4.0 technologies. The materials for the study were publications presented in the international database Scopus in the period from 2017 to 2022. It was revealed that the most popular publications relate mainly to the issues of substantiating the cost of investments in construction, predicting the properties of reinforced concrete and concrete structures, using information modeling technologies in integration with machine learning models, including as part of the design of capital construction projects, etc. However, there are no publications considering the use of Industry 4.0 technologies and predictive analytics to ensure the autonomy of socially significant elements of the urban infrastructure or even capital construction projects. In this regard, the issue of determining the sufficiency and completeness of the data that needs to be collected and processed to identify critical deviations of the system and ensure the autonomy of socially significant elements of the urban infrastructure by comparing the reference model of the operation of an object or its elements and measurements collected from the system in the mode real time.