Article

Received 14.04.2024

Revised 18.08.2024

Accepted 27.09.2024

Retrieved from Iss. 116, P. 1, 2024

Pages 248 -258

Sokolova, N., & Kozynets, V. (2024). EVALUATION OF THE EFFECTIVENESS OF MODERN METHODS FOR DIAGNOSING THE OPERATIONAL CONDITION OF HIGHWAYS USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES. Automobile Roads and Road Construction, (116.1), 248-258. https://doi.org/10.33744/0365-8171-2024-116.1-248-258

EVALUATION OF THE EFFECTIVENESS OF MODERN METHODS FOR DIAGNOSING THE OPERATIONAL CONDITION OF HIGHWAYS USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Natalia Sokolova Vladislav Kozynets

The purpose of the article is to assess the effectiveness of modern methods of diagnosing the operational condition of roads and the possibilities of their improvement using artificial intelligence technologies. The dynamics of the length of public roads in Ukraine for 2017-2022 is considered, which revealed insignificant changes in the total length of paved roads and partial fluctuations in the growth rate. The analysis of the road surface structure showed the predominance of asphalt-concrete roads, while other types of pavement have a small share. A further analysis of the distribution of roads by category showed a higher share of category III roads, which indicates a significant number of medium quality roads that require regular maintenance. The study also examined modern methods of diagnosing the condition of roads, including comparison of indicators with standard values, qualitative assessment of each parameter, and assessment by a generalized indicator. The advantages and disadvantages of each method were analyzed. A detailed review of the most common road diagnostic devices, such as deflectometers, deflectometers and visual inspection units used to determine the strength, flatness, roughness and other operational qualities of the pavement, was also conducted. Based on the analysis, it is proposed to introduce artificial intelligence technologies to improve the accuracy of road condition assessment and automate diagnostic processes. Artificial intelligence is able to integrate data from various sources, including sensors, cameras and satellites, and create complex models to predict pavement wear and determine the optimal timing of repairs. The introduction of such innovations will significantly increase the efficiency of road infrastructure management, reduce maintenance costs and improve safety for road users. As a result, the development of artificial intelligence technologies and their application in road diagnostics is becoming an important area for improving the quality of road infrastructure and ensuring the sustainable development of the transport network

roads, road operational condition, road infrastructure management, diagnostic methods, pavement strength, artificial intelligence, process automation
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https://doi.org/10.33744/0365-8171-2024-116.1-248-258