Problems of external information in developing design and construction documentation for transport infrastructure using artificial intelligence

The paper demonstrates the prospects for applying artificial intelligence in construction. However, despite the significant potential of artificial intelligence for optimising planning, resource allocation, and risk management, its implementation is associated with serious information-related risks. The study examines key groups of external information required in transport infrastructure design (historical and archival data, regulatory standards, environmental indicators, socio-economic metrics, etc.) and identifies typical challenges in processing such data using artificial intelligence. These include insufficient or poor-quality training data, excessive data noise, lack of contextual understanding in artificial intelligence models, the phenomenon of hallucinations (generation of untrustworthy information). Measures to mitigate information risks are proposed, including data quality control, expert verification of results, staff training, clear formulation of model objectives, improvement of training data quality, use of data templates, limitation of response ranges. Particular attention is paid to legal aspects, including the new national standard GOST R 71718-2024, which regulates the use of artificial intelligence in construction. In conclusion, the paper outlines the prospects for the evolution artificial intelligence’s role in design — from a tool for automatic decision-making to an assistant, and further to an autonomous agent capable of coordinating multidisciplinary projects and optimising resource use.

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