Artificial Intelligence and Semantic Technologies in Architectural and Construction Project Management

The article explores the possibilities of using artificial intelligence (AI) and semantic technologies to automate the processing of construction data and the integration of cost estimates (GESN) into building information modeling (BIM) processes. The authors propose an innovative approach that combines natural language processing (NLP), ontological modeling and machine learning to structure informal technical specifications and automatically generate cost indicators for building structures. The study consists of two interrelated stages, including the analysis of relevant publications in the field under consideration and the confirmation of the hypothesis put forward based on the analysis of the collected samples. As a result of the research, it was found that the use of NLP tools such as spaCy and BERT allows for the efficient extraction of key parameters from text technical specifications, ensuring the transition from a free form of description to a structured presentation of information. The creation of ontologies based on regulatory databases (including GESN) in RDF format opens up the possibility of semantic integration of design solutions with the regulatory framework, increasing the accuracy and consistency of estimate calculations. The use of "clear rules" (rule-based) and machine learning methods for comparing building structures with the corresponding estimate standards contributes to the formation of a more adaptive and intelligent pricing system. Thus, the conducted study reflects the potential of AI to reduce labor costs at the early stages of design and improve the accuracy of estimate calculations. Prospects for the work are associated with the development of digital twins and end-to-end automation of the life cycle of construction projects.