Application of artificial intelligence and machine learning for BIM

Quality control is an integral component of building information modeling processes in architectural design. At each stage of the facility's life cycle, systematic collection and monitoring of key indicators is required. BIM technology, based on the intensive use of data, involves the use of complex computational methods such as image processing and analysis of large amounts of information. In this context, artificial intelligence and machine learning have proven effective in automating tasks and extracting valuable insights both in Russia and abroad. These technologies also make it possible to predict the need for maintenance and quality control with high accuracy, determining optimal time and spatial parameters. This article analyzes modern approaches to the integration of artificial intelligence and machine learning in architectural design, and discusses the prospects and challenges associated with the implementation of these technologies in architectural design, construction and landscape design. The purpose of the study is to form a comprehensive understanding of the current needs of architecture and the construction industry and the impact of artificial intelligence and machine learning on their development, as well as to identify areas for further scientific research. Special attention is paid to operational systems capable of solving complex tasks and learning from data, which ensures high accuracy in identifying patterns and predicting the life cycle of objects, especially when processing significant amounts of information.