Integration of artificial intelligence and bim technologies to ensure regulatory compliance and energy efficiency of buildings - FEDORCHENKO, Mykola (Ways to Improve Construction Efficiency, 2025)
The modern development of the construction industry is characterized by increasing demands for quality, energy efficiency, and compliance with regulatory standards, which necessitates the deep digitalization of design and management processes. The integration of Artificial Intelligence (AI) and Building Information Modeling (BIM) technologies establishes a new data management architecture, where design, technical, structural, and energy information are unified into a single intelligent system. This approach enables the automation of regulatory compliance verification, energy efficiency forecasting, and real-time decision-making processes. AI serves as a key analytical tool capable of processing large datasets from BIM models and identifying deviations from design or regulatory parameters. The use of machine learning algorithms allows for predicting building behavior under various operational scenarios, assessing the influence of climatic and structural factors, and proposing optimal architectural and technological solutions. As a result, a dynamic digital environment is formed, where artificial intelligence becomes the core of a self-learning management system, and the BIM model serves as its informational foundation. The use of the digital twin concept ensures a comprehensive representation of a building’s energy, technical, and spatial characteristics. This enables simulations, the analysis of design alternatives, energy loss calculations, and verification of compliance with state and international standards (DBN, ISO, EN). Such an approach provides architects, engineers, and facility managers with a powerful tool for making informed decisions based on real-world data. The combination of AI and BIM not only enhances energy efficiency but also fosters a transition to intelligent design, where the system can autonomously detect inefficiencies, adjust models, and recommend optimal structural parameters. This results in reduced time and financial costs, minimized design risks, and improved quality of architectural and construction documentation.