ANALYSIS OF THE APPLICATION OF NEURAL NETWORKS FOR FORECASTING THE ECONOMIC INDICATORS OF CONSTRUCTION PROJECTS

Abstract

The article examines the use of neural networks for forecasting key economic indicators in the construction industry. It is shown that using neural network models improves the accuracy of cost and project completion time estimates, facilitates early detection of market changes, reduces risks, and enhances companies’ resilience to economic fluctuations. The advantages of integrating neural networks with other artificial intelligence technologies, BIM systems, digital twins, and IoT data are highlighted. The importance of data quality, the development of explainable AI methods, as well as the necessity of regular model updates and adaptation to a dynamically changing environment are emphasized. In the long term, the implementation of neural network solutions provides construction companies with a sustainable competitive advantage, increases the efficiency of economic planning, and strengthens the strategic quality of management.