Assessment of the Service Life of Buildings and Structures Using Artificial Intelligence

This thesis considers the use of artificial intelligence technologies to assess the technical condition of buildings and structures and to determine their remaining service life. During operation, structural elements of buildings may develop cracks, deformations, corrosion, settlement, and other defects. Traditional assessment methods require considerable time and effort to identify and evaluate such defects. The use of artificial intelligence, particularly computer vision and machine learning algorithms, makes it possible to automate structural health monitoring and predict the remaining service life of structures. The study proposes a general model for detecting defects from images, assessing the technical condition of structures, and predicting their remaining service life.