Modern construction demands the integration of advanced technologies to enhance the efficiency of structural design and performance. One critical factor influencing the stability and durability of buildings is the selection of pile foundations optimized for specific soil conditions. This article explores the application of artificial intelligence (AI) methods to automate and optimize the process of selecting pile foundations. The study proposes the integration of machine learning algorithms and deep neural networks to analyze geotechnical data, such as soil composition, load-bearing capacity, and water saturation levels. The article outlines the stages of AI model development, including data collection, preprocessing, training, and testing of algorithms. Special attention is given to the development of a predictive model capable of accounting for the geographic and climatic specifics of the construction region. The findings demonstrate that AI significantly reduces the time required for engineering calculations while minimizing risks of design errors. Additionally, the proposed approach optimizes material costs and reduces environmental impacts by enabling the selection of sustainable solutions. The results hold practical value for civil engineers, designers, and geotechnical specialists. The conclusion discusses prospects for further development of AI technologies in construction and opportunities for their integration with other innovative approaches, such as digital twins and Building Information Modeling (BIM).
Montayeva et al. (Thu,) studied this question.