Аңдатпа
The integration of artificial intelligence (AI) technologies, particularly machine learning (ML), with geotechnical engineering is transforming the landscape of infrastructure development and maintenance. This abstract explores the confluence of geotechnical data analytics and machine learning, illustrating how this synergy enhances predictive modeling and risk assessment in geotechnical applications. Recent advancements in sensor technology and data acquisition methods have resulted in the generation of vast amounts of geotechnical data. These datasets, characterized by their volume, variety, and velocity, present a unique opportunity for the application of machine learning techniques. Machine learning algorithms, particularly deep learning, have demonstrated exceptional capability in identifying patterns and making predictions from large, complex datasets. When applied to geotechnical data, these algorithms can significantly improve the accuracy of predicting soil behavior, foundation performance, and potential geohazards. This paper reviews several case studies where machine learning models have been successfully implemented in geotechnical engineering. These include the use of convolutional neural networks for the classification of soil types, the application of recurrent neural networks for predicting landslide susceptibility, and reinforcement learning for optimizing the design of tunneling projects. The results from these studies indicate that machine learning not only enhances the efficiency and effectiveness of geotechnical investigations but also contributes to safer and more cost-effective engineering solutions. In conclusion, the convergence of geotechnical data analytics and machine learning heralds a new era in geotechnology. By harnessing the power of AI, geotechnical engineers can tackle complex challenges with greater precision, ultimately leading to more reliable and resilient infrastructure systems. This interdisciplinary approach not only pushes the boundaries of traditional geotechnical engineering but also sets a new standard for future research and practice in the field.