Artificial Intelligence and Big Data in Geotechnical Engineering: A Comprehensive Review

Abstract

Geotechnical engineering is a critical field underpinning infrastructure development, but traditional methods face challenges due to data variability, complexity, and inefficiencies. The integration of artificial intelligence (AI) and big data technologies is revolutionizing this discipline, enabling enhanced prediction accuracy, real-time decision-making, and cost-efficient solutions.

This review explores the transformative role of AI and big data in geotechnics, focusing on applications like site characterization, slope stability analysis, foundation design optimization, seismic hazard assessment, and subsurface modeling. Key AI techniques, including machine learning, deep learning, and expert systems, are analyzed for their effectiveness in addressing complex geotechnical problems. Big data sources, such as satellite imagery, remote sensing, and real-time monitoring, are examined for their contribution to improving predictive modeling and risk assessment.

The review highlights the benefits of AI and big data, including improved safety, faster decision-making, and cost savings, while addressing challenges such as data quality, model interpretability, and integration with traditional methods. Future directions, such as AI-augmented digital twins, autonomous monitoring systems, advanced data fusion, and generative AI in design, are discussed as potential innovations shaping the field.

Through case studies and real-world applications, this review emphasizes the importance of adopting data-driven approaches and interdisciplinary collaboration to overcome challenges and unlock the full potential of AI and big data in geotechnical engineering. This vision aims to achieve sustainable, efficient, and resilient infrastructure development in the coming decades.