Machine Learning-Based Assessment of Soil Liquefaction for Geotechnical Engineering Applications

Soil liquefaction is a major challenge in geotechnical engineering, often leading to severe structural instability and infrastructure damage. With the growing complexity of geotechnical problems, machine learning (ML) has emerged as an innovative tool offering new possibilities for analyzing and predicting soil behavior. This paper examines the role and importance of machine learning in the assessment of soil liquefaction, focusing on how ML techniques contribute to improving the efficiency, accuracy, and adaptability of conventional evaluation methods. Through a review of recent research and applications, the study highlights the transformative potential of ML in modern geotechnical engineering and its capacity to support more intelligent and data-driven approaches to liquefaction risk management. The findings emphasize the growing relevance of machine learning as a strategic component in the future of infrastructure resilience.

کلیدواژه ها:

Soil Liquefaction ، Machine Learning ، Geotechnical Engineering ، Artificial Intelligence