Mathematical Model and Computation in Geotechnical Engineering

Dear Colleagues,

It is widely known that within geotechnical engineering exists various uncertainties, resulting in many troubles when studying the reliability or risk assessment of geotechnical structures. Modelling the behavior of such uncertainties is complex and sometimes beyond the capability of traditional methods. In recent years, the applications of state-of-the-art methods, such as artificial intelligence (AI), machine learning (ML), big data and other advanced mathematical models, in a wide range of geotechnical engineering, have increased rapidly. For example, AI can be very useful in solving problems where deterministic solutions are not available or are excessively expensive in terms of computational cost, but for which there are significant observations and data available. This Special Issue contains original and hitherto unpublished works on the applications of AI, ML, big data, reliability algorithms and probabilistic risk analysis (PRA) in geotechnical engineering. We invite you to send original articles on topics, including, but not limited to:

New and improved reliability algorithm and design;

Geotechnical data optimization and mining;

Applications of AI or ML in geotechnical engineering;

Geotechnical risk assessment and management;

Geotechnical probabilistic problems;

Novel experiment and numerical simulation in geotechnical engineering

Prof. Dr. Chongchong Qi
Prof. Dr. Zhang Wengang
Guest Editors

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