Recent Developments in the Machine Learning Modeling of Geotechnical Data

Dear Colleagues,

Machine learning-based approaches offer numerous advantages for field inspection, testing, data modeling, and prediction, enabling significant cost savings by utilizing historical datasets and seamlessly integrating machine learning into workflows. Recent advancements in machine learning, particularly deep learning, have empowered computational models to effectively represent complex datasets. Cutting-edge machine learning models, which were previously underutilized in geotechnical engineering and geoscience research, are now addressing critical challenges in design and construction, materials testing, data management, operational optimization, and field inspection.

In the geotechnical field, extensive engineering and construction datasets accumulated over time provide an excellent opportunity to apply machine learning models for the reliable prediction of engineering characteristics and other key features. These models can estimate geotechnical asset conditions and the characteristics of civil infrastructure, such as foundations and slopes, throughout their life cycle. Additionally, they can optimize scheduling, construction, and maintenance processes, enabling data-driven decision-making and improving overall efficiency.

Machine learning models are increasingly being applied to address challenges in geotechnical engineering. Previous studies have demonstrated that machine learning algorithms can serve as valuable tools for generating the essential information required in geotechnical and foundation design. The difficulty of obtaining borehole logs and subsurface soil profiles in areas with scarce or unavailable data can potentially be mitigated using artificial intelligence (AI) methods. By leveraging historical data, machine learning offers a promising opportunity to reduce the costs associated with geotechnical subsurface investigations while enhancing the prediction and understanding of subsurface conditions. This Special Issue focuses on exploring emerging methods for developing and applying machine learning models to geotechnical data.

Prof. Dr. Yunfeng Zhang
Guest Editor

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• machine learning
• artificial intelligence
• geotechnical data
• subsurface investigation
• soil properties
• slope