Development of training image database for subsurface stratigraphy

Reliable geotechnical site characterization and geohazard assessment are critical for bridge foundation design and management. This paper explores existing and emerging artificial intelligence-machine learning methods (AI-ML) transforming geotechnical site characterization and scour assessment for bridge foundation design and maintenance. The prevalent ML techniques applied for subsurface characterization are reviewed, and step-by-step methodologies for stratigraphy classification, borehole interpretation, geomaterial characterization, and ground modeling are provided. The ML techniques for maximum scour depth prediction are reviewed, and a simple ML methodology is proposed to provide a more reliable tool for scour depth estimation for implementation in practice. Also, a novel deep learning approach, with a detailed implementation description, is recommended for real-time scour monitoring and assessment of existing bridges. The challenges with database design and data processing for ML modeling, model optimization, training and validation, and uncertainty assessments are discussed, and innovative techniques for addressing them are reviewed.

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Development of training image database for subsurface stratigraphy

Cited by 3 publications

References 41 publications

Stochastic Modelling of Subsurface Stratigraphy from Sparse Measurements and Augmented Training Images

Stochastic Modelling of Subsurface Stratigraphy from Sparse Measurements and Augmented Training Images

Special issue on “Machine learning and AI in geotechnics”

Machine Learning Methods for Geotechnical Site Characterization and Scour Assessment

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