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Machine Learning & Risk Assessment in Geoengineering

Geotechnical engineering deals with more uncertainties (due to nature of materials, e.g. soil and rock) than other areas of civil and mechanical engineering. Modeling the behavior of such materials in geotechnical engineering applications is complex and sometimes beyond the abili- ty of most traditional forms of physically-based engineer- ing methods. In recent years, the application of statistical and machine learning (ML) techniques in a wide range of geotechnical engineering has grown rapidly, such as site characterization, geo-structure design and construction. The MLRA2021 joint conference under one umbrella aims to bring together researchers and engineers working in the field of Information Technology and Risk Assessment in Ge- osciences to discuss how progresses in the field of Big-Da- ta and Machine Learning could impact engineering and re- search practices related to the natural hazards’ assessment and the quantification of variabilities and uncertainties.

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