Interpretable Machine Learning for Shear Strength Determination in Dikes Using Cone Penetration Tests
V.W. Chen (TU Delft - Civil Engineering & Geosciences)
C Jommi – Mentor (TU Delft - Geo-engineering)
S. Muraro – Graduation committee member (TU Delft - Geo-engineering)
R. Taormina – Graduation committee member (TU Delft - Sanitary Engineering)
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Abstract
This thesis performed a novel approach to shear strength determination using cone penetration tests and weather data to predict undrained shear strength and volumetric water content. Through SHAP feature importance analysis, models were interpreted and improved through feature engineering.