Levee Soil Stratification Based on PAM Cluster Analysis of Measured Soil Samples from Multiple Probe Drilling Sites

Accurate soil stratification is crucial for levee safety evaluation, yet limited field sampling often hinders comprehensive analysis. This study applies the Partitioning Around Medoids (PAM, also known as K-Medoids) clustering approach for levee soil stratification using data from multiple probe drilling sites. Focusing on a Yellow River levee section in China as a study case, the PAM clustering approach effectively identifies its distinct soil types and reconstructs its soil stratification by analyzing key soil properties relevant to levee seepage and stability characteristics, including coefficient of permeability, angle of internal friction, and cohesion. The resulting soil stratification, when applied to seepage and stability analyses of the levee section, yields relatively high safety factors, indicating low failure risks under design flood conditions. These analytical results align with recent monitoring records, validating the effectiveness of the approach. A sensitivity analysis on the number of clusters, the key parameter in the PAM clustering approach, demonstrates the typical existence of an optimal value balancing computational accuracy and practical interpretability. A comparison with a hierarchical clustering approach further confirms the robustness of the PAM clustering approach. This study contributes to improving levee soil stratification methodology and enhancing levee safety evaluation, particularly when dealing with limited and spatially distributed sampling data.