Case Study on Analysis of Soil Compression Index Prediction Performance Using Linear and Regularized Linear Machine Learning Models (In Korea)

The compression index (Cc) is a critical soil parameter that is used to estimate the consolidation settlement of ground. In this study, the compression index, typically obtained through consolidation tests, was predicted using machine learning techniques after preprocessing data that considered the geotechnical and hydrogeological characteristics of the study area. This approach enabled an analysis of how geotechnical and hydrogeological characteristics affect the performance of machine learning models. Data obtained from geotechnical investigations were used to train models for each classified zone. Suitable models were then selected to predict the compression index, and their performance was evaluated. Predictions that considered the geotechnical and hydrogeological characteristics showed improved accuracy in zones influenced by a single water system or zones near the coast. However, in offshore areas with complex water systems, using the entire dataset proved to be more effective. Differences in the clay mineral of the soil also affected the prediction accuracy, indicating a correlation between clay mineral properties and model performance. These findings suggest that classifying data based on geotechnical and hydrogeological characteristics is necessary when developing compression index prediction models to achieve relatively stable results.