Soil Classification and Feature Importance of EPBM Data using Random Forests

Abstract

This paper presents an implementation of Random Forest (RF), a supervised learning algorithm, to classify the encountered geologic conditions using continuous earth pressure balance machine (EPBM) operation data. This study was performed on a data set from State Route 99 (SR99) tunnel construction in Seattle, WA. Hyperparameter tuning was performed to investigate the
effects of RF hyperparameters on the classification performance as well as to determine the best hyperparameter configuration. The role of the features in the classification model was investigated by evaluating the feature importance measures. This study demonstrates that, with straightforward hyperparameter tuning, RF could deliver good classification performance and
could infer the geologic transition through the classification probabilities. This study indicates that although several EPBM features had relatively larger “weights” for the classification, it was the interactions among the features that contain the geologic information.