Field Observation and Settlement Prediction Study of a Soft Soil Embankment under Rolling Dynamic Compaction

Abstract

Rolling dynamic compaction (RDC) has been found to be useful for compaction soils and is now widely used globally. Because RDC is not often used in soft soils with poor engineering properties, field monitoring was used to study the soft clay embankment responses under RDC conditions in this study. Analysis of the monitoring data revealed that the response of the soil occurred mainly in the first 20 passes. Field monitoring revealed a strong correlation between settlement, horizontal displacement, and pore water pressure. The depth of impact of RDC on the soft soil embankment was between 3 and 3.5 m. Although settlement prediction is an important issue for construction, there is a lack of prediction methods for RDC-induced soil settlement. In this study, we used three different machine learning algorithms: random forest regression (RFR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) to predict the total settlement and uneven settlement induced by RDC on the soft soil embankment. The three prediction models were compared, and the predictive accuracy of these models was assessed. This study analyzes and summarizes the effect of RDC application on a soft clay embankment and explores the machine learning method used for settlement prediction based on monitoring data, which provides some methods and ideas for research on the application of RDC on soft soil foundations.