Spatio-Temporal Prediction of Soil Deformation using Machine Learning

Abstract

Unmanned construction machine working in dangerous environments such as construction sites and disaster areas has been developed. However, it is still necessary to improve its work efficiency especially during bulldozing and excavating soil. This research aims to develop a method for predicting soil deformation using machine learning. The feasibility of the proposed method is verified in a scenario where a simple bulldozing blade excavates soil. In the experiment, soil deformation at a front part of the blade is captured by multiple stereoscopic cameras. The camera provides depth data that are then converted to height field data. This dataset is fed to machine learning using Recurrent Neural Network (RNN) because soil deformation is continuous phenomena depending on time variation. The learned model for predicting soil deformation is confirmed in varied intrusion depth of the bulldozing blade.