Spatio-temporal prediction of soil deformation in bucket excavation using machine learning

This paper proposes a prediction model for three-dimensional spatio-temporal soil deformation in bucket excavation. The prediction model for soil deformation (PMSD) consists of two machine learning processes: the long short-term memory (LSTM) and convolutional autoencoder (Conv-AE). These processes use datasets obtained from an experimental apparatus for bucket excavation developed in this work. The apparatus equips multiple depth cameras that precisely capture time-series data of soil deformation in bucket excavation. The LSTM, an extension of a recurrent neural network, successively predicts three-dimensional soil deformation. The Conv-AE is incorporated to both ends of the LSTM in order to quasi-reversibly compress and reconstruct the datasets so that the computational burden of the LSTM is relaxed. Qualitative and quantitative evaluations of the PMSD confirm the feasibility of time-series prediction of three-dimensional soil deformation. The Conv-AE shows sufficient accuracy equivalent to the measurement accuracy of the depth camera. The prediction accuracy of the PMSD is about 10 mm in most of the cases.