Prediction of submarine soil dredging difficulty scale in cutter suction dredger construction with clustering-based deep learning

Expensive marine geological surveys and subjective human assessments limit the accuracy of submarine soil information, making it challenging to predict dredging difficulty for cutter suction dredger (CSD) excavation. To this end, we propose a data-driven framework that comprises data preprocessing, unsupervised clustering, and time series prediction using CSD construction big data. First, a high-dimensional feature selection method is employed to identify key features significantly affecting cutter cutting torque (CCT) and winch swing torque (WST) from 256-dimensional construction data. Then, the K-means algorithm defines a dredging difficulty scale (DDS) for dividing dredging difficulty by clustering CCT and WST. Finally, a deep learning model integrating the convolutional neural network (CNN), long short-term memory (LSTM), and the attention mechanism is formulated. The CNN-LSTM-Attention model aims to predict CCT and WST in the context of multivariate time series and then map such predictions to different DDSs. The applicability of the proposed framework is validated using 14,400 construction data collected from Tian Jing Hao CSD. Results show that four DDSs defined can effectively represent various dredging difficulty levels. The CNN-LSTM-Attention model achieves a high real-time prediction accuracy of 95.83% for DDSs and maintains robust performance across various prediction steps, which outperform baseline models. The proposed framework provides a novel approach for dividing and predicting dredging difficulty without relying on soil information, helping operators to optimize operational instructions in advance.