Prediction of the Permeability Tensor of Marine Clayey Sediment during Cyclic Loading and Unloading of Confinement Pressure Using Physical Tests and Machine Learning Techniques

Abstract

In this study, a method was introduced to validate the presence of a Representative Elementary Volume (REV) within marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure. Physical testing provided the basis for this verification. Once the existence of the REV for such sediment was confirmed, we established a machine-learning predictive model. This model utilizes a hybrid algorithm combining Particle Swarm Optimization (PSO) with a Support Vector Machine (SVM). The model was trained using a database generated from the aforementioned physical tests. The machine-learning model demonstrates favorable predictive performance based on several statistical metrics, including the coefficient of determination (R2), mean residual error (MSE), mean relative residual error (MRSE), and the correlation coefficient R during the verification process. Utilizing the established machine-learning predictive model, one can effortlessly obtain the permeability tensor of marine clayey sediment containing cracks during cyclic loading and unloading of confinement pressure by inputting the relevant stress condition parameters. The original research cannot estimate the permeability tensor under similar loading and unloading conditions through REV. In this study, the physical model test was used to determine the REV of marine cohesive sediments with cracks by cyclic-constrained pressure loading and unloading. Referring to the results of physical tests, we developed a machine-learning prediction model that can easily estimate the permeability tensor of marine cohesive sediments with cracks under cyclic loading and constrained pressure unloading conditions. This method greatly saves time and computation and provides a direct method for engineering and technical personnel to predict the permeability tensor in this case.