Predicting undrained shear strength in marine sediments using a physics-informed neural network (PINN)

Undrained shear strength (S_U) is a key parameter for evaluating slope stability, offshore foundation design, and submarine geohazards in marine environments. Conventional methods for predicting S_U often fall short in accuracy because they fail to account for the complex and nonlinear relationships among sediment properties. Here, we propose a physics-informed neural network (PINN) framework to predict the three-dimensional structure of S_U using various observed physical parameters, including bulk density, porosity, P-wave velocity, gamma ray attenuation, and natural gamma ray. The framework embeds governing physical laws—total vertical stress σ(z), pore water pressure u(z), and effective stress σ′(z)—as constraints within the loss function to ensure physically consistent and accurate predictions. The results show that our physics-informed model significantly improves prediction accuracy and stability, achieving R² up to 0.91 (mean ≈ 0.85) and reducing prediction error by more than 18% for marine sediments compared to purely data-driven models. Sensitivity analysis highlights that bulk density and porosity are the most influential inputs for predicting S_U, consistent with their fundamental role in sediment consolidation and strength. The predicted S_U exhibits a northwest–southeast gradient, with the strongest increase at depths of approximately 100–200 m below the seafloor. The proposed model provides spatially continuous S_U maps and demonstrates that embedding first-order physical laws in network training greatly improves the reliability and interpretability of S_U predictions, offering a practical tool for marine geotechnical applications in areas with complex stratigraphy.