关键词: 原位固结系数, 静力触探, 孔压测试数据, 固结方程, 物理信息神经网络, 参数反演

Abstract: The consolidation coefficient is a crucial parameter for settlement calculation and stability analysis of soft foundations. Existing in-situ testing methods for the consolidation coefficient have the disadvantages of time-consuming and low accuracy. Based on the penetration mechanism of piezocone penetration test (CPTU) and the dissipation pattern of excess pore water pressure at the cone shoulder, the formation, development, and dissipation processes of excess pore water pressure at the CPTU cone shoulder are described using the theory of circular cavity expansion and the axisymmetric consolidation equation. By incorporating the automatic differentiation capability of neural networks, the axisymmetric consolidation equation is embedded into a deep neural network. The physical information constraints of the neural network are formed through the loss functions of physical equations, boundary conditions, and initial conditions. At the same time, the CPTU pore pressure test data serve as a data-driven term. Consequently, with the minimization of the excess pore water pressure loss function as the optimization goal, a physics-informed neural networks (PINNs) model is established for inversely analyzing the in-situ consolidation coefficient using CPTU pore pressure test data. The effectiveness of the PINNs model in inversely analyzing in-situ consolidation coefficient is verified through example analysis and inversion validation using existing centrifuge test data. The robustness of the PINNs model is also analyzed using CPTU pore pressure test data. The results indicate that the proposed PINNs model can effectively use CPTU pore pressure test data to rapidly and accurately invert the site in-situ consolidation coefficient. Due to the integration of physical mechanism constraints, the model requires only a small amount of training data and exhibits strong robustness and generalization performance against noisy pore pressure test data, providing an effective approach for accurate, rapid, and reliable testing of the in-situ consolidation coefficient.

Key words: in-situ consolidation coefficient, static cone penetration, pore pressure test data, consolidation equation, physics-informed neural networks, parameter inversion