Physics-Informed Inversion of CPTu Dissipation Curves for Anisotropic Clay Permeability

Lucy Fisher; Grace Cooper

Authors

Lucy Fisher Department of Civil, School of Engineering, University of Southampton, Southampton, England, United Kingdom Author
Grace Cooper Department of Mechanical, Faculty of Science and Engineering, University of Liverpool, Liverpool, England, United Kingdom Author

Keywords:

Piezocone Testing, Dissipation Curves, Anisotropic Permeability, Physics-Informed Neural Networks, Geotechnical Engineering, Consolidation Theory

Abstract

The accurate determination of hydraulic conductivity in fine-grained soils is a fundamental requirement for predicting consolidation settlement, assessing slope stability, and designing offshore foundations. Piezocone penetration testing offers a reliable in situ method for estimating these parameters through pore pressure dissipation tests. However, conventional interpretation methods rely on simplified analytical or semi-analytical solutions that often assume isotropic permeability and simplified initial excess pore pressure distributions. Natural clay deposits exhibit significant anisotropy due to depositional processes and particle orientation, rendering isotropic assumptions inadequate. This paper presents a novel framework for interpreting piezocone dissipation tests using Physics-Informed Neural Networks to invert anisotropic permeability coefficients directly from dissipation curves. By embedding the governing partial differential equations of axisymmetric consolidation into the loss function of a neural network, the proposed method bypasses the need for large labeled datasets while strictly adhering to physical laws. The methodology is rigorously validated against high-fidelity finite element simulations and applied to field data from well-characterized clay test sites. Results demonstrate that the physics-informed inversion accurately separates horizontal and vertical coefficients of consolidation, offering a superior alternative to traditional curve-fitting techniques. The framework exhibits strong robustness against signal noise and provides a continuous, highly detailed mapping of spatial permeability variations, marking a significant advancement in geotechnical site characterization.

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