@article{Zhang2026, 
author = {Xiatao Zhang and Lei Bian and Yuqi Gu and Dong Wang},
title = {Inversion of Undrained Strength of Clayey Soils from Cone Penetration Tests Using Physics-Informed Neural Network},
year = {2026},
journal = {Periodical of Ocean University of China},
volume = {56},
number = {10},
pages = {126-134},
keywords = {cone penetration test, undrained strength, physics-informed neural network, spherical cavity expansion, partially drained},
url = {https://www.sciopen.com/article/10.16441/j.cnki.hdxb.20260003},
doi = {10.16441/j.cnki.hdxb.20260003},
abstract = {When cone penetration testing is used to evaluate the undrained strength of cohesive soils, the conventional cone factor shows considerable dispersion and is not suitable for partially drained penetration. To address this problem, an inversion method for undrained strength based on physics-informed neural network was proposed. Spherical cavity expansion theory provides the physical constraint. Separate neural networks were constructed for the elastic and plastic zones. We incorporated the cavity wall limit pressure into the loss function as observational data, enabling direct inversion of undrained strength. The method was then applied to silty clay and clayey silt using the relationship between cone tip resistance and normalized penetration rate. Comparisons with analytical solutions from spherical cavity expansion theory confirmed the numerical reliability of the proposed method. Centrifuge and field tests were conducted on four cohesive soils. The inverted undrained strength showed errors generally within 20%. These results validate the method's applicability across different soil types and drainage conditions.}
}