  Soil consolidation is closely related to seepage, stability, and settlement
of geotechnical buildings and foundations, and directly affects the use and
safety of superstructures. Nowadays, the unidirectional consolidation theory of
soils is widely used in certain conditions and approximate calculations. The
multi-directional theory of soil consolidation is more reasonable than the
unidirectional theory in practical applications, but it is much more
complicated in terms of index determination and solution. To address the above
problem, in this paper, we propose a deep learning method using
physics-informed neural networks (PINN) to predict the excess pore water
pressure of two-dimensional soil consolidation. In the proposed method, (1) a
fully connected neural network is constructed, (2) the computational domain,
partial differential equation (PDE), and constraints are defined to generate
data for model training, and (3) the PDE of two-dimensional soil consolidation
and the model of the neural network is connected to reduce the loss of the
model. The effectiveness of the proposed method is verified by comparison with
the numerical solution of PDE for two-dimensional consolidation. Using this
method, the excess pore water pressure could be predicted simply and
efficiently. In addition, the method was applied to predict the soil excess
pore water pressure in the foundation in a real case at Tianjin port, China.
The proposed deep learning approach can be used to investigate the large and
complex multi-directional soil consolidation.
