This report is about advancement in soil mechanics and deep learning methods , Recent development in soil mechanic's using deep learning approaches

1. Soil mechanices
The primary objective of this research is to develop an appropriate
connection for the effective stress parameter via the use of a potent
machine learning strategy known as Multi-Gene Genetic Programming
(MGGP), which is a subfield of artificial intelligence (AI).

2. Introduction
• The capacity of the MGGP method to forecast equations without the
need for any previous assumptions about the nature of the sought-after
connection is the primary benefit of the approach when compared to
regression and other forms of soft computing. Independent variables
in this investigation include soil suction, residual volumetric water
content, saturated volumetric water content, air entry pressure
(bubbling pressure), and saturated volumetric water content.
Additionally, residual volumetric water content, saturated volumetric
water content, and net stress are als

3. Introduction
• where is the so-called effective stress (χ) parameter developed by
Bishop and advised to be equal to the degree of saturation, P stands
for net stress, and S indicates suction. Bishop stated that this
parameter should be equal to the degree of saturation. This
approximation, on the other hand, has not been proven to be highly
suitable and accurate by his successors who have done the study. It is
important to highlight that one of the primary focuses of researchers is
still on developing an appropriate equation that can reliably measure
the value of the effective stress parameter in unsaturated soils.

4. Methodology
• In order to perform multi-gene genetic programming for the purpose of finding an appropriate
formula for the effective stress parameter, the GPtips 2.0 software and programming in the
MATLAB environment were used. Each and every one of the SWRC parameters, as well as any
combination of these parameters, may be taken into consideration for use as a potential input to the
model. As a result, there were a total of six characteristics that were taken into consideration as
model input variables. These parameters were net stress, suction, SWRC slope, air entry pressure,
residual, and saturated volumetric water content. In order to use these variables as input variables for
the model, they were first transformed into dimensionless quantities, which are represented by the
equation (2). The effective stress parameter was the output variable in this equation.

5. Methodology
• For the purpose of creating the model in this investigation, a total of
101 pieces of data were employed. Following an examination of
earlier research and a search for studies with datasets that included all
of the target variables that were necessary for this investigation, data
points were gathered from the relevant literature. These data were
retrieved based on the outcomes of triaxial shear tests, from which
the effective stress parameter values were obtained; pressure plate
studies; and filter paper experiments; all of which were conducted in
order to define the parameters of the soil water retention curve [1–10].

6. Methodology
• The dataset consists of seven unsaturated soil properties, namely,
suction (S), air entry suction (hb), net stress (P), residual water content
(θr), volumetric water content at saturation (θs), soil water retention
curve slope (λ), and the effective stress
• arameter (χ). Input variables were made dimensionless/normalized as:
θ /θ , P/h and h /S and used together with λ. The dataset was divided
into two separate groups: an 80% training dataset and the remaining
data were selected for testing the accuracy and effectiveness of the
model.

7. • that is shown below was taken into consideration. The value of
function f was determined using multi-gene genetic programming.

8. that is shown below was taken into consideration.
The value of function f was determined using
multi-gene genetic programming.

12. Conclusion
• This study used a multi-gene genetic programming methodology to derive an equation for the effective stress
parameter in unsaturated soils. The generated equation exhibited a satisfactory level of accuracy when compared to a
dataset consisting of 101 test points obtained from laboratory experiments. Additionally, it demonstrated favourable
performance in comparison to other existing connections documented in the academic literature. The conducted
parametric analysis of the suggested equation revealed that both air entry suction and the net stress ratio have a
substantial impact on variations in the effective stress parameter in relation to suction. The influence of the slope of the
water retention curve and the ratio of residual to saturated volumetric water content on the fluctuation of the effective
stress parameter with suction was found to be comparatively less significant than the other factors stated before.

13. References
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evidence, International journal of Geomechanics, 4(2) (2004) 115- 126.
• A. Russell, N. Khalili, A unified bounding surface plasticity model for unsaturated soils,
International Journal for Numerical and Analytical Methods in Geomechanics, 30(3) (2006)
181-212
•L. Miao, S. Liu, Y. Lai, Research of soil–water characteristics and shear strength features
of Nanyang expansive soil, Engineering Geology, 65(4) (2002) 261-
• 267
• C. Rampino, C. Mancuso, F. Vinale, Experimental behaviour and modelling of an
unsaturated compacted soil, Canadian Geotechnical Journal, 37(4) (2000) 748-