The damage parameter is a variable used to describe the transition of geomaterials from a bonded state to an unbonded state. The correct expression of the damage evolution of structured soil is crucial in establishing constitutive models for structured soils. Currently, research on damage laws typically involves assuming expressions for damage parameters and then fitting these parameters using experimental results to establish the damage law. The rationality and applicability of these damage laws are yet to be validated. To derive a unified expression for the damage law of structured sands incorporating microscopic mechanisms, a prediction model based on symbolic regression is proposed. Firstly, using the definitions of damage parameters with microscopic physical significance, various damage databases are constructed using the distinct element method (DEM). Secondly, preliminary parameter screening is conducted on isotropic compression and constant p true triaxial compression stress paths using a method that combines input variables. p is the average effective stress. Combined with the genetic programming-based symbolic regression (GPSR), damage expressions with different complexities are derived. Finally, the best-performing expression is selected as the damage law for structured sand, namely the GPSR damage law, based on an analysis of prediction and generalization errors. The applicability of different expressions is compared using various DEM damage databases. The results show that the GPSR damage law represents damage parameters as functions of plastic deviatoric strain epsilon(s), normalized mean effective stress p/p(y) and coefficient of intermediate principal stress b. It effectively reflects the transition from structured soil to remolded soil. The outstanding prediction ability of the GPSR damage law on different damage databases further demonstrates its applicability to various geomaterials. The research findings are valuable to establish constitutive models for structured sands.

It has been imperative to study and stabilize cohesive soils for use in the construction of pavement subgrade and compacted landfill liners considering their unconfined compressive strength (UCS). As long as natural cohesive soil falls below 200 kN/m2 in strength, there is a structural necessity to improve its mechanical property to be suitable for the intended structural purposes. Subgrades and landfills are important environmental geotechnics structures needing the attention of engineering services due to their role in protecting the environment from associated hazards. In this research project, a comparative study and suitability assessment of the best analysis has been conducted on the behavior of the unconfined compressive strength (UCS) of cohesive soil reconstituted with cement and lime and mechanically stabilized at optimal compaction using multiple ensemble-based machine learning classification and symbolic regression techniques. The ensemble-based ML classification techniques are the gradient boosting (GB), CN2, na & iuml;ve bayes (NB), support vector machine (SVM), stochastic gradient descent (SGD), k-nearest neighbor (K-NN), decision tree (Tree) and random forest (RF) and the artificial neural network (ANN) and response surface methodology (RSM) to estimate the (UCS, MPa) of cohesive soil stabilized with cement and lime. The considered inputs were cement (C), lime (Li), liquid limit (LL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). A total of 190 mix entries were collected from experimental exercises and partitioned into 74-26% train-test dataset. At the end of the model exercises, it was found that both GB and K-NN models showed the same excellent accuracy of 95%, while CN2, SVM, and Tree models shared the same level of accuracy of about 90%. RF and SGD models showed fair accuracy level of about 65-80% and finally (NB) badly producing an unacceptable low accuracy of 13%. The ANN and the RSM also showed closely matched accuracy to the SVM and the Tree. Both of correlation matrix and sensitivity analysis indicated that UCS is greatly affected by MDD, then the consistency limits and cement content, and lime content comes in the third place while the impact of (OMC) is almost neglected. This outcome can be applied in the field to obtain optimal compacted for a lime reconstituted soil considering the almost negligible impact of compactive moisture.

In this study, an explicit solution for the limit expansion pressure of cavity expansion in unsaturated soil, which follows a hydraulic coupling constitutive model, was proposed based on the cavity expansion method (CEM) and deep symbolic regression (DSR). In detail, a semi-analytical elastic-plastic solution for cavity expansion in unsaturated soils was obtained based on the unsaturated critical state model; hence, a database was created. To obtain an explicit solution for the limit expansion pressure, DSR, which is a gradient-based approach to symbolic regression based on reinforcement learning, was applied. Subsequently, 13-and 4-parameter regression analyses were conducted, with satisfactory results being obtained. Finally, based on the proposed explicit expression, a theoretical calculation model for the jacked-pile penetration resistance in unsaturated soils was established. The study presented in this paper opens up possibilities for obtaining theoretical solutions to issues such as predicting pile driving performance in unsaturated soils.