An evolutionary approach to modelling the thermomechanical behaviour of unsaturated soils

Abstract

A new data mining approach is presented for modelling of the stress-strain and volume change behaviour of unsaturated soils considering temperature effects. The proposed approach is based on the evolutionary polynomial regression (EPR), which unlike some other data mining techniques, generates a transparent and structured representation of the behaviour of systems directly from raw experimental (or field) data. The proposed methodology can operate on large quantities of data in order to capture nonlinear and complex relationships between contributing variables. The developed models allow the user to gain a clear insight into the behaviour of the system. Unsaturated triaxial test data from literature was used for development and verification of EPR models. The developed models were also used (in a coupled manner) to produce the entire stress path of triaxial tests. Comparison of the EPR model predictions with the experimental data revealed the robustness and capability of the proposed methodology in capturing and reproducing the constitutive thermo-mechanical behaviour of unsaturated soils. More importantly, the capability of the developed models in accurately generalising the predictions to unseen data cases was illustrated. The results of a sensitivity analysis showed that the models developed from data are able to capture and represent the physical aspects of the unsaturated soil behaviour accurately. The merits and advantages of the proposed methodology are also discussed.

Neural network prediction of the reliability of heterogeneous cohesive slopes

“…More recently, another modelling procedure called evolutionary polynomial regression has also been demonstrated that is capable of developing mathematical expressions for reproducing the performance and behaviour of complicated systems (e.g. ). These studies have demonstrated that mathematical equations developed using ANNs and evolutionary polynomial regression generally performed better than the available empirical formulae.…”