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This paper aims to develop a unified framework for modelling triaxial deviator stress – axial strain and volumetric strain – axial strain behaviour of granular soils with the ability to predict the entire stress paths, incrementally, point by point, in deviator stress versus axial strain and volumetric strain versus axial strain spaces using an evolutionary-based technique based on a comprehensive set of data directly measured from triaxial tests without pre-processing. In total, 177 triaxial test results acquired from literature were used to develop and validate the models. Models aimed to not only be capable of capturing and generalising the complicated behaviour of soils but also explicitly remain consistent with expert knowledge available for such behaviour.

Evolutionary polynomial regression (EPR) was used to develop models to predict stress – axial strain and volumetric strain – axial strain behaviour of granular soils. EPR integrates numerical and symbolic regression to perform EPR. The strategy uses polynomial structures to take advantage of favourable mathematical properties. EPR is a two-stage technique for constructing symbolic models. It initially implements evolutionary search for exponents of polynomial expressions using a genetic algorithm (GA) engine to find the best form of function structure; second, it performs a least squares regression to find adjustable parameters, for each combination of inputs (terms in the polynomial structure).

EPR-based models were capable of generalising the training to predict the behaviour of granular soils under conditions that have not been previously seen by EPR in the training stage. It was shown that the proposed EPR models outperformed ANN and provided closer predictions to the experimental data cases. The entire stress paths for the shearing behaviour of granular soils using developed model predictions were created with very good accuracy despite error accumulation. Parametric study results revealed the consistency of developed model predictions, considering roles of various contributing parameters, with physical and engineering understandings of the shearing behaviour of granular soils.

In this paper, an evolutionary-based data-mining method was implemented to develop a novel unified framework to model the complicated stress-strain behaviour of saturated granular soils. The proposed methodology overcomes the drawbacks of artificial neural network-based models with black box nature by developing accurate, explicit, structured and user-friendly polynomial models and enabling the expert user to obtain a clear understanding of the system.

This study is an attempt to estimate the influence of the presence of cavities on the stability of slopes in earth dams under rapid drawdown conditions. The purpose of this paper is to study the influence of different factors, such as the diameter and location of cavities, in addition to their existence effects.

A series of finite element simulation models were developed using PLAXIS 2D finite element software to analyse the stability of slopes in earth dams while considering various effects from cavities in the subsoil under rapid drawdown conditions.

The results indicated that the presence of cavities and an increase in the diameter of cavities decreased the stability of the upstream face dramatically for all examined locations in a horizontal direction; however, this effect was less on the downstream side. The results also showed that variations in the location of cavities in the horizontal direction have a greater effect on the stability than those in the vertical direction. The results revealed that increasing shear strength parameters of embankment does not reduce the influence of cavities on stability when those cavities are in critical locations.

A numerical model has been developed to simulate the effects of cavities on the stability of slopes in water-retaining structures/earth dams. The stability of earth dam slopes on upstream and downstream sides under rapid drawdown conditions considering various cavity effects, including their existence, diameter and location, were numerically analysed.

Using discarded tyre rubber as concrete aggregate is an effective solution to the environmental problems associated with disposal of this waste material. However, adding rubber as aggregate in concrete mixture changes, the mechanical properties of concrete, depending mainly on the type and amount of rubber used. An appropriate model is required to describe the behaviour of rubber concrete in engineering applications. The purpose of this paper is to show how a new evolutionary data mining technique, evolutionary polynomial regression (EPR), is used to predict the mechanical properties of rubber concrete.

EPR is a data‐driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. In this technique, a combination of the genetic algorithm and the least square method is used to find feasible structures and the appropriate constants for those structures.

Data from 70 cases of experiments on rubber concrete are used for development and validation of the EPR models. Three models are developed relating compressive strength, splitting tensile strength, and elastic modulus to a number of physical parameters that are known to contribute to the mechanical behaviour of rubber concrete. The most outstanding characteristic of the proposed technique is that it provides a transparent, structured, and accurate representation of the behaviour of the material in the form of a polynomial function, giving insight to the user about the contributions of different parameters involved. The proposed model shows excellent agreement with experimental results, and provides an efficient method for estimation of mechanical properties of rubber concrete.

In this paper, a new evolutionary data mining approach is presented for the analysis of mechanical behaviour of rubber concrete. The new approach overcomes the shortcomings of the traditional and artificial neural network‐based methods presented in the literature for the analysis of slopes. EPR provides a viable tool to find a structured representation of the system, which allows the user to gain additional information on how the system performs.

Analysis of stability of slopes has been the subject of many research works in the past decades. Prediction of stability of slopes is of great importance in many civil engineering structures including earth dams, retaining walls and trenches. There are several parameters that contribute to the stability of slopes. This paper aims to present a new approach, based on evolutionary polynomial regression (EPR), for analysis of stability of soil and rock slopes.

EPR is a data‐driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. In this technique, a combination of the genetic algorithm and the least square method is used to find feasible structures and the appropriate constants for those structures.

EPR models are developed and validated using results from sets of field data on the stability status of soil and rock slopes. The developed models are used to predict the factor of safety of slopes against failure for conditions not used in the model building process. The results show that the proposed approach is very effective and robust in modelling the behaviour of slopes and provides a unified approach to analysis of slope stability problems. It is also shown that the models can predict various aspects of behaviour of slopes correctly.

In this paper a new evolutionary data mining approach is presented for the analysis of stability of soil and rock slopes. The new approach overcomes the shortcomings of the traditional and artificial neural network‐based methods presented in the literature for the analysis of slopes. EPR provides a viable tool to find a structured representation of the system, which allows the user to gain additional information on how the system performs.