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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.

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 many civil engineering phenomena is a complex problem due to the participation of a large number of factors involved. Traditional methods usually suffer from a lack of physical understanding. Furthermore, the simplifying assumptions that are usually made in the development of the traditional methods may, in some cases, lead to very large errors. The purpose of this paper is to present a new method, based on evolutionary polynomial regression (EPR) for capturing nonlinear interaction between various parameters of civil engineering systems.

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‐squares method is used to find feasible structures and the appropriate constants for those structures.

Capabilities of the EPR methodology are illustrated by application to two complex practical civil engineering problems including evaluation of uplift capacity of suction caissons and shear strength of reinforced concrete deep beams. The results show that the proposed EPR model provides a significant improvement over the existing models. The EPR models generate a transparent and structured representation of the system. For design purposes, the EPR models, presented in this study, are simple to use and provide results that are more accurate than the existing methods.

In this paper, a new evolutionary data mining approach is presented for the analysis of complex civil engineering problems. The new approach overcomes the shortcomings of the traditional and artificial neural network‐based methods presented in the literature for the analysis of civil engineering systems. 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.

Auxetic materials differ from conventional materials by the manner in which they respond to stretching; they tend to get fatter when stretched, resulting in a negative Poisson's ratio. The purpose of this paper is to present a numerical methodology for design of microstructure of 2D and 3D auxetic materials with a wide range of different negative Poisson's ratios.

The proposed methodology is based on a combination of finite element method and a genetic algorithm. The problem is formulated as an optimization problem of finding microstructures with prescribed behavioral requirements. Different microstructures are generated and evolved using the genetic algorithm and the behavior of each microstructure is analyzed using the finite element method to evaluate its fitness in competition with other generated structures.

Numerical examples show that it is possible to design a large number of new auxetic materials, each with a different value of negative Poisson's ratio.

The proposed methodology can be used as an effective method to tailor new materials with prescribed values of negative (or positive) Poisson's ratio. The methodology can also be used to optimize other material properties.

In this paper, the peak kinetic energy density (KED) of soil particles during earthquake excitation is used as an intensity measure for the evaluation of liquefaction potential under field conditions. The paper seeks to discuss this measure.

Using centrifuge tests data, it is shown that seismic pore water pressure buildup is proportional to cumulative KED at a particular soil depth. Linear relationships are found between cumulative kinetic energy and corresponding cumulative strain energy. To consider the effect of soil amplification, several equivalent linear ground response analyses are performed and the results are used to derive an equation for depth reduction factor of peak kinetic energy density. Two separate databases of liquefaction case histories are used in order to validate the proposed model. The performance of the proposed model is compared with a number of commonly used shear stress‐based liquefaction assessment methods. Finally, the logistic regression method is employed to obtain probabilistic boundary curves based on the present model. Parametric study of the proposed probabilistic model is carried out to verify its agreement with the previous methods.

It has been shown that the kinetic energy model works satisfactorily in classifying liquefied and non‐liquefied cases compared with the existing recommendations of shear stress‐based criterion. The results of the probabilistic kinetic energy model are in good agreement with those of previous studies and show a reasonable trend with respect to the variations of fines content and effective overburden pressure. The proposed model can be as used an alternative approach for assessment of liquefaction potential.

These findings make a sound basis for the development of a kinetic energy‐based method for assessment of liquefaction potential.

The purpose of this paper is to develop and validate a numerical model to study the effect of changing hydraulic parameters on saltwater intrusion in coastal aquifers.

The numerical model SEAWAT is validated and applied to a hypothetical case (Henry problem) and a real case study (Biscayne aquifer, Florida, USA) for different values of hydraulic parameters including; hydraulic conductivity, porosity, dispersion, diffusion, fluid density and solute concentration. The dimensional analysis technique is used to correlate these parameters with the intrusion length.

The results show that the hydraulic parameters have a clear effect on saltwater intrusion as they increase the intrusion in some cases and decrease it in some other cases. The results indicate that changing hydraulic parameters may be used as a control method to protect coastal aquifers from saltwater intrusion.

The results of the application of the model to the Biscayne aquifer in Florida showed that the intrusion can be reduced to 50 percent when the hydraulic conductivity is reduced to 50 percent. Decreasing hydraulic conductivity by injecting some relatively cheap materials such as bentonite can help to reduce the intrusion of saltwater. So the saltwater intrusion can be reduced with relatively low cost through changing some hydraulic parameters.

A relationship to calculate intrusion length in coastal aquifer is developed and the impact of different hydraulic parameters on saltwater intrusion is highlighted. Control of saltwater intrusion using relatively cheap method is presented.

Liquefaction of soils is defined as significant reduction in shear strength and stiffness due to increase in pore water pressure. This phenomenon can occur in static (monotonic) or dynamic loading patterns. However, in each pattern, the inherent variability of the soil parameters indicates that this problem is of a probabilistic nature rather than being deterministic. The purpose of this paper is to present a method, based on random finite element method, for reliability assessment of static liquefaction of saturated loose sand under monotonic loading.

The random finite element analysis is used for reliability assessment of static liquefaction of saturated loose sand under monotonic loading. The soil behavior is modeled by an elasto-plastic effective stress constitutive model. Independent soil parameters including saturated unit weight, peak friction angle and initial plastic shear modulus are selected as stochastic parameters which are modeled using a truncated normal probability density function (pdf).

The probability of liquefaction is assessed by pdf of modified pore pressure ratio at each depth. For this purpose pore pressure ratio is modified for monotonic loading of soil. It is shown that the saturated unit weight is the most effective parameter, within the selected stochastic parameters, influencing the static soil liquefaction.

This research focuses on the reliability analysis of static liquefaction potential of sandy soils. Three independent soil parameters including saturated unit weight, peak friction angle and initial plastic shear modulus are considered as stochastic input parameters. A computer model, coded in MATLAB, is developed for the random finite element analysis. For modeling of the soil behavior, a specific elasto-plastic effective stress constitutive model (UBCSAND) was used.

The Iranian entrepreneurial ecosystem has grown dramatically during the past decade. Several improvements have been made at different levels, and, therefore, one could witness its unique achievements. Digital entrepreneurs are an integral part of this ecosystem, as most of the early achievements are the results of their proactive behaviors. Hopefully, the number of female digital entrepreneurs has increased, and their entrepreneurial activities have profoundly changed the competition scene. Therefore, this chapter provides a better understanding of the multilayered entrepreneurial ecosystem of Iran and then elaborates how female entrepreneurs are positioned in this ecosystem. Moreover, six well-known award-winning female digital entrepreneurs are introduced, and their challenges are scrutinized accordingly through narrative research. Finally, the chapter concludes with some remarks and directions for future research.