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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.

By designing a system dynamics model in the form of a multimodal transportation system, this study for the first time seeks to reduce costs and time, and increase customer satisfaction by considering uncertainties in the intra city transit system, especially demand uncertainty and provide a prototype system to prove the capability of the dynamical system.

The paper tried to model the factors affecting the intra city multimodal transportation system by defining different scenarios in the cause-and-effect model. The maps and results developed according to system dynamics modeling principles are discussed.

Four scenarios were considered given the factors affecting the urban transportation system to implement the transportation information system for reducing the material and non-material costs of wrong planning of the intra city transit system. After implementing the scenarios, scenario two was selected under the following conditions: advertising for cultural development, support of authorities by efforts such as street widening to reduce traffic, optimize infrastructure, increase and optimize public transport and etc.

The value of this paper is considering uncertainty in traffic optimization; taking into account behavioral and demand indicators such as cultural promotion, official support, early childhood learning, traffic hours and the impact of traveler social status; investigating the factors affecting the system under investigation and the reciprocal effects of these factors and real-world simulation by considering the factors and effects between them.

Nowadays, the importance of infant birth weight (IBW) as a key factor in determining the future of physical and mental development of children is a growing concern. The purpose of this paper is to investigate the relationship between maternal characteristics and IBW among pregnant women who were referred to health centers in Qazvin city in the year 2016.

A descriptive-analytical study was conducted among pregnant women in 28–36 weeks of gestation who referred to healthcare centers and facilities affiliated by the Qazvin University of Medical Sciences in April–June 2016. The associations between maternal physical activity, mothers’ socioeconomic status and birth weight were examined by SPSS Software Package version 16 through linear and logistic regression tests.

Linear regression modeling suggested that maternal weight (p=0.001), income (p=0.04), gestational age of delivery (p=0.00) and pre-pregnancy BMI (p=0.02) were positively associated with birth weight, while occupational and heavy physical activity (p=0.003 and 0.008, respectively) were negatively associated with IBW. In this study, low birth weight infants are compared to those with normal weight belonged to mothers who have spent more time in doing heavy physical activities (OR=1.11, 95% CI 1.01–1.23). Also infants with low birth weight compared to others in the normal weight category were born from mothers with lower pre-pregnancy BMI (OR=0.65, 95% CI 0.62–0.78), gestational age of delivery (OR=0.82, 95% CI 0.79–0.86), maternal weight (OR=0.86, 95% CI 0.84–0.88) and income (OR=0.79, 95% CI 0.69–0.83).

The study findings revealed that certain maternal characteristics could play a significant role in IBW. Despite the importance, in most of developing countries (particularly Iran), future mothers are not advised about an appropriate weight gain during pregnancy or the optimal level of physical activity in such a period of time. Therefore, counseling pregnant women and giving them proper information on appropriate perinatal care would be helpful in order to have pregnancies with optimal outcomes.

The authors applied several statistical methods to analyze IBW among mothers with different maternal characteristics and predict birth weight based on contributing factors.

The purpose of this paper is to present an analytical calculation for estimating the leakages field distribution in surface-mounted permanent magnet synchronous motors (SMPMSMs) according to a sub-domain field model for eccentricity fault detection.

The magnetic field domain is classified into four sub-domains of PMs, air gap, stator core and outer region. In the proposed method, the governing equations taking the rotor eccentricity effect into account per region and the interface boundary conditions between sub-domains are formulated using the regular perturbation technique, Taylor series and Fourier series expansion. Maxwell's equations are solved in different regions in the polar coordinate system regarding the boundary conditions.

The radial and tangential components of electromagnetic field distribution in all sub-domains of one SMPMSM are obtained using the proposed method analytically. Finite element analysis is used to validate the results of the proposed method; the results indicated that the analytical model matches the finite-element prediction up to 30% eccentricity, except for some peak values that depend on the harmonic order value. The results of this paper demonstrated that in the event of eccentricity, an asymmetric magnetic field is generated in the outer region of the machine. Although its amplitude is small, it can be an indicator for detecting eccentricity faults from the outside environment of the machine.

The formulas presented in this paper can be applied as a new technique for detecting eccentricity faults in these motors from the outside environment.

The complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional models may lead to very large errors. The purpose of this paper is to illustrate capabilities of promising variants of genetic programming (GP), namely linear genetic programming (LGP), gene expression programming (GEP), and multi‐expression programming (MEP) by applying them to the formulation of several complex geotechnical engineering problems.

LGP, GEP, and MEP are new variants of GP that make a clear distinction between the genotype and the phenotype of an individual. Compared with the traditional GP, the LGP, GEP, and MEP techniques are more compatible with computer architectures. This results in a significant speedup in their execution. These methods have a great ability to directly capture the knowledge contained in the experimental data without making assumptions about the underlying rules governing the system. This is one of their major advantages over most of the traditional constitutive modeling methods.

In order to demonstrate the simulation capabilities of LGP, GEP, and MEP, they were applied to the prediction of: relative crest settlement of concrete‐faced rockfill dams; slope stability; settlement around tunnels; and soil liquefaction. The results are compared with those obtained by other models presented in the literature and found to be more accurate. LGP has the best overall behavior for the analysis of the considered problems in comparison with GEP and MEP. The simple and straightforward constitutive models developed using LGP, GEP and MEP provide valuable analysis tools accessible to practicing engineers.

The LGP, GEP, and MEP approaches overcome the shortcomings of different methods previously presented in the literature for the analysis of geotechnical engineering systems. Contrary to artificial neural networks and many other soft computing tools, LGP, GEP, and MEP provide prediction equations that can readily be used for routine design practice. The constitutive models derived using these methods can efficiently be incorporated into the finite element or finite difference analyses as material models. They may also be used as a quick check on solutions developed by more time consuming and in‐depth deterministic analyses.