Alireza Ahangar-Asr

A new approach for prediction of the stability of soil and rock slopes

Purpose – 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.Design/methodology/approach – 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.Findings – 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 fail...

Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression

This paper presents a new approach, based on evolutionary polynomial regression (EPR), for prediction of permeability (K), maximum dry density (MDD), and optimum moisture content (OMC) as functions of some physical properties of soil. 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 (GA) and the least-squares method is used to find feasible structures and the appropriate parameters of those structures. EPR models are developed based on results from a series of classification, compaction, and permeability tests from the literature. The tests included standard Proctor tests, constant head permeability tests, and falling head permeability tests conducted on soils made of four components, bentonite, limestone dust, sand, and gravel, mixed in different proportions. The results of the EPR model predictions are compared with those of a neural network model, a correlation equation from the literature, and the experimental data. Comparison of the results shows that the proposed models are highly accurate and robust in predicting permeability and compaction characteristics of soils. Results from sensitivity analysis indicate that the models trained from experimental data have been able to capture many physical relationships between soil parameters. The proposed models are also able to represent the degree to which individual contributing parameters affect the maximum dry density, optimum moisture content, and permeability.

Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression

Purpose – 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.Design/methodology/approach – 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.Findings – Data from 70 cases of experiments on rubber concrete are use...

Determination of the most probable slip surface in 3D slopes considering the effect of earthquake force direction

Considering the effect of earthquake forces on stability of slopes has always been of crucial importance in seismic analysis of geotechnical structures like dams, roads and embankments and there has been much concern about stability of cuts, fills and natural slopes under earthquake loadings in recent years. In this research a three-dimensional approach in conjunction with genetic algorithm (GA) was proposed to investigate the effect of earthquake force inclination on minimum stability factor of safety and the shape and direction of the corresponding failure surface. The stability factor of safety was considered to be a function of soil properties, slope dimensions, coordination of the nodal points on the slip surface mesh and their rotation angle to the rotation centre, magnitude of pseudo-static coefficient and the inclination of earthquake forces. The proposed methodology was found to be very effective in determining the minimum stability factor of safety and the corresponding most probable slip surface considering pseudo-static analysis of slopes under inclined earthquake forces.

A new evolutionary approach to geotechnical and geo-environmental modelling

In many cases, models based on certain laws of physics can be developed to describe the behaviour of physical systems. However, in case of more complex phenomena with less known or understood contributing parameters or variables the physics-based modelling techniques may not be applicable. Evolutionary Polynomial Regression (EPR) offers a new way of rendering models, in the form of easily interpretable polynomial equations, explicitly expressing the relationship between contributing parameters of a system of complex nature, and the behaviour of the system. EPR is a recently developed hybrid regression method that provides symbolic expressions for models and works with formulae based on pseudo-polynomial expressions. In this chapter the application of EPR to two important geotechnical and geo-environmental engineering systems is presented. These systems include thermo-mechanical behaviour of unsaturated soils and optimisation of performance of an aquifer system subjected to seawater intrusion. Comparisons are made between the EPR model predictions and the actual measured or synthetic data. The results show that the proposed methodology is able to develop highly accurate models with excellent capability of reflecting the real and expected physical effects of the contributing parameters on the performance of the systems. Merits and advantages of the suggested methodology are highlighted.

An EPR approach to the modeling of civil and geotechnical engineering systems

Evolutionary polynomial regression (EPR) is a data-driven technique based on evolutionary computing that aims 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 representing the behavior of the system directly from data. The accuracy of the developed models and their generalization capabilities are directly related to the accuracy and completeness of the data used to develop the models. Several examples are presented on practical applications of EPR. It is shown that EPR models are applicable to a diverse range of engineering systems and are capable of modeling the behavior of complex systems with very high accuracy. An example is also presented to show the possibility of using EPR-based models in the finite element method. The merits and advantages of the proposed approach are highlighted.

Modelling Stress-Strain Behaviour of Granular Soils

This paper presents a unified framework for constitutive modelling of the axial stress-volumetric strain behaviour of granular soils using an evolutionary polynomial regression technique. It is a data-driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. A procedure is presented for construction of the model. The main advantage of the proposed model over conventional and neural network-based constitutive models is that it provides the optimum structure for the material constitutive model representation as well as its parameters, directly from raw experimental (or field) data. It can learn nonlinear and complex material behavior without any prior assumptions on the constitutive relationship. The proposed algorithm provides a transparent relationship for the constitutive material model. Merits and advantages of the proposed technique are discussed in the paper.