Asaad Faramarzi

An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement

Prediction of liquefaction and the resulting lateral displacement is a complex engineering problem due to heterogeneous nature of soils and participation of a large number of factors involved. In this paper new models are developed, based on evolutionary polynomial regression (EPR), for assessment of liquefaction potential and lateral spreading. The models developed for liquefaction and lateral spreading are compared to those obtained from neural network and linear regression based techniques. It is shown that the developed models are able to learn the complex relationship between either of these problems and their contributing factors in the form of a function with high level of accuracy (mostly in excess of 90%). The results of the EPR model developed for the liquefaction determination are used to find a novel 3-D boundary surface that discriminates between the cases of occurrence and non-occurrence of liquefaction. The developed boundary surface is employed to calculate the factor of safety against liquefaction occurrence.

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.

Prediction of sulphide build-up in filled sewer pipes

Millions of dollars are being spent worldwide on the repair and maintenance of sewer networks and wastewater treatment plants. The production and emission of hydrogen sulphide has been identified as a major cause of corrosion and odour problems in sewer networks. Accurate prediction of sulphide build-up in a sewer system helps engineers and asset managers to appropriately formulate strategies for optimal sewer management and reliability analysis. This paper presents a novel methodology to model and predict the sulphide build-up for steady state condition in filled sewer pipes. The proposed model is developed using a novel data-driven technique called evolutionary polynomial regression (EPR) and it involves the most effective parameters in the sulphide build-up problem. EPR is a hybrid technique, combining genetic algorithm and least square. It is shown that the proposed model can provide a better prediction for the sulphide build-up as compared with conventional models.

Design and optimization of microstructure of auxetic materials

Purpose – 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 Poissons 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 Poissons ratios.Design/methodology/approach – 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.Findings – Numerical examples show that it is possible to design a large number of new auxetic materials, each with a different value of negative Poissons r...

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...

EPR-based material modelling of soils considering volume changes

In this paper an approach is presented for developing material models for soils based on evolutionary polynomial regression (EPR), taking into account its volumetric behaviour. EPR is a recently developed hybrid data mining technique that searches for structured mathematical equations (representing the behaviour of a system) using genetic algorithm and the least squares method. Stress-strain data from triaxial test are used to train and develop EPR-based material models for soil. The developed models are compared with some of the well known conventional material models. In particular, the capability of the developed EPR models in predicting volume change behaviour of soils is illustrated. It is also shown that the developed EPR-based material models can be incorporated in finite element (FE) analysis. Two geotechnical examples are presented to verify the developed EPR-based FE model (EPR-FEM). The results of the EPR-FEM are compared with those of a standard FEM where conventional constitutive models are used to describe the material behaviour. The results show that EPR-FEM can be successfully employed to analyse geotechnical engineering problems. The advantages of the proposed EPR models are highlighted.

An evolutionary approach to modelling concrete degradation due to sulphuric acid attack

We present a new evolutionary approach for modelling the degradation of concrete.The developed models predict the mass loss of concrete due to acid attack.Optimum concrete mix to maximise resistance against degradation is determined. Concrete corrosion due to sulphuric acid attack is known to be one of the main contributory factors for degradation of concrete sewer pipes. This article proposes to use a novel data mining technique, namely, evolutionary polynomial regression (EPR), to predict degradation of concrete subject to sulphuric acid attack. A comprehensive dataset from literature is collected to train and develop an EPR model for this purpose. The results show that the EPR model can successfully predict mass loss of concrete specimens exposed to sulphuric acid. Parametric studies show that the proposed model is capable of representing the degree to which individual contributing parameters can affect the degradation of concrete. The developed EPR model is compared with a model based on artificial neural network (ANN) and the advantageous of the EPR approach over ANN is highlighted. In addition, based on the developed EPR model and using an optimisation technique, the optimum concrete mixture to provide maximum resistance against sulphuric acid attack has been identified.

Modelling the variation of suction pressure during caisson installation in sand using FLAC3D

A suction caisson is an upturned ‘bucket’ of cylindrical shape made from steel. This type of foundation has been very popular in the oil and gas industry and the current trend is to extend its use to offshore wind farms. Seepage conditions play a pivotal role in suction caisson installation process in sand. Pressure gradients generated by imposed suction inside the caisson cavity cause an overall reduction in the soil resistance around the caisson wall and tip. This transient soil loosening around the caisson wall helps caisson penetration into the seabed. In this paper, we present a study of the role of seepage on the suction caisson installation process in homogenous sand. We also investigate the effects of seepage conditions on soil resistance to caisson penetration with a particular focus on how frictional and tip resistances are differently affected. For this purpose, a series of numerical models are developed using FLAC3D. These models are used to investigate the variation of suction pressure during caisson installation in homogenous sand and to predict the amount of suction required to penetrate the caisson to a certain depth. An explicit strategy is used for each embedment depth, which consists of updating current suction based on displacement history available after the previous prescribed displacement increment. The numerical models are developed for different caisson sizes and wall thicknesses to study the effects of caisson geometry on soil resistance during caisson installation. Problem dimensions are normalised with respect to the diameter of the caisson in order to obtain the results that can be applied to any caisson size. The results showed that suction pressure tends to increase with the embedment depth. Additionally, the overall behaviour and the pressure variation with depth are similar for caissons of different sizes and wall thicknesses. Finally, in order to validate the developed numerical models, data from centrifuge tests are investigated and compared with the results obtained from this study. The developed finite difference models are found to be in good agreement with centrifuge tests, in particular for thicker caissons (t/D = 1%).

Predicting the Probability of Failure of Cementitious Sewer Pipes Using Stochastic Finite Element Method

In this paper, a stochastic finite element method (SFEM) is employed to investigate the probability of failure of cementitious buried sewer pipes subjected to combined effect of corrosion and stresses. A non-linear time-dependant model is used to determine the extent of concrete corrosion. Using the SFEM, the effects of different random variables, including loads, pipe material, and corrosion on the remaining safe life of the cementitious sewer pipes are explored. A numerical example is presented to demonstrate the merit of the proposed SFEM in evaluating the effects of the contributing parameters upon the probability of failure of cementitious sewer pipes. The developed SFEM offers many advantages over traditional probabilistic techniques since it does not use any empirical equations in order to determine failure of pipes. The results of the SFEM can help the concerning industry (e.g., water companies) to better plan their resources by providing accurate prediction for the remaining safe life of cementitious sewer pipes.