An evolutionary approach to modelling the thermo-mechanical behaviour of unsaturated soils

A new data mining approach is presented for modelling of the stress-strain and volume change behaviour of unsaturated soils considering temperature effects. The proposed approach is based on the evolutionary polynomial regression (EPR), which unlike some other data mining techniques, generates a transparent and structured representation of the behaviour of systems directly from raw experimental (or field) data. The proposed methodology can operate on large quantities of data in order to capture nonlinear and complex relationships between contributing variables. The developed models allow the user to gain a clear insight into the behaviour of the system. Unsaturated triaxial test data from literature was used for development and verification of EPR models. The developed models were also used (in a coupled manner) to produce the entire stress path of triaxial tests. Comparison of the EPR model predictions with the experimental data revealed the robustness and capability of the proposed methodology in capturing and reproducing the constitutive thermo-mechanical behaviour of unsaturated soils. More importantly, the capability of the developed models in accurately generalising the predictions to unseen data cases was illustrated. The results of a sensitivity analysis showed that the models developed from data are able to capture and represent the physical aspects of the unsaturated soil behaviour accurately. The merits and advantages of the proposed methodology are also discussed

A new approach to modeling the behavior of frozen soils

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordIn this paper a new approach is presented for modeling the behavior of frozen soils. A data-mining technique, Evolutionary Polynomial Regression (EPR), is used for modeling the thermo-mechanical behavior of frozen soils including the effects of confining pressure, strain rate and temperature. EPR enables to create explicit and well-structured equations representing the mechanical and thermal behavior of frozen soil using experimental data. A comprehensive set of triaxial tests were carried out on samples of a frozen soil and the data were used for training and verification of the EPR model. The developed EPR model was also used to simulate the entire stress-strain curve of triaxial tests, the data for which were not used during the training of the EPR model. The results of the EPR model predictions were compared with the actual data and it was shown that the proposed methodology can extract and reproduce the behavior of the frozen soil with a very high accuracy. It was also shown that the EPR model is able to accurately generalize the predictions to unseen cases. A sensitivity analysis revealed that the model developed from raw experimental data is able to extract and effectively represent the underlying mechanics of the behavior of frozen soils. The proposed methodology presents a unified approach to modeling of materials that can also help the user gain a deeper insight into the behavior of the materials. The main advantages of the proposed technique in modeling the complex behavior of frozen soil have been highlighted

Development and Applications of Self-learning Simulation in Finite Element Analysis

Numerical analysis such as the finite element analysis (FEA) have been widely used to solve many engineering problems. Constitutive modelling is an important component of any numerical analysis and is used to describe the material behaviour. The accuracy and reliability of numerical analysis is greatly reliant on the constitutive model that is integrated in the finite element code. In recent years, data mining techniques such as artificial neural network (ANN), genetic programming (GP) and evolutionary polynomial regression (EPR) have been employed as alternative approach to the conventional constitutive modelling. In particular, EPR offers great advantages over other data mining techniques. However, these techniques require a large database to learn and extract the material behaviour. On the other hand, the link between laboratory or field tests and numerical analysis is still weak and more investigation is needed to improve the way that they matched each other. Training a data mining technique within the self-learning simulation framework is currently considered as one of the solutions that can be utilised to accurately represent the actual material behaviour. In this thesis an EPR based machine learning technique is utilised in the heart of the self-learning framework with an automation process which is coded in MATLAB environment. The methodology is applied to simulate different material behaviour in a number of structural and geotechnical applications. Two training strategies are used to train the EPR in the developed framework, total stress-strain and incremental stress-strain strategies. The results show that integrating EPR based models in the framework allows to learn the material response during the self-learning process and provide accurate predictions to the actual behaviour. Moreover, for the first time, the behaviour of a complex material, frozen soil, is modelled based on the EPR approach. The results of the EPR model predictions are compared with the actual data and it is shown that the proposed model can capture and reproduce the behaviour of the frozen soil with a very high accuracy. The developed EPR based self-learning methodology presents a unified approach to material modelling that can also help the user to gain a deeper insight into the behaviour of the materials. The methodology is generic and can be extended to modelling different engineering materials

Lateral load bearing capacity modelling of piles in cohesive soils in undrained conditions; an intelligent evolutionary approach

The complex behaviour of fine-grained materials in relation with structural elements has received noticeable attention from geotechnical engineers and designers in recent decades. In this research work an evolutionary approach is presented to create a structured polynomial model for predicting the undrained lateral load bearing capacity of piles. The proposed evolutionary polynomial regression (EPR) technique is an evolutionary data mining methodology that generates a transparent and structured representation of the behaviour of a system directly from raw data. It can operate on large quantities of data in order to capture nonlinear and complex relationships between contributing variables. The developed model allows the user to gain a clear insight into the behaviour of the system. Field measurement data from literature was used to develop the proposed EPR model. Comparison of the proposed model predictions with the results from two empirical models currently being implemented in design works, a neural network-based model from literature and also the field data shows that the EPR model is capable of capturing, predicting and generalising predictions to unseen data cases, for lateral load bearing capacity of piles with very high accuracy. A sensitivity analysis was conducted to evaluate the effect of individual contributing parameters and their contribution to the predictions made by the proposed model. The merits and advantages of the proposed methodology are also discussed

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully

ALERT Doctoral School 2012: advanced experimental techniques in geomechanics

The twenty-second session of the European Graduate School 2012 (called usually ALERT Doctoral School) entitled Advanced experimental techniques in geomechanics is organized by Cino Viggiani, Steve Hall and Enrique Romero.Postprint (published version

Modeling of unconfined compressive strength and Young's modulus of lime and cement stabilized clayey subgrade soil using Evolutionary Polynomial Regression (EPR)

In this study, the evolutionary polynomial regression (EPR) method has been employed to develop simple models with reasonable accuracy to predict the compressive strength and Young's modulus of the lime/cement stabilized clayey subgrade soil. For this purpose, the different specimens with the various cement and lime contents, at three moisture contents (dry side, wet side, and optimum moisture content) were fabricated and were cured for 7, 14, 21, 28 and, 60 days to conduct the unconfined compressive strength (UCS) test. According to the test results, a dataset consisting of 75 records for each additive was prepared. Results of this study show that the R2 value of the developed model for predicting UCS of cement-stabilized clay soil is equal to 0.96 and 0.95 for training and testing sets, respectively. These two values for lime-stabilized soil are 0.91 and 0.87, respectively. Moreover, the R2 for predicting Young's modulus of cement-stabilized clay soil is equal to 0.90 and 0.89 for training and testing set, respectively. These two values for predicting Young's modulus of lime-stabilized soil are 0.88 and 0.94, respectively. The sensitivity analysis showed that for the Portland cement stabilized clayey subgrade, the percentage of the Portland cement and moisture content are the most significant parameters for predicting the UCS and Young's modulus, respectively. In contrast, for the lime-stabilized clayey subgrade soil, the most important parameters are the moisture content and the UCS, respectively

Multiscale soft computing-based model of shear strength of steel fibre-reinforced concrete beams

Concrete is weak in tension, so steel fibres are added to the concrete members to increase shear capability. The shear capacity of steel fibre-reinforced concrete (SFRC) beams is crucial when building reinforced concrete structures. Creating a precise equation to determine the shear resistance of SFRC beams is challenging since many factors can influence the shear capacity of these beams. In addition, the precision available equations to predict the shear capacity are examined. The current research aims to examine the available equations and propose novel and more accurate model to predict the shear capacity of SFRC beams. An innovative evolutionary polynomial regression analysis (EPR- MOGA) is utilized to propose the new equation. The proposed equation offered improved prediction and increased accuracy compared to available equations, where it scored a lower mean absolute error (MAE) and root mean square error (RMSE), a mean (μ) close to the optimum value of 1.0 and a higher coefficient of determination (R 2) when a comparison with literature was conducted. Therefore, the new equation can be employed to assure more resilient and optimized design calculations due to their improved performance.</p

Evaluation of liquefaction potential based on CPT results using C4.5 decision tree

The prediction of liquefaction potential of soil due to an earthquake is an essential task in Civil Engineering. The decision tree is a tree structure consisting of internal and terminal nodes which process the data to ultimately yield a classification. C4.5 is a known algorithm widely used to design decision trees. In this algorithm, a pruning process is carried out to solve the problem of the over-fitting. This article examines the capability of C4.5 decision tree for the prediction of seismic liquefaction potential of soil based on the Cone Penetration Test (CPT) data. The database contains the information about cone resistance (q_c), total vertical stress (σ_0), effective vertical stress (σ_0^'), mean grain size (D_50), normalized peak horizontal acceleration at ground surface (a_max), cyclic stress ratio (τ/σ_0^') and earthquake magnitude (M_w). The overall classification success rate for the entire data set is 98%. The results of C4.5 decision tree have been compared with the available artificial neural network (ANN) and relevance vector machine (RVM) models. The developed C4.5 decision tree provides a viable tool for civil engineers to determine the liquefaction potential of soil

Lattice Element Method and its application to Multiphysics

In this thesis, a Lattice element modelling method is developed and is applied to model the loose and cemented, natural and artificial, granular matters subject to thermo-hydro-mechanical coupled loading conditions. In lattice element method, the lattice nodes which can be considered as the centres of the unit cells, are connected by cohesive links, such as spring beams that can carry normal and shear forces, bending and torsion moment. For the heat transfer due to conduction, the cohesive links are also used to carry heat as 1D pipes, and the physical properties of these rods are computed based on the Hertz contact model. The hydro part is included with the pore network modelling scheme. The voids are inscribed with the pore nodes and connected with throats, and then the meso level flow equation is solved. The Euler-Bernoulli and Timoshenko beams are chosen as the cohesive links or the lattice elements, while the latter should be used when beam elements are short and deep. This property becomes interesting in modelling auxetic materials. The model is applied to study benchmarks in geotechnical engineering. For heat transfer in the dry and full range of saturation, and fractures in the cemented granular media.How through porous media failure behaviours of rocks at high temperature and pressure and granular composites subjected to coupled Thermo hydro Mechanical loads. The model is further extended to capture the wave motion in the heterogeneous granular matter, and a few case studies for the wavefield modification with existing cracks are presented. The developed method is capable of capturing the complex interaction of crack wave interaction with relative ease and at a substantially less computational cost