Abstract

The main objective of this research is to formulate and couple technologies for modeling discrete and continuous media using real particle morphologies. To that end, two coupled formulations based on virtual modeling technologies of single real particles with another one called real particle packing technique are presented. The first formulation employs Fourier descriptors’ theory to virtually achieve the morphology and construct a repository of real particle geometries. The second formulation is a particle packing method, supported by advancing front techniques combined with dynamic methods. This method presents a stochastic formulation and allows the packing of particle systems following continuous, discrete and empirical statistical distributions. The coupling of both techniques is a very efficient tool to achieve discrete or continuous media geometries to solve engineering problems. Three different examples are developed to illustrate the usefulness of the formulations. The first one is a discrete angle-of-repose problem involving clusters of spheres (real particle morphologies are described with groups of spheres); in the second example the same angle-of-repose problem is resolved with real particles. In the third case, which involves continuous medium mechanics, a small-scale road engineering problem is modeled, specifically, the testing of an asphalt concrete.

Abstract

Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multiclass, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed.

Abstract

Double-curvaturedamsareunique structures for several reasons. Their behaviour changes significantly after joint grouting, when they turn from a set of independent cantilevers into a monolithic structure with arch effect. The construction process has a relevant influence on the stress state, due to the way in which self-weight loads are transmitted, and to the effect on the dissipation of the hydration heat. Temperature variations in the dam body with respect to those existing at joint grouting generate thermal stresses that may be important in the stress state of the structure. It is thus essential to have a realistic estimate of this thermal field, also called reference or

Abstract

Conventional and high-speed train lines are being constructed all over the world with the objective of improving the mobility of both people and goods. Most of these infrastructures are built with railway ballast, a granular material whose main functions are resisting train loads and facing climate actions. The growth in popularity of railway infrastructures has led to an increasing interest in the development of numerical models to evaluate their performance and improve their maintenance. To this respect, the Discrete Element Method (DEM) is an approach that considers the discontinuous nature of granular materials, such as railway ballast. Moreover, it can also be used to compute the behaviour of continuum materials, such as rails and bearing plates, applying the so-called bonded DEM. The code used is developed within DEMPack, a specific software tool for modelling physical problems using the DEM. After evaluating the different geometrical alternatives for representing railway ballast and considering the high amount of material involved in the full-scale tests (more than 130,000 particles) the calculations were carried out using the most efficient option, spheres with rolling friction. This geometrical simplification is not suitable for small-scale tests, however, previous analysis showed that it is accurate enough for reproducing the macroscopic behaviour of the ballast layer. The numerical results correctly capture the effect of changing several parameters such as ballast compaction, inter-particle friction or grain size. It can be concluded that the DEM increases the possibilities for analysing innovative solutions aiming to improve ballasted tracks design, maintenance and performance, since real case-scenarios can be studied with enough accuracy and feasible time.

Abstract

Rail transport, both for people and goods, is becoming increasingly significant all over the world, which is reflected in the great growth of conventional and high-speed train lines. Most of these infrastructures are built with railway ballast, a granular material whose main functions are to resist vertical and horizontal loads and to face climatic actions. The growing popularity of these infrastructures has led to the development of numerical models to evaluate their performance. Among a wide range of numerical methods, the Discrete Element Method (DEM) was found to be effective for evaluating the performance of granular materials. This approach considers their discontinuous nature and has proven to be a useful tool to determine the dynamic behaviour of groups of particles. Moreover, the DEM is also used to compute the behaviour of continuum materials. In this work, rails and bearing plates are characterised in the calculations using this methodology, called the bonded DEM. It is a modification of the classical DEM which assumes that bonds exist between particles, resisting their separation. The code used is developed within DEMPack, a specific software tool for modelling physical problems using the DEM. Currently, DEMPack allows the use of two different types of geometry: spheres with rolling friction and clusters of spheres. A previous analysis showed that spheres are more effective for studying the macroscopic behaviour of the ballast layer, while clusters are necessary for small-scale tests involving highly compacted particles since their results are greatly influenced by particles and contacts distribution. After calibrating the code, full-scale tests were performed applying the load of a high-speed train on a railway track section in different situations. Considering the amount of material (about 260,000 particles) and that the aim is to evaluate the deflection of the rails, the calculations are carried out using spheres. The numerical results correctly capture the effect on the deflection of the rails. It can be concluded that the DEM increases the possibilities for analysing innovative solutions since real case-scenarios can be studied with enough accuracy and feasible time.

Abstract

The discrete element method (DEM) is an emerging tool for the calculation of the behaviour of bulk materials. One of the key features of this method is the explicit integration of the motion equations. Explicit methods are rapid, at the cost of a limited time step to achieve numerical stability. First- or second-order integration schemes based on a Taylor series are frequently used in this framework and shown to be accurate for the translational and rotational motion of spherical particles. However, they may lead to relevant inaccuracies when non-spherical particles are used since the orientation implies a modification in the second-order inertia tensor in the inertial reference frame. Specific integration schemes for non-spherical particles have been proposed in the literature, such as the fourth-order Runge–Kutta scheme presented by Munjiza et al. and the predictor–corrector scheme developed by Zhao and van Wachem which applies the direct multiplication algorithm for integrating the orientation. In this work, both methods are adapted to be used together with a velocity Verlet scheme for the translational integration. The performance of the resulting schemes, as well as that of the direct integration method, is assessed, both in benchmark tests with analytical solution and in real-scale problems. The results suggest that the fourth-order Runge–Kutta and the Zhao and van Wachem schemes are clearly more accurate than the direct integration method without increasing the computational time.

Abstract

Landslide‐generated impulse waves may have catastrophic consequences. The physical phenomenon is difficult to model because of the uncertainties in the kinematics of the mobilised material and to the intrinsic complexity of the fluid–soil interaction. The particle finite element method (PFEM) is a numerical scheme that has successfully been applied to fluid–structure interaction problems. It uses a Lagrangian description to model the motion of nodes (particles) in both the fluid and the solid domains (the latter including soil/rock and structures). A mesh connecting the particles (nodes) is re‐generated at every time step, where the governing equations are solved. Various constitutive laws are used for the sliding mass, including rigid solid and the Newtonian and non‐Newtonian fluids. Several examples of application are presented, corresponding both to experimental tests and to actual full‐scale case studies. The results show that the PFEM can be a useful tool for analysing the risks associated with landslide phenomena, providing a good estimate to the potential hazards even for full‐scale events.

Abstract

The Discrete Element Method (DEM) was found to be an effective numerical method for the calculation of engineering problems involving granular materials. However, the representation of irregular particles using the DEM is a very challenging issue, leading to different geometrical approaches. This document presents a new insight in the application of one of those simplifications known as rolling friction, which avoids excessive rotation when irregular shaped materials are simulated as spheric particles. This new approach, called the Bounded Rolling Friction model, was applied to reproduce a ballast resistance test.

Abstract

The development of high-speed train lines has increased significantly during the last decades leading to more demanding loads in railway infrastructures. Most of these infrastructures were constructed using railway ballast, whose main roles are resisting to vertical and horizontal loads and facing climate action. Moreover, new challenges are arising in the railway industry, such as the development of high-speed train lines in locations with extreme weather. For these reasons, the implementation of a numerical code able to represent ballast behaviour, including its interaction with other structures, has become very attractive. Among a wide range of numerical methods, the Discrete Element Method (DEM) was found to be effective for the calculation of engineering problems with granular materials. This approach considers the discontinuous nature of these materials and has proven to be a very useful tool to obtain complete qualitative information on calculations of groups of particles. The code used in this work is developed within DEMPack, a specific software tool for modelling physical problems using the DEM. The computer program is adapted to meet the needs for representing the behaviour of railway ballast.

Abstract

Railway ballast is a layer of granular material that resists to vertical and horizontal loads, produced by the passing train over the rail. The calculation of this kind of complex geomechanic problems has been traditionally addressed using refined constitutive models, based in continuum assumptions. Although these models may be suitable in the evaluation of the critical state of soils, or in the calculation of bulk material masses flowing, they are not appropriate to represent the local discontinuities of granular materials, which induce special features such as anisotropy or instabilities. The Discrete Element Method (DEM) is an alternative approach that considers the discontinuous nature of granular materials, which has proven to be a very useful tool to obtain complete qualitative information on calculations of groups of particles. However, the computational cost of contact evaluation between Discrete Elements (DEs) is high and limits the calculation capability. In this regard, it should be noted that particle shape greatly affects contact calculation computational cost, being spherical DEs the less computational demanding type of particles. From the point of view of micro-scale analysis, it is essential to represent the exact geometry of the grains. By contrast, if the interest lies in the behaviour of the granular material as a whole, particles geometry is not a determining factor. Therefore, for efficiency purposes, a trade-off between particle shape accuracy and computational cost needs to be achieved. In this work, different approaches to represent ballast stones are assessed: spheres with rolling friction, sphere clusters, polyhedrons and superquadrics. The first two were chosen for further analysis. Rolling friction allows avoiding excessive rotation when irregular shaped materials are simulated as spherical particles. This work presents a new insight for its application called the Bounded Rolling Friction model. Regarding sphere clusters, there is a key point in the friction between elements. As they reproduce irregular particles using clumps of spheres rigidly joined, the cavities between those spheres introduce interlocks that increase friction. To overcome this drawback, a new contact model is proposed. Finally, results of the application of both approaches are displayed, and conclusions are drawn as regards the convenience of using more accurate and computational demanding geometries.