The round robin test (the simultaneous analysis of the same problem) is a method to investigate the variance and sensitivity of results provided by different analysts for a given problem and the ...reliability of the particular software used by each group participating in the test. A round robin test has been conducted for the traditional numerical method (e.g., finite difference method), but not yet for the discrete element method (DEM). This paper presents the results of the first ever round robin test on the DEM simulation for the angle of repose, involving 16 groups from around the world using different softwares. Within the scope of this round robin test, most groups reported similar simulation results for the angle of repose that differed only by a few degrees from the average of the experimental values, which was initially concealed from participants. There was also good agreement on the degree of variance of the angle of repose. In addition, this paper revealed the recent trends on the interparticle constitutive models and DEM softwares by considering the reports obtained from the participants.
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.
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.
The discrete element method (DEM) is well suited for calculating the behaviour of bulk materials. However, its application is limited because of the cumbersome calibration process required. Trial and ...error calibration can be useful for the computation of single outputs, but is unfeasible when the aim is reproducing more complex phenomena with high accuracy. This paper describes an iterative procedure based on machine learning to automatically calibrate the parameters of DEM models for reproducing the behaviour of bulk materials. The performance of the methodology is assessed by its application to the calibration of a DEM model to compute the stress–strain evolution of a cohesive material under uniaxial compression. In this case, a random forest model is used in conjunction with the iterative calibration algorithm proposed. The results of this study show that the algorithm is accurate and flexible for the calibration of material parameters.
Decision making in dam safety is fundamentally based on the comparison between the predictions of a behavior model and the records of the monitoring system. Traditionally, simple linear regression ...models have been used. Recently, models based on machine learning are being explored, which generally offer greater precision –therefore, greater capacity for detecting anomalies –, higher flexibility and versatility. We have developed an interactive application based on R-Shiny to generate models based on boosted regression trees, evaluate their accuracy and analyze the effect of predictor variables on the system response. This allows for identifying changes in dam behavior, detecting potential anomalies and better understanding the effect of the loads on the structure. The availability of the software will contribute to the penetration of machine learning techniques in the dam engineering sector and will open the door to its use in structural health monitoring for other civil infrastructures.
•A user-friendly application for building machine-learning based predictive models.•The usefulness and applicability is proven by previous scientific publications.•Interactive data exploration, model fitting and interpretation.•Developed for dam monitoring data analysis, but can be applied in other settings.•Contributes to the improvement of dam safety procedures.
Double-curvature dams are unique 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 closing temperature. In this work, the factors involved in the calculation of the reference temperature of double-curvature arch dams are analysed: material properties, boundary conditions and numerical aspects. First, a critical review of the state of the art is made with respect to the criteria used by various authors for decision-making in the construction of the model. Next, specific analyses are made on the effect of some important elements: the time step, the size of the domain of analysis and the methodology used for the calculation of the reference temperature. The results show the relevance of a correct calculation of the closing temperature to adequately determine the stress state of the structure.
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: multi-class, 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.
In this work, we present a new methodology for the treatment of the contact interaction between rigid boundaries and spherical discrete elements (DE). Rigid body parts are present in most of ...large-scale simulations. The surfaces of the rigid parts are commonly meshed with a finite element-like (FE) discretization. The contact detection and calculation between those DE and the discretized boundaries is not straightforward and has been addressed by different approaches. The algorithm presented in this paper considers the contact of the DEs with the geometric primitives of a FE mesh, i.e. facet, edge or vertex. To do so, the original hierarchical method presented by Horner et al. (J Eng Mech 127(10):1027–1032,
2001
) is extended with a new insight leading to a robust, fast and accurate 3D contact algorithm which is fully parallelizable. The implementation of the method has been developed in order to deal ideally with triangles and quadrilaterals. If the boundaries are discretized with another type of geometries, the method can be easily extended to higher order planar convex polyhedra. A detailed description of the procedure followed to treat a wide range of cases is presented. The description of the developed algorithm and its validation is verified with several practical examples. The parallelization capabilities and the obtained performance are presented with the study of an industrial application example.
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.
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