Modellazione agli elementi discreti di prove di punzonamento di una rete corticale doppio torta a maglia esagonale

Le reti metalliche sono comunemente usate per la protezione su pendio e la prevenzione del rischio di crolli e distacchi. La loro progettazione \ue8 principalmente basata su considerazioni di carattere empirico, legate all\u2019esperienza del progettista; data l\u2019importanza di tali interventi, e al fine di ottimizzare il progetto, stanno entrando in uso nuovi metodi numerici. In questo lavoro verr\ue0 impiegato il metodo agli elementi discreti (DEM), particolarmente adatto per lo studio di problemi a grande deformazioni, fino alla rottura degli elementi. L\u2019obiettivo \ue8 quello di validare un modello di rete doppio torta a maglia esagonale durante una prova di punzonamento attraverso il raffronto con i risultati sperimentali. In particolare verranno analizzati tre diversi modelli costitutivi e l\u2019influenza delle curve tensione-deformazione relative al filo singolo e a quello doppio torto

INFLUENCE OF ILLUMINATION CHANGES ON IMAGE-BASED 3D SURFACE RECONSTRUCTION

Abstract. The paper investigates the influence of lighting conditions on image-based 3D surface reconstruction, with particular focus on periodic photogrammetric surveys for monitoring and 3D mapping applications. The analyses focus on the accuracy and completeness of each DSM and the daily and hourly repeatability of repeated photogrammetric surveys. Three test sites with rock slopes with a different orientation to the sun and different slope characteristics (slope, pattern, amount of outcropping elements that cast shadows) have been considered to ensure that results can give a general indication of the behaviours in different light conditions. In addition, a simulated virtual test site is included in the study to allow controlled image acquisition and evaluate the effect of the sun's inclination on the DSM accuracy without influence of other weather conditions. The results show that, although there is an optimal time for the acquisitions, if particularly unfavourable light conditions are excluded, the accuracy reduction with time variation is always below 30%. The repeatability analyses by day and by time highlight a good consistence between DEMs belonging to the same day but acquired at different times and, also, between DEMs acquired at the same time but on different days. This suggests that reliable results can be obtained during continuous monitoring of, for instance, rock faces to identify rockfalls

A simplified scheme for piezoelectric anisotropic analysis in human vertebrae using integral methods

This paper outlines a computational model for the analysis of the piezoelectric behaviour of the vertebral body remodelling process. Particular attention is paid to the algorithms for the simulation of the stress energy density for each point of the geometry and the distribution of the density in the bone. In addition, the model takes into account the piezoelectric effect and the anisotropy (transversal isotropy) of the bone. A model for internal anisotropic piezoelectric bone remodelling of a human vertebra is discussed in detail. The model consists of the implementation of an algorithm which includes the elastic and electric variables in a single equation using boundary element method. The presented results show a good agreement with biological data and the model does not include any electric additional charge.Peer ReviewedPostprint (published version

a Comparison of Low-Cost Cameras Applied to Fixed Multi-Image Monitoring Systems

Abstract. Photogrammetry is becoming a widely used technique for slope monitoring and rock fall data collection. Its scalability, simplicity of components and low costs for hardware and operations makes its use constantly increasing for both civil and mining applications. Recent on site permanent installation of cameras resulted particularly viable for the monitoring of extended surfaces at very reasonable costs. The current work investigates the performances of a customised Raspberry Pi camera module V2 system and three additional low-cost camera systems including an ELP-USB8MP02G camera module, a compact digital camera (Nikon S3100) and a DSLR (Nikon D3). All system, except the Nikon D3, are available at comparable price. The comparison was conducted by collecting images of rock surfaces, one located in Australia and three located in Italy, from distances between 55 and 110 m. Results are presented in terms of image quality and three dimensional reconstruction error. Thereby, the multi-view reconstructions are compared to a reference model acquired with a terrestrial laser scanner

An iterative Bayesian filtering framework for fast and automated calibration of DEM models

The nonlinear, history-dependent macroscopic behavior of a granular material is rooted in the micromechanics between constituent particles and irreversible, plastic deformations reflected by changes in the microstructure. The discrete element method (DEM) can predict the evolution of the microstructure resulting from interparticle interactions. However, micromechanical parameters at contact and particle levels are generally unknown because of the diversity of granular materials with respect to their surfaces, shapes, disorder and anisotropy. The proposed iterative Bayesian filter consists in recursively updating the posterior distribution of model parameters and iterating the process with new samples drawn from a proposal density in highly probable parameter spaces. Over iterations the proposal density is progressively localized near the posterior modes, which allows automated zooming towards optimal solutions. The Dirichlet process Gaussian mixture is trained with sparse and high dimensional data from the previous iteration to update the proposal density. As an example, the probability distribution of the micromechanical parameters is estimated, conditioning on the experimentally measured stress–strain behavior of a granular assembly. Four micromechanical parameters, i.e., contact-level Young’s modulus, interparticle friction, rolling stiffness and rolling friction, are chosen as strongly relevant for the macroscopic behavior. The a priori particle configuration is obtained from 3D X-ray computed tomography images. The a posteriori expectation of each micromechanical parameter converges within four iterations, leading to an excellent agreement between the experimental data and the numerical predictions. As new result, the proposed framework provides a deeper understanding of the correlations among micromechanical parameters and between the micro- and macro-parameters/quantities of interest, including their uncertainties. Therefore, the iterative Bayesian filtering framework has a great potential for quantifying parameter uncertainties and their propagation across various scales in granular materials

Performance study of iterative Bayesian filtering to develop an efficient calibration framework for DEM

This work presents an efficient probabilistic framework for the Bayesian calibration of micro-mechanical parameters for Discrete Element Method (DEM) modelling. Firstly, the superior behaviour of the iterative Bayesian filter over the sequential Monte Carlo filter for calibrating micro-mechanical parameters is shown. The linear contact model with rolling resistance is used for simulating the triaxial responses of Toyoura sand under different confining pressures. Secondly, synthetic data from DEM simulations of triaxial compression are used to assess the reliability of iterative Bayesian filtering with respect to the user-defined parameters, such as the number of samples and predefined parameter ranges. Excellent calibration results with errors between 1 and 2% are obtained when the number of samples is chosen high enough. It is crucial that the sample size is representative for the distribution of individual parameters within the predefined parameter ranges. The wider the ranges, the more samples are required. The investigation also shows the necessity of including both stress and strain histories, at certain confidence levels, for estimation of the correct mechanical responses, especially the correct fabric responses. Finally, based on the findings of this work a fully-automated open-source calibration tool is developed and demonstrated for selected stress paths

An analytical solution for geotextile-wrapped soil based on insights from DEM analysis

This paper presents a novel analytical solution for geotextile-wrapped soil based on a comprehensive numerical analysis conducted using the discrete element method (DEM). By examining the soil–geotextile interface friction, principal stress distribution, and stress–strain relations of the constituent soil and geotextile in the DEM analysis, a complete picture of the mechanical characterization of geotextile-wrapped soil under uniaxial compression is first provided. With these new insights, key assumptions are verified and developed for the proposed analytical solution. In the DEM analysis, a near-failure state line that predicts stress ratios relative to the maximums at failure with respect to deviatoric strain is uniquely identified; dilation rates are found to be related to stress ratios via a single linear correlation regardless of the tensile stiffness of the geotextile. From these new findings, the assumptions on the stress-state evolution and the stress–dilatancy relation are developed accordingly, and the wrapped granular soil can therefore be modeled as a Mohr–Coulomb elastoplastic solid with evolving stress ratio and dilation rate. The development of the proposed analytical model also demonstrates an innovative approach to take advantage of multiscale insights for the analytical modeling of complex geomaterials. The analytical model is validated with the DEM simulation results of geotextile-wrapped soil under uniaxial compression, considering a wide range of geotextile tensile stiffnesses. To further examine the predictive capacity of the analytical model, the stress–strain response under triaxial compression conditions is solved analytically, taking both different confining pressures and geotextile tensile stiffnesses into account. Good agreement is obtained between the analytical and DEM solutions, which suggests that the key assumptions developed in the uniaxial compression conditions also remain valid for triaxial compression conditions

Calibration of micromechanical parameters for DEM simulations by using the particle filter

The calibration of DEM models is typically accomplished by trail and error. However, the procedure lacks of objectivity and has several uncertainties. To deal with these issues, the particle filter is employed as a novel approach to calibrate DEM models of granular soils. The posterior probability distribution of the microparameters that give numerical results in good agreement with the experimental response of a Toyoura sand specimen is approximated by independent model trajectories, referred as ‘particles’, based on Monte Carlo sampling. The soil specimen is modeled by polydisperse packings with different numbers of spherical grains. Prepared in ‘stress-free’ states, the packings are subjected to triaxial quasistatic loading. Given the experimental data, the posterior probability distribution is incrementally updated, until convergence is reached. The resulting ‘particles’ with higher weights are identified as the calibration results. The evolutions of the weighted averages and posterior probability distribution of the micro-parameters are plotted to show the advantage of using a particle filter, i.e., multiple solutions are identified for each parameter with known probabilities of reproducing the experimental response