247 research outputs found
Erratum: Loading-unloading hysteresis loop of randomly rough adhesive contacts (Physical Review E - Statistical, Nonlinear, and Soft Matter Physics (2015) 92 (062404))
No full textThis corrects the article DOI: 10.1103/PhysRevE.92.062404
Loading-unloading hysteresis loop of randomly rough adhesive contacts
No full textWe investigate the loading and unloading behavior of soft solids in adhesive contact with randomly rough
profiles. The roughness is assumed to be described by a self-affine fractal on a limited range of wave vectors.
A spectral method is exploited to generate such randomly rough surfaces. The results are statistically averaged,
and the calculated contact area and applied load are shown as a function of the penetration, for loading and
unloading conditions. We found that the combination of adhesion forces and roughness leads to a hysteresis
loading-unloading loop. This shows that energy can be lost simply as a consequence of roughness and van der
Waals forces, as in this case a large number of local energy minima exist and the system may be trapped in
metastable states. We numerically quantify the hysteretic loss and assess the influence of the surface statistical
properties and the energy of adhesion on the hysteresis process
Strength of anisotropy in a granular material: Linear versus nonlinear contact model
No full textIn this paper, we deal with anisotropy in an idealized granular material made of a collection of frictional, elastic,
contacting particles. We present a theoretical analysis for an aggregate of particles isotropically compressed
and then sheared, in which two possible contacts laws between particles are considered: a linear contact law,
where the contact stiffness is constant; and a nonlinear contact law, where the contact stiffness depends on the
overlapping between particles. In the former case the anisotropy observed in the aggregate is associated with
particle arrangement. In fact, although the aggregate is initially characterized by an isotropic network of contacts,
during the loading, an anisotropic texture develops, which is measured by a fabric tensor.With a nonlinear contact
law it is possible to develop anisotropy because contacting stiffnesses are different, depending on the orientation
of the contact vectors with respect to the axis of the applied deformation. We find that before the peak load is
reached, an aggregate made of particles with a linear contact law develops a much smaller anisotropy compared
with that of an aggregate with a nonlinear law
DEM simulation of anisotropic granular materials:elastic and inelastic behavior
No full textIn this work, Discrete Elements Method simulations are carried out to investigate the effective stiffness of an assembly of frictional, elastic spheres under anisotropic loading. Strain probes, following both forward and backward paths, are performed at several anisotropic levels and the corresponding stress is measured. For very small strain perturbations, we retrieve the linear elastic regime where the same response is measured when incremental loading and unloading are applied. Differently, for a greater magnitude of the incremental strain a different stress is measured, depending on the direction of the perturbation. In the case of unloading probes, the behavior stays elastic until non-linearity is reached.Under forward perturbations, the aggregate shows an intermediate inelastic stiffness, in which the main contribution comes from the normal contact forces. That is, when forward incremental probes are applied the behavior of anisotropic aggregates is an incremental frictionless behavior. In this regime we show that contacts roll or slide so the incremental tangential contact forces are zero. Graphical Abstract: [Figure not available: see fulltext.].</p
A micro-mechanical model for the Biot theory of acoustic waves in a fully saturated granular material
No full textIn the context of the classical Biot theory for acoustic waves in a fully-saturated granular material, we improve upon the constitutive relation of the solid phase by means of micro-mechanical modeling. This is needed to explain discrepancies on the dependency of the frequency with the sound speed attenuation and dispersion between present models and experiment. As first step, we provide a more detailed description of the interaction between the particles and water. A Standard Linear Solid model is proposed to represent the behavior at contact level between particles immersed in a compressible fluid
Deep Change Vector Analysis to Map Bark Beetle Outbreaks in Open Sentinel-2 Data
No full textOpen remote sensing science has been recently
boosted by the free availability of Sentinel-2 images of planet
Earth acquired with the Copernicus programme. In particular,
processing open Sentinel-2 images with Artificial Intelligence (AI)
techniques holds great potential for revolutionizing data science
applications in many domains of Earth sciences. In this paper,
we explore the potential of an unsupervised learning method
designed to process Sentinel-2 images of Earth’s forest scenes
and automate the inventory of forest tree dieback caused by
bark beetle outbreaks. Specifically, we describe PHANTASM: a
method to identify forest tree dieback patches performing the
Change Vector Analysis (CVA) of bi-temporal Sentinel-2 images
of forest scenes. While the traditional CVA strategy is based
on the analysis of pixel-wise differences in spectral values, we
enrich the Sentinel-2 spectrum with both a selection of Spectral
Vegetation Indexes and a Spectral-Spatial Deep Embedding.
The Spectral Vegetation Indexes are pre-defined combinations
of spectral bands commonly designed to enhance the accuracy
of semantic segmentation models trained to map bark beetle
stress in spectral data. The Deep Embedding is a spectralspatial representation of Sentinel-2 pixels trained with a deep
neural network. In particular, we use a pre-trained, semantic
segmentation U-Net to obtain the Deep Embedding that models
the spatial relationship among neighbouring spectral pixels. We
assess the effectiveness of the proposed method in a case study
regarding bark beetle outbreaks in Sentinel-2 images of forest
scenes in the Czech Republi
Potential of Spectral-Spatial Analysis to Map Forest Tree Dieback Due to Bark Beetle Hotspots in Sentinel-2 Images
No full textForest tree dieback inventory plays a crucial role to improve forest management strategies. In this study, we explore the performance of a spectral-spatial machine learning approach used to analyse Sentinel-2 images to detect forest tree dieback events due to bark beetle infestation. We analyse the performance of classification models trained with Random Forest, XGBoost and Multi-Layer Perceptron, as well as semantic segmentation models trained with U-Net by accounting for both spectral and spatial information contained in the remote sensing data. We consider a set of Sentinel-2 images acquired in non-overlapping forest scenes from a region located in the Northeast of France. The selected scenes host bark beetle infestation hotspots originated from the mass reproduction of the bark beetle in the 2018 infestation. Results show that the U-Net model, trained accounting for spectral and spectral-spatial data, achieves the best performance. However, the simpler Random Forest model achieves competitive results with respect to the more complex one, namely U-Net
Storage and Loss Moduli in an Ideal Aggregate of Elastic Disks, with Lubricated Contacts
No full textWe are interested in understanding how elastic waves propagate in a granular material with lubricated contacts. Unlike classical models that attack this problem from a continuum point of view, we choose a micro-mechanical approach, in which the macroscopic stress depends on how particles interact. Because of the complexity of the problem, we present a rather simplified situation, where the aggregate is made of identical elastic disks, isotropically compressed and then incrementally sheared. We assume that particles move with an average rate of deformation and, as they approach, the fluid between them moves out, generating a pressure over the particle surface. We determine this pressure through classical lubrication theory, in which the response to a sinusoidal perturbation provides the storage and loss moduli of the aggregate
An Attention-Based CNN Approach to Detect Forest Tree Dieback Caused by Insect Outbreak in Sentinel-2 Images
No full textForests play a key role in maintaining the balance of ecosystems, regulating climate, conserving biodiversity, and supporting various ecological processes. However, insect outbreaks, particularly bark beetle outbreaks, pose a significant threat to European spruce forest health by causing an increase in forest tree mortality. Therefore, developing accurate forest disturbance inventory strategies is crucial to quantifying and promptly mitigating outbreak diseases and boosting effective environmental management. In this paper, we propose a deep learning-based approach, named AVALON, that implements a CNN to detect tree dieback events in Sentinel-2 images of forest areas. To this aim, each pixel of a Sentinel-2 image is transformed into an imagery representation that sees the pixel within its surrounding pixel neighbourhood. We incorporate an attention mechanism into the CNN architecture to gain accuracy and achieve useful insights from the explanations of the spatial arrangement of model decisions. We assess the effectiveness of the proposed approach in two case studies regarding forest scenes in the Northeast of France and the Czech Republic, which were monitored using Sentinel-2 satellite in October 2018 and September 2020, respectively. Both case studies host bark beetle outbreaks in the considered periods
Sensing inhomogeneous mechanical properties of human corneal Descemet's membrane with AFM nano-indentation
No full textThe paper describes a highly space-resolved characterization of the surface mechanical properties of the posterior human corneal layer (Descemet's membrane). This has been accomplished with Atomic Force Microscopy (AFM) nano-indentation by using a probe with a sharp tip geometry. Results indicate that the contact with this biological tissue in liquid occurs with no (or very low) adhesion. More importantly, under the same operating conditions, a broad distribution of penetration depth can be measured on different x-y positions of the tissue surface, indicating a high inhomogeneity of surface stiffness, not yet clearly reported in the literature. An important contribution to such inhomogeneity should be ascribed to the discontinuous nature of the collagen/proteoglycans fibers matrix tissue, as can be imaged by AFM when the tissue is semi-dry. Using classical contact mechanics calculations adapted to the specific geometry of the tetrahedral tip it has been found that the elastic modulus E of the material in the very proximity of the surface ranges from 0.23 to 2.6 kPa
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