COMPRESSIVE PROPERTIES OF AUXETIC STRUCTURES WITH CONTROLLED STIFFNESS OF STRUT JOINTS

Presented paper deals with experimental study on compressive properties of auxetics with controlled stiffness of strut joints. The variable strut joints properties were simulated by adding extra amount of material in the struts’ intersection regions. Four groups of inverted honeycomb structures were prepared by multi-jet 3D printing and tested in quasi-static compression. The structure collapsed gradually, however after the first collapse, failure in entire cross-section occurred due to the brittle nature of the base material. The behavior up to the first collapse was consistent among the specimens within each group, while differed slightly subsequently. With higher reinforcement in the joints, results showed increasing stress at the first collapse (ultimate compressive stress) while the strain at the first collapse remained unchanged. The auxetic behaviour became less significant with increasing joints’ reinforcement

HIGH STRAIN-RATE COMPRESSIVE TESTING OF FILLING MATERIALS FOR INTER-PENETRATING PHASE COMPOSITES

In this study behavior of the selected types of filling material for the inter-penetrating phase composites was tested in compressive loading mode at low and high strain-rates. Three types of the filling material were tested, (i) ordnance gelatin, (ii) low expansion polyurethane foam, and (iii) polyurethane putty. To evaluate their impact energy absorption bulk samples of the selected materials were tested in compression loading mode at strain-rates 1000 s−1 to 4000 s−1. The high strain-rate compressive loading was provided by Split Hopkinson Pressure Bar (SHPB) which was equipped with PMMA bars to enable testing of cellular materials with low mechanical impedance. Based on the comparative measurement response to compression at both low and high strain-rates was analysed. The results show a significant strain-rate sensitivity of the ordnance gelatin and of the polyurethane putty, while strain-rate effect in the polyurethane foam was not observed

SEMI–AUTOMATED ASSESSMENT OF MICROMECHANICAL PROPERTIES OF THE METAL FOAMS ON THE CELL-WALL LEVEL

Metal foams are innovative porous material used for wide range of application such as deformation energy or sound absorption, filter material, or microbiological incubation carrier. To predict mechanical properties of the metal foam is necessary to precisely describe elasto–plastic properties of the foam on cell–wall level. Indentation with low load is suitable tool for this purpose. In this paper custom designed instrumented microindentation device was used for measurement of cell-wall characteristics of two different aluminium foams (ALPORAS and ALCORAS). To demonstrate the possibility of automated statistical estimation of measured characteristics the device had been enhanced by semi-automatic indent positioning and evaluation procedures based on user-defined grid. Vickers hardness was measured on two samples made from ALPORAS aluminium foam and one sample from ALCORAS aluminium foam. Average Vickers hardness of ALPORAS foam was 24.465HV1.019 and average Vickers hardness of ALCORAS was 36.585HV1.019

IMPACT TESTING OF ORDNANCE GELATINE UNDER MODERATE STRAIN RATE CONDITIONS

An experimental study on energy absorption capabilities and strain rate sensitivity of ordnance gelatine was performed. Strain energy density under quasi static compression and moderate strain rate impact tests was compared. In the study two types of material were tested, bulk ordnance gelatine and polymeric open-cell meshwork filled with ordnance gelatine. From the results a significant strain-rate effect was observed in terms of ultimate compressive strength and strain energy density. In comparison of the deformation behaviour under quasi static conditions and drop weight test the difference was very significant, however slight increase in both strength and strain energy density was observed even between different impact energies and velocities during the impact testing. The peak acceleration was significantly reduced in polymer meshwork filled by gelatine in comparison to the bulk gelatine

Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation

Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature\u27s fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel\u27s class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. Fuzzy classification comes close in terms of information loss and likewise preserves the continuous nature of vegetation, however, still relying on a priori classification. The need for a priori classification may be a disadvantage or, in other cases, an advantage because it allows using categorical input data instead of the detailed vegetation records required for ordination. Both approaches support spatially explicit accuracy analyses, which further improves the usefulness of the output maps. Gradient mapping and fuzzy classification offer various advantages over hard classification, can always be transformed into a crisp map and are generally applicable to various data structures. We thus recommend the use of these approaches over hard classification for applications in ecological research

UTILIZATION OF IMAGE AND SIGNAL PROCESSING TECHNIQUES FOR ASSESSMENT OF BUILT HERITAGE CONDITION

Historical buildings represent invaluable heritage from the past and therefore their protection is a very important task. Assessment of their condition must not cause damage accumulation, thus the least possible volume removed from the structure is essential. As many historical buildings in the Czech Republic are built using sandstone that can be considered as a typical heterogeneous system, statistical signal processing is a promising approach for determination of the representative volume element (RVE) dimensions. Such calculations can be carried out on the domain of logical arrays representing binary images of the materials microstructure. This paper deals with processing of image data obtained using SEM-BSE and high resolution flatbed scanner for determination of RVE dimensions. Advanced image processing techniques are employed and results from calculation using grayscale data are presented and compared with results calculated on the basis of color input images

Gradient-based assessment of habitat quality for spectral ecosystem monitoring

The monitoring of ecosystems alterations has become a crucial task in order to develop valuable habitats for rare and threatened species. The information extracted from hyperspectral remote sensing data enables the generation of highly spatially resolved analyses of such species’ habitats. In our study we combine information from a species ordination with hyperspectral reflectance signatures to predict occurrence probabilities for Natura 2000 habitat types and their conservation status. We examine how accurate habitat types and habitat threat, expressed by pressure indicators, can be described in an ordination space using spatial correlation functions from the geostatistic approach. We modeled habitat quality assessment parameters using floristic gradients derived by non-metric multidimensional scaling on the basis of 58 field plots. In the resulting ordination space, the variance structure of habitat types and pressure indicators could be explained by 69% up to 95% with fitted variogram models with a correlation to terrestrial mapping of >0.8. Models could be used to predict habitat type probability, habitat transition, and pressure indicators continuously over the whole ordination space. Finally, partial least squares regression (PLSR) was used to relate spectral information from AISA DUAL imagery to floristic pattern and related habitat quality. In general, spectral transferability is supported by strong correlation to ordination axes scores (R$^{2}$ = 0.79–0.85), whereas second axis of dry heaths (R$^{2}$ = 0.13) and first axis for pioneer grasslands (R$^{2}$ = 0.49) are more difficult to describe

INFLUENCE OF PRINTING AND LOADING DIRECTION ON MECHANICAL RESPONSE IN 3D PRINTED MODELS OF HUMAN TRABECULAR BONE

The paper deals with investigation on directional variations of mechanical response in 3D printed models of human trabecular bone. Sample of trabecular bone tissue was resected from human donor and 3D model was obtained by X-ray computed tomography. Then a series of cubical samples was prepared by additive manufacturing technique and tested by uniaxial compression loading mode. Mechanical response was compared in nine different combinations of direction of 3D printing and loading direction. The results show neglectible influence on the deformation response in elastic region (stiffness) and significant changes of the behaviour in plastic region (stress and strain at yield point, strain at full collapse)

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field