Abstract
The roughness property of rocks is significant in engineering studies due to their mechanical and hydraulic performance and the possibility of quantifying flow velocity and predicting the ...performance of wells and rock mass structures. However, the study of roughness in rocks is usually carried out through 2D linear measurements (through mechanical profilometer equipment), obtaining a coefficient that may not represent the entire rock surface. Thus, based on the hypothesis that it is possible to quantify the roughness coefficient in rock plugs reconstructed three-dimensionally by the computer vision technique, this research aims to an alternative method to determine the roughness coefficient in rock plugs. The point cloud generated from the 3D model of the photogrammetry process was used to measure the distance between each point and a calculated fit plane over the entire rock surface. The roughness was quantified using roughness parameters (
$$R_a$$
R
a
) calculated in hierarchically organized regions. In this hierarchical division, the greater the quantity of division analyzed, the greater the detail of the roughness. The main results show that obtaining the roughness coefficient over the entire surface of the three-dimensional model has peculiarities that would not be observed in the two-dimensional reading. From the 2D measurements, mean roughness values (
$$R_a$$
R
a
) of
$$0.35\,\upmu \hbox {m}$$
0.35
μ
m
and
$$0.235\,\upmu \hbox {m}$$
0.235
μ
m
were obtained for samples 1 and 2, respectively. By the same method, the results of the
$$R_a$$
R
a
coefficient applied three-dimensionally over the entire rocky surface were at most
$$0.165\,\upmu \hbox {m}$$
0.165
μ
m
and
$$0.166\,\upmu \hbox {m}$$
0.166
μ
m
, respectively, showing the difference in values along the surface and the importance of this approach.
The quantitative determination of average roughness parameters, from the determination of height variations of the surface points, is frequently used to estimate the adhesion between an adhesive and ...the surface of a substrate. However, to determine the interaction between an adhesive and a surface of a heterogeneous material, such as a red ceramic, it is essential to define other roughness parameters. This work proposes a method for determining the roughness of red ceramic blocks from a three-dimensional evaluation, with the objective of estimating the contact area that the ceramic substrate can provide for a cementitious matrix. The study determines the average surface roughness from multiple planes and proposes the adoption of 2 more roughness parameters, the valley area index and the average valley area. The results demonstrate that there are advantages in using the proposed multiple plane method for roughness computation and that the valley area parameters are efficient to estimate the extent of adhesion between the materials involved.
The adhesion of coating mortars is influenced by various factors, with the roughness of the substrates being particularly important. Surface roughness encompasses irregularities, including peaks and ...valleys, which can impact the adhesive properties between the cementitious matrix and the substrate. This study quantitatively analyses the roughness, topography, and interface area influence between ceramic substrates and cementitious coating on tensile bond strength. A single type of paste was applied to 400 surfaces of clay hollow blocks with varying roughness levels. The ceramic substrates were characterized using 3D laser profilometry to assess roughness. The study’s findings reveal crucial insights into average tensile bond strengths concerning specific roughness parameters, notably average valley area (AVA) and average valley height (AHV). It was observed that deeper valleys necessitated broader lake expansions for optimum adhesion. Remarkably improved average tensile bond strength was achieved at AHV values ranging from 2.16 to 2.78 µm and at 0.98 µm, beginning from an AVA of 0.19 mm
2
. Furthermore, even at an AVA of 0.07 mm
2
, a notable increase in adhesion was observed. In conclusion, this study highlights the profound influence of roughness parameters, AVA and AHV, on the adhesion between clay hollow blocks and cementitious matrices. The comprehensive analysis of surface roughness and its impact on adhesion provides valuable insights for optimizing coating mortar applications in construction and engineering practices.
Fracture modeling plays a valuable role to understand the fluid flow in carbonate reservoirs. For this, the fracture characterization to generate Discrete Fracture Networks (DFNs) can take advantage ...of analogue outcrops through Virtual Outcrop Models (VOMs), acquired by Unmanned Aerial Vehicles (UAV) and digital photogrammetry. The stochastic DFN generation is an important step in reservoir modeling as it brings more representative data to the process and has long been studied. However, optimizations concerning automatizing some of the steps necessary to its generation like data clustering are still open to advancements. In this sense, this work aims the fracture data clustering and the definition of the number of clusters when gathering data for the stochastic process, developing an Elbow method for spherical data and a balanced K-means, both based on Fisher statistics. For this, we interpreted fracture planes in a VOM that recreates a carbonate reservoir analogue from the Jandaíra Formation, in the Northeast, Brazil. As result, we show a workflow for immersive fracture interpretation alongside a 3D stochastic DFN model with fracture intensity of 22.57m −1 for cell sizes of 1m 3 . Regarding the clustering balance, our method achieved a lower standard deviation between sets while maintaining the Fisher values greater to obtain fracture sets with lower dispersion. Additionally, the Elbow method implementation proved a beneficial step to the workflow as it reduced the interpretation bias of family clusters. These results alongside the proposed workflow bring a better understanding of the outcrop geometry while offering data scalability for reservoir modeling.
The method of measuring the roughness of ceramic substrates is not consensual, with unsuccessful attempts to associate roughness with the adhesion of coatings because the ceramic blocks have ...different areas of contact, shapes, and dimensions of the roughness as well as the extrusion process influences the mechanical anisotropy of the block. The goal of this work is a quantification and comparison of roughness data obtained by 2D and 3D methods, evaluating the variations of results between the measurement methods and formulating a critical analysis regarding the quality of the information obtained with each method. For this propose, four sets of ceramic blocks with different firing temperature were produced, in order to provide groups of blocks with different surface topographies in which the roughness was estimated. The roughness measurements were made in 4608 regions, resulting in 1536 values using 2D method and 3072 values using 3D method. In the 2D method for ceramic blocks, the measurement orientation strongly influences the result, depending on the measurement position and orientation. The 3D method generates a higher average value and allows to identify roughness variations typical of the ceramic block. The roughness estimation of a ceramic block surface must be done using the 3D method.
Quality evaluation of a material's surface is performed through roughness analysis of surface samples. Several techniques have been presented to achieve this goal, including geometrical analysis and ...surface roughness analysis. Geometric analysis allows a visual and subjective evaluation of roughness (a qualitative assessment), whereas computation of the roughness parameters is a quantitative assessment and allows a standardized analysis of the surfaces. In civil engineering, the process is performed with mechanical profilometer equipment (2D) without adequate accuracy and laser profilometer (3D) with no consensus on how to interpret the result quantitatively. This work proposes a new method to evaluate surface roughness, starting from the generation of a visual surface roughness signature, which is calculated through the roughness parameters computed in hierarchically organized regions. The evaluation tools presented in this new method provide a local and more accurate evaluation of the computed coefficients. In the tests performed it was possible to quantitatively analyze roughness differences between ceramic blocks and to find that a quantitative microscale analysis allows to identify the largest variation of roughness parameters R
avg, R
sdv, R
min and R
max between samples, which benefit the evaluation and comparison of the sampled surfaces.
Abstrac This study assesses the joint influence of capillary absorption and substrate roughness on the adhesive strength of a cementitious matrix on brick substrate. One cementitious rendering and ...two substrates with different water absorption and roughness were used. The capillary absorption coefficient and the roughness coefficient were determined in 1cm2 test areas to then evaluate the matrix tensile bond strength and correlate it with the properties of the substrates. The results were validated by SEM and AFM analyses. Substrates with higher capillary absorption and lower roughness presented higher tensile bond strength. Micro and nanoscale analyses led us to conclude that, in the substrates used, the higher capillary absorption and the lower roughness generate a denser and less porous paste-substrate interface, suggesting a higher extent of contact between the hydrated paste and the substrate and, consequently, higher adhesive strength.
Resumo Este estudo avalia a influência conjunta da absorção capilar e da rugosidade do substrato na adesão de uma matriz cimentícia em um bloco cerâmico. Foram usados um revestimento cimentício e dois substratos com absorção de água e rugosidade diferentes. O coeficiente de absorção capilar e o coeficiente de rugosidade da superfície foram determinados em áreas de teste de 1 cm 2 , sendo correlacionados com a resistência de aderência. Os resultados foram validados por análises de MEV e MFA. Os substratos com maior absorção capilar e menor rugosidade apresentaram maior resistência à tração. As análises em micro e nanoescala permitem concluir que, nos substratos utilizados, a maior absorção capilar e a menor rugosidade geram uma interface pasta-substrato mais densa e menos porosa, sugerindo uma maior extensão de contato entre a pasta hidratada e o substrato e, consequentemente, maior resistência de aderência.
Image morphing has been extensively studied in computer graphics and it can be summarized as follows: given two input images, morphing algorithms produce a sequence of inbetween images which ...transforms the source image into the target image in a visually pleasant way. In this paper, we propose an algorithm, based on recent advances from texture-from-sample ideas, which synthesizes a metamorphosis sequence targeted specifically for textures. We use the idea of binary masks—or texton masks—to control and drive the morphing sequence. Our solution provides an automatic mapping from source texels into target texels and thus guarantees a coherent and visually smoothing transition from the source texture into the target texture. We compare our morphing results with prior work and with simple interpolation between corresponding pixels in the same source and target images.
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Este trabalho apresenta um modelo para ...síntese de texturas a partir de amostras, mais especificamente para síntese de texturas com elementos que variam progressivamente ao longo da textura, conhecidas como Progressively-Variant Textures (PVT). Este tipo de textura é comum na Natureza, particularmente na pelagem de animais como leopardos.
Os algoritmos de síntese existentes, na grande maioria, não permitem qualquer controle sobre o resultado final, ou então, os que permitem, são limitados ou tem problemas de
performance inerente ao método de síntese utilizado (pixel-a-pixel). Para resolvermos estes problemas, propomos um algoritmo baseado numa nova unidade de síntese: o texel.
O nosso algoritmo sintetiza uma nova textura agrupando texels que satisfazem um critério de similaridade numa vizinhança. As características de uma PVT são obtidas
com transformações afim e operações morfológicas aplicadas sobre os texels e definidas pelo usuário. Os resultados obtidos apresentam uma ótima qualidade visual para uma
We present a model for texture synthesis from samples, particularly for synthesis of textures with smooth variation of the texture elements, known as Progressively-Variant
Textures (PVT). This type of textures is common in Nature, specially in animal coat markings such as leopards. Current solutions, for the most part, do not allow much control by the user or the ones which do allow control are limited or have problems due to the pixel-at-a-time nature of synthesis. We address these problems with the introduction of an algorithm based on a new building block for synthesis: the texel. Our solution builds a new texture grouping texels which satisfy a neighborhood similarity criterion. The PVT features are introduced with affine transformations and morphological operators defined by the user and applied to the texels. The results show a good visual quality for a large number of sample textures, including cases where current solutions for natural textures fail. Besides, the many possibilities for variation of te
New advances in image based texture synthesis techniques allow the generation of arbitrarily sized textures based on a small sample. The generated textures are perceived as very similar to the given ...sample. One main drawback of these techniques, however, is that the synthesized result cannot be locally controlled, that is, we are able to synthesize a larger version of the sample but without much variation. We present in this paper a technique which improves on current fast texture synthesis techniques by allowing local control over the result. By local control we mean a final texture that is still perceived as a whole but presents variations in size of the basic elements. Our solution generates the final texture from a small collection of the same sample at different resolutions, adequately interpolated. We illustrate our results with some examples, including natural textures such as animal coat patterns, which exhibit local variations that can be adequately captured by our algorithm.
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