Relevant bibliographies by topics / Granite Concrete Image processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Granite Concrete Image processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Granite Concrete Image processing"

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CHEN Jian-li, 陈建立. "Concrete detection method based on image processing." Chinese Journal of Liquid Crystals and Displays 35, no. 4 (2020): 395–401. http://dx.doi.org/10.3788/yjyxs20203504.0395.

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Zhu, Ze Qi, Qian Sheng, and Xiao Dong Fu. "Numerical Simulation of Fracture Propagation of Heterogeneous Material." Applied Mechanics and Materials 170-173 (May 2012): 581–84. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.581.

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For the Three Gorges granite, a finite difference software package FLAC was used to study failure process of heterogeneous rock material. Based on mineral components identification results of the granite and fuzzy clustering method, the actual image date was transformed into the finite difference grid by applying image processing techniques. A convenient and efficient two-dimensional numerical modeling method for heterogeneous geomaterials was presented. Then, failure process of the granite were simulated in uniaxial compression test based on experimental strain soft model, and the stress concentration phenomenon was analyzed. The results show that the numerical modeling method based on digital image processing can be used to calculate the mechanical responses of geomaterials by taking their heterogeneities into considerations.
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Zhadanovsky, Boris, Sergey Sinenko, and Alexey Slavin. "Processing of exterior light concrete wall panels." MATEC Web of Conferences 193 (2018): 03017. http://dx.doi.org/10.1051/matecconf/201819303017.

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The characteristics of wall panels, the facing element of which is porous concrete, are given. For exterior wall panels, laboratory suggested the exposed porous concrete of grades B7,5 and B10 based on white and grey cements M400. As aggregates, one uses limestone and granite mined in the corresponding quarry. Air-entangling additives are applied in concrete manufacturing, due to which placeability of concrete mix is improved and volumetric weight of concrete is reduced. In its turn, it allowed reducing water absorption and enhancing freeze resistance of products. Presence of fine-splitted air in formation of closed pores (pore forming) improves the structure of exposed concrete, bringing its properties closer to the ones of porous claydite-concrete as the essential material of exterior wall panel. Homogeneity of concrete by durability came to 0,70 with variability coefficient equal to 0,11, what exceeds regulatory requirements. Its factory features and way of production are indicated. The range of application and the actual cost of such panels are identified.
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Tian, Zhao, Che, Zhao, and Xin. "Concrete Crack Identification and Image Mosaic Based on Image Processing." Applied Sciences 9, no. 22 (2019): 4826. http://dx.doi.org/10.3390/app9224826.

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Crack assessment is an essential process in bridge detection. In general, most non-contact crack detection techniques are not suitable for widespread use. The reason for this is that they all need to position the ruler at the inspection site in advance or calibrate the camera unit pixel size at a certain distance in a very intricate process. However, the object distance method in this paper can complete the calculation using only the crack image and the working distance, which are provided by an acquisition system equipped with a camera and laser range finder. First, the object distance method and the scale method are compared by calculating the crack width, and the results show that the object distance method is the more accurate method. Then, a double edge pixel statistical method is proposed to calculate the crack length, which solves the problem of redundant and missing pixels. In addition, the conventional mosaic algorithm is improved to realize an image mosaic for the more efficient splicing of crack images. Finally, a series of laboratory tests were conducted to verify the proposed approach. The experiments showed that the precision of crack length extraction can reach 92%, and the improved algorithm stitching precision can reach 98%.
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Sonwane, Rajiv, Pushpendra Kumar Kushwaha, and Jiji M. Thomas. "Review on The Production Of Concrete By Using Waste Coarse Material." IJOSTHE 6, no. 6 (2019): 3. http://dx.doi.org/10.24113/ojssports.v6i6.111.

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Marble Industry produces large amount of waste during mining and processing stages. This waste is dumped on to open land which creates a lot of environmental problems. Similarly granite is also produced in the same manner in great amount. We get recycle aggregate from the old dumped structures and buildings. the main objective of this study was utilization of marble, granite and recycled aggregate waste with polypropylene fiber as a replacement for conventional natural coarse aggregates in concrete.
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Sonwane, Rajiv, Pushpendra Kumar Kushwaha, and Jiji M. Thomas. "Study of the Compressive Strength of Concrete Using Marble, Granite and Recycled Aggregates with Polypropylene Fiber." SMART MOVES JOURNAL IJOSCIENCE 5, no. 6 (2019): 6. http://dx.doi.org/10.24113/ijo-science.v5i6.242.

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Marble Industry produces large amount of waste during mining and processing stages. This waste is dumped on to open land which creates a lot of environmental problems We get recycle aggregate from the old dumped structures and buildings. The main objective of this study was utilization of marble, granite and recycled aggregate waste with polypropylene fiber as a replacement for conventional natural coarse aggregates in concrete. Experimental investigations were carried out to examine the feasibility of use of marble, granite and recycled aggregates waste as coarse aggregates in concrete. Conventional natural coarse aggregates was fully replacement by marble in different percentages 0-60% , granite 0-30% and recycle aggregates 0-40% with polypropylene fiber less than 1% by weight. The concrete formulations were prepared with a constant water.
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Sonwane, Rajiv, Pushpendra Kumar Kushwaha, and Jiji M. Thomas. "Study of the Compressive Strength of Concrete Using Marble, Granite and Recycled Aggregates with Polypropylene Fiber." SMART MOVES JOURNAL IJOSCIENCE 5, no. 12 (2019): 7–11. http://dx.doi.org/10.24113/ijoscience.v5i6.242.

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Marble Industry produces large amount of waste during mining and processing stages. This waste is dumped on to open land which creates a lot of environmental problems We get recycle aggregate from the old dumped structures and buildings. The main objective of this study was utilization of marble, granite and recycled aggregate waste with polypropylene fiber as a replacement for conventional natural coarse aggregates in concrete. Experimental investigations were carried out to examine the feasibility of use of marble, granite and recycled aggregates waste as coarse aggregates in concrete. Conventional natural coarse aggregates was fully replacement by marble in different percentages 0-60% , granite 0-30% and recycle aggregates 0-40% with polypropylene fiber less than 1% by weight. The concrete formulations were prepared with a constant water.
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Sonwane, Rajiv, Pushpendra Kumar Kushwaha, and Jiji M. Thomas. "Study of the Compressive Strength of Concrete Using Marble, Granite and Recycled Aggregates with Polypropylene Fiber." SMART MOVES JOURNAL IJOSCIENCE 5, no. 12 (2019): 7–11. http://dx.doi.org/10.24113/ijoscience.v5i12.242.

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Marble Industry produces large amount of waste during mining and processing stages. This waste is dumped on to open land which creates a lot of environmental problems We get recycle aggregate from the old dumped structures and buildings. The main objective of this study was utilization of marble, granite and recycled aggregate waste with polypropylene fiber as a replacement for conventional natural coarse aggregates in concrete. Experimental investigations were carried out to examine the feasibility of use of marble, granite and recycled aggregates waste as coarse aggregates in concrete. Conventional natural coarse aggregates was fully replacement by marble in different percentages 0-60% , granite 0-30% and recycle aggregates 0-40% with polypropylene fiber less than 1% by weight. The concrete formulations were prepared with a constant water.
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True, G., D. Searle, L. Sear, and J. Khatib. "Voidage assessment of concrete using digital image processing." Magazine of Concrete Research 62, no. 12 (2010): 857–68. http://dx.doi.org/10.1680/macr.2010.62.12.857.

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Tsushima, Satoshi, and Norio Fukiage. "Flaw Detection in Concrete by Ultrasonic Image Processing." Concrete Research and Technology 1, no. 2 (1990): 51–58. http://dx.doi.org/10.3151/crt1990.1.2_51.

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Dissertations / Theses on the topic "Granite Concrete Image processing"

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Chen, Sha. "Digital image processing-based numerical methods for mechanics of heterogeneous geomaterials." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B36357765.

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Chen, Sha, and 陳沙. "Digital image processing-based numerical methods for mechanics of heterogeneous geomaterials." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B36357765.

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Mora, Carlos F. "Particle size and shape analysis of coarse aggregate using digital image processing." Thesis, Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22030153.

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Wei, Xiangmin. "GPR data processing for reinforced concrete bridge decks." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53066.

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In this thesis, several aspects of GPR data processing for RC bridge decks are studied. First, autofocusing techniques are proposed to replace the previous expensive and unreliable human visual inspections during the iterative migration process for the estimation of the velocity/dielectric permittivity distribution from GPR data. Second, F-K filtering with dip relaxation is proposed for interference removal that is important for both imaging and the performance of post-processing techniques including autofocusing techniques and CS-based migration studied in this thesis. The targeted interferes here are direct waves and cross rebar reflections. The introduced dip relaxation is for accommodating surface roughness and medium inhomogeneity. Third, the newly developed CS-based migration is modified and evaluated on GPR data from RC bridge decks. A more accurate model by accounting for impulse waveform distortion that leads to less modeling errors is proposed. The impact of the selection of the regularization parameter on the comparative amplitude reservation and the imaging performance is also investigated, and an approach to preserve the comparative amplitude information while still maintaining a clear image is proposed. Moreover, the potential of initially sampling the time-spatial data with uniform sampling rates lower than that required by traditional migration methods is evaluated.
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Ozerkan, Nesibe Gozde. "Evaluation Of Air Void Parameters Of Fly Ash Incorporated Self Consolidating Concrete By Image Processing." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611195/index.pdf.

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Self consolidating concrete (SCC) is defined as an innovative concrete that does not require vibration for placing and compaction and it is able to flow under its own weight, completely filling formwork and achieving full compaction. Although significant amount of research has been carried out regarding the fresh properties, mix design, placing methods and strength of various SCC mixes, only a very limited amount of work has been done to assess the durability performance of SCC. Concretes in cold climates are subjected to freeze-thaw cycles which are one of the major durability problems, and if the concrete is in a saturated or nearly saturated condition, those cycles lead to expansion of the water in the capillary pores of concrete causing great internal stresses. For a durable concrete subjected to freeze-thaw cycles, an adequate air void system is obtained by using air-entraining admixtures. The performance of the air void system is characterized by air void parameters that are determined using microscopical examination of the concrete microstructure. In this thesis a software tool, based on image analysis of concrete surface, is developed to evaluate the air void parameters of concrete using both American and European standards. Later on, an experimental program is conducted to evaluate the effect of freezing-thawing on self consolidating concrete that contain different percentages of fly ash (FA) and air entraining agents. For this purpose, a total of ten self consolidating concrete mixtures that contain four different contents of fly ash, and three different levels of air entrainment were prepared. During the casting operation, the workability properties of SCCs were observed through slump flow time and diameter, air content, V-funnel flow time, L-box height ratio, and segregation ratio. Hardened properties were evaluated by compressive strength, permeability tests (water absorption, sorptivity and rapid chloride permeability test), freezing-thawing test, resonant frequency test, ultrasonic pulse velocity test. The developed tool was used to characterize and evaluate the effects of air void parameters of SCC on its resistance to freeze-thaw cycles. At the end of this experimental investigation, it was concluded that the addition of air entraining agent increased the flowability and an increase in the fly ash content decreased the effect of air entraining agent. On the other hand, during image processing, it was observed that the surface preparation procedures have a crucial effect on processing quality. Moreover, spacing factor -which is the most important air void characteristic that is utilized for determination of the resistance to freezing-thawing- should not be restricted to 0.2 mm for SCC, since SCCs with spacing factors smaller than 0.4 mm could still exhibit good freezethaw resistance.
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Onifade, Ibrahim. "Internal structure characterization of asphalt concrete using x-ray computed tomography." Thesis, KTH, Väg- och banteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-127689.

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This study is carried out to develop the workflow from image acquisition to numerical simulation for asphalt concrete (AC) microstructure. High resolution computed tomography (CT) images are acquired and the image quality is improved using digital image processing (DIP). Non-uniform illumination which results in inaccurate phase segmentation is corrected by applying an illumination profile to correct the background and flat-fields in the image. Distance map based watershed segmentation is used to accurately segment the phases and separate the aggregates. Quantitative analysis of the microstructure is used to determine the phase volumetric relationship and aggregates characteristics. The results of the phase reconstruction and internal structure quantification using this procedure shows a very high level of reliability. Numerical simulations are carried out in Two dimensions (2D) and Three dimensions (3D) on the processed AC microstructure. Finite element analysis (FEM) is used to capture the strength and deformation mechanisms of the AC microstructure. The micromechanical behaviour of the AC is investigated when it is considered as a continuum and when considered as a multi-phase model. The results show that the size and arrangement of aggregates determines the stress distribution pattern in the mix.
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Vosahlik, Jan. "Air void clustering in concrete." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18206.

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Master of Science<br>Department of Civil Engineering<br>Kyle A. Riding<br>Air void clustering around coarse aggregate in concrete has been identified as a potential source of low strengths in concrete mixes by several Departments of Transportation around the country. Research was carried out to (1) develop a quantitative measure of air void clustering around aggregates, (2) investigate whether air void clustering can be reproduced in a laboratory environment, (3) determine if air void clustering can blamed for lower compressive strengths in concrete mixes, (4) and identify potential factors that may cause clustering. Five types of coarse aggregate and five different air entraining agents were included in the laboratory study to see if aggregate type or chemical composition of air entraining agent directly relates to air void clustering. A total of 65 mixes were made, implementing the frequently used technique of retempering that has been previously associated with air void clustering around aggregates. Compressive strength specimens as well as samples for hardened void analysis were made. Compressive strength at 7 and 28 days was determined and the automated hardened void analysis (including a new method of clustering evaluation) was performed on all samples. It was found that it is possible to reproduce air void clustering in laboratory conditions. However, the results have shown that retempering does not always cause air void clustering. It was also observed that air void clustering is not responsible for a decrease in compressive strength of retempered concrete as neither aggregate type nor chemical composition of air entraining agent had a significant impact on severity of void clustering around coarse aggregate particles. It was also found that the total air content and an inhomogeneous microstructure and not air void clustering were responsible for lower strengths.
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Sánchez, Calderón Luis Alberto. "A system for crack pattern detection, characterization and diagnosis in concrete structures by means of image processing and machine learning techniques." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/461408.

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A system that attempts to find cracks in a RGB picture of a concrete beam, measure the cracks angles and widths; and classify crack patterns in 3 pathologies has been designed and implemented in the MATLAB programming language. The system is divided in three parts: Crack Detection, Crack Clustering and Crack Pattern Classification. The Crack Detection algorithm attempts to detect pixels depicting cracks in a region of interest (ROI) and measure the crack angles and widths. The input ROI is segmented several times: First with an artificial Neural Network (NN) that classifies image patches in "Crack" or "Not Crack", then with the Canny Edge detector and finally with the local Mean and Standard deviation of the intensities. Then all neighborhoods in the mask are passed through special modified line kernels called "orientation kernels" designed to detect cracks and measure their angles; in order to obtain the width measurement, a line of pixels perpendicular to the crack is extracted and with an approximation of the intensity gradient of that line the width is measured. This algorithm outputs a mask the same size as the input picture with the measured angles and widths. The Crack Clustering algorithm groups up all the crack image patches recognized from the Crack Detection to approximate clusters that match the quantity of cracks in the image. To achieve this a special distance metric has been designed to group up aligned crack image patches; then with an algorithm based on the connectivity among the crack patches the clusters are obtained. The Crack Pattern Classification takes the mask outputs from the Crack Detection step as input for a Neural Network (NN) designed to classify crack patterns in concrete beams in 3 classes: Flexion, Shear and Corrosion-Bond cracks. The width and angles masks are first transformed into a Feature matrix to reduce the degrees of freedom of the input for the NN. To achieve a desirable classification in cases when more than 1 pathology is present, every angle and width mask is separated in as many Features matrices as clusters found with the Clustering algorithm; then separately classified with the NN designed. Several photos depicting concrete surfaces are presented as examples to check the accuracy of the width and angle measurements from the Crack Detection step. Other photos showing concrete beams with crack patterns are used to check the classification prowess of the Crack Pattern Classification step. The most important conclusion of this work is the transference of empirical knowledge from rehabilitation of structures to a machine learning model in order to diagnose the damage on an element. This opens possibilities for new lines of research to make a larger system with wider utilities, more pathologies and elements to classify.<br>Se ha diseñado un sistema que a partir de una foto a color de una superficie de hormigón realiza las siguientes tareas: Detectar fisuras, medir su ángulo y ancho, clasificar los patrones de fisuración asociados a tres patologías del hormigón; el cual ha sido implementado en el lenguaje de programación MATLAB. El sistema se divide en tres partes: Detección y medición de fisuras; algoritmo de análisis de grupos de fisuras y clasificación de patrones de fisuración. El algoritmo de detección de fisuras detecta los pixeles en donde hay fisuras dentro de una región de interés y mide el ancho y ángulos de dichas fisuras. La región de interés es segmentada varias veces: Primero con una red neuronal artificial que clasifica teselas de la imagen en dos categorías "Fisura" y "No fisura"; después se hace otra segmentación con un filtro Canny de detección de bordes y finalmente se segmenta con la media y desviaciones intensidades en teselas de la imagen. Entonces todas las localidades de la máscara de imagen obtenida con las segmentaciones anteriores se las pasa por varios filtros de detección de líneas diseñados para detectar y medir las fisuras. Este algoritmo resulta en dos máscaras de imagen con los anchos y ángulos de todas las fisuras encontradas en la región de interés. El algoritmo de análisis de grupos de teselas reconocidas como fisuras se hace para intentar reconocer y contar cuantas fisuras aparecen en la región de interés. Para lograr esto se diseñó una función de distancia para que teselas de fisura alineadas se junten; después con un algoritmo basado en la conectividad entre estas teselas o vectores fisura se obtienen los grupos de fisura. La clasificación de patrones de fisuración toma las máscaras de imagen del paso de detección de fisuras y lo toma como dato de entrada para una red neuronal diseñada para clasificar patrones de fisuración en tres categorías seleccionadas: Flexión, Cortante y Corrosión-Adherencia. Las máscaras de imagen de ancho y ángulo se transforman en una matriz de características para reducir los grados de libertad del problema, estandarizar un tamaño para la entrada al modelo de red neuronal. Para lograr clasificaciones correctas cuando más de 1 patología está presente en las vigas, cada máscara de imagen de ángulos y anchos de fisura se divide en cuantos cuantos grupos de teselas de fisuras haya en la imagen, y para cada uno se obtienen una matriz de características. Entonces se clasifican separadamente dichas matrices con la red neuronal artificial diseñada. Varias fotos con superficies de hormigón se presentan como ejemplos para evaluar la precisión de las mediciones de ancho y ángulo del paso de detección de fisuras. Otras fotos mostrando patrones de fisuración en vigas de hormigón se muestran para revisar las capacidades de diagnóstico del paso de clasificación de patrones de fisuración. La conclusión más importante de este trabajo es la transferencia del conocimiento empírico de la rehabilitación de estructuras hacia un modelo de inteligencia artificial para diagnosticar el daño en un elemento de la estructura. Esto abre un campo grande de líneas de investigación hacia el diseño e implementación de sistemas automatizados con más utilidades, más patologías y elementos para clasificar.
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German, Stephanie Ann. "Automated damage assessment of reinforced concrete columns for post-earthquake evaluations." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47686.

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An automated method in damage state assessment of reinforced concrete columns for the purpose of establishing a rapid and quantitative post-earthquake safety and structural evaluation procedure is proposed. Several techniques from the fields of computer vision and image processing are employed in order to develop a set of methods capable of automatically detecting spalled regions on the surface of reinforced concrete columns as well as the properties of cracks and spalled regions on these surfaces. The resulting properties of the observed visible damage on the reinforced concrete column surfaces are then utilized to automatically estimate the existing condition and safety of the column. The damage state is quantified according to the maximum drift capacity of the column. The methods proposed in this research were implemented in a Microsoft Visual Studio .NET environment, and tested on real images of damaged columns. The test results indicated that the methods could automatically detect spalled regions and retrieve the properties of spalling and cracks on reinforced concrete column surfaces in images or video frames, and further, that this retrieved information could be accurately translate to a meaningful assessment of the column's existing damage state in the form of the maximum drift capacity.
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Qsymah, Ansam. "In-situ X-ray computed tomography tests and numerical modelling of ultra high performance fibre reinforced concrete." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/insitu-xray-computed-tomography-tests-and-numerical-modelling-of-ultra-high-performance-fibre-reinforced-concrete(7c27ef36-afc8-4ea7-8c72-7d92eba924f9).html.

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Ultra high performance fibre reinforced concrete (UHPFRC) is a relatively new fibre reinforced cementitious composite and has become very popular in construction applications. Extensive experimental studies have been conducted, demonstrating its superior properties such as much higher strength, ductility and durability than conventional fibre reinforced concrete (FRC) and high performance concrete. However, the material's damage and fracture mechanisms at meso/micro scales are not well understood, limiting its wider applications considerably. This study aims at an in-depth understanding of the damage and fracture mechanisms of UHPFRC, combining microscale in-situ X-ray computed tomography (µXCT) experiments and mesoscale image-based numerical modelling. Firstly, in-situ µXCT tests of small-sized UHPFRC specimens under wedge splitting loading were carried out, probably for the first time in the world, using an in-house designed loading rig. With a voxel resolution of 16.9µm, the complicated fracture mechanisms are clearly visualised and characterised using both 2D images and 3D volumes at progressive loading stages, such as initiating of micro-cracks, arresting of cracks by fibres, bending and pulling out of fibres and spalling of mortar at the exit points of inclined fibres. Secondly, based on the statistics of pores in the µXCT images obtained for a 20mm cube specimen, an efficient two-scale analytical-numerical homogenisation method was developed to predict the effective elastic properties of the UHPFRC. The large number of small pores were first homogenised at microscale with sand and cement paste, using elastic moduli from micro-indentation tests. 3D mesoscale finite element models were built at the second scale by direct conversion of the µXCT images, with fibres and large pores were faithfully represented. The effects of the volume fraction and the orientation of steel fibres on the elastic modulus were investigated, indicating that this method can be used to optimise the material micro-structure. Thirdly, 3D mesoscale finite element models were built for the specimen used in the in-situ µXCT wedge splitting test, with embedded fibre elements directly converted from the µXCT images. The fracture behaviour in the mortar was simulated by the damage plasticity model available in ABAQUS. Finally, 2D mesoscale finite element models were developed to simulate the fracture behaviour of UHPFRC using cohesive interface elements to simulate cracks in the mortar, and randomly distributed two-noded 1D fibres and connector elements to simulate the pull-out behaviour of fibres. This approach offers a link between the fibres pull-out behaviour and the response of the whole composite at the macroscale, thus it can be used to conduct parametric studies to optimise the material properties.
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Books on the topic "Granite Concrete Image processing"

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Bandy, G. E. Image processing algorithms for surface crack detection and characterisation in concrete. UMIST, 1996.

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ZnO bao mo zhi bei ji qi guang, dian xing neng yan jiu. Shanghai da xue chu ban she, 2010.

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Book chapters on the topic "Granite Concrete Image processing"

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Lemes, J. V. B., G. E. S. Lima, F. G. D. Machado, et al. "Reactive Powder Concrete Production with the Addition of Granite Processing Waste." In Characterization of Minerals, Metals, and Materials 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51382-9_80.

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Cheon, Mi-Hyeon, Dong-Gyun Hong, and Donghwa Lee. "Surface Crack Detection in Concrete Structures Using Image Processing." In Robot Intelligence Technology and Applications 5. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78452-6_45.

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Kapadia, Harsh, Ripal Patel, Yash Shah, J. B. Patel, and P. V. Patel. "An Improved Image Pre-processing Method for Concrete Crack Detection." In Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00665-5_149.

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Aryanfar, Asghar, and Irem Şanal. "Image Processing for Workability of Concrete with Super Absorbent Polymer." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66501-2_54.

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Reghukumar, Arathi, and L. Jani Anbarasi. "Crack Detection in Concrete Structures Using Image Processing and Deep Learning." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9019-1_19.

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Rudzki, Marcin, Monika Bugdol, and Tomasz Ponikiewski. "An Image Processing Approach to Determination of Steel Fibers Orientation in Reinforced Concrete." In Information Technologies in Biomedicine. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31196-3_15.

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Li, J., and X. Wu. "Application of Digit Image Processing Technique to the Analysis of Concrete Damage Process." In Computational Mechanics. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75999-7_84.

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Jagadeesh, Ajayshankar, Ghim Ping Ong, and Yu-Min Su. "Effect of Global Thresholding Algorithms on Pervious Concrete Pore Network Properties Using XRCT-Based Digital Image Processing." In Lecture Notes in Civil Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48679-2_54.

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Heinemann, H., H. Zijlstra, R. van Hees, and T. Nijland. "From concrete repair to concrete conservation." In Computational Vision and Medical Image Processing. CRC Press, 2011. http://dx.doi.org/10.1201/b11570-11.

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Raijiwala, D., and H. Patil. "Geopolymer concrete." In Computational Vision and Medical Image Processing. CRC Press, 2011. http://dx.doi.org/10.1201/b11570-41.

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Conference papers on the topic "Granite Concrete Image processing"

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CARLOS, BARONETTO, BEGLIARDO HUGO, FORNARI JAVIER, OSHIRO ANGEL, and POSITIERI MARIA. "Concrete Characterization through digital image processing techniques." In Sixth International Conference on Advances in Civil Structural and Mechanical Engineering CSM 2018. Institute of Research Engineers and Doctors, 2018. http://dx.doi.org/10.15224/978-1-63248-150-4-32.

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Gorsky, Mykhaylo P., and Peter P. Maksimyak. "Application of speckle-field images processing for concrete hardening diagnostics." In Applications of Digital Image Processing XL, edited by Andrew G. Tescher. SPIE, 2017. http://dx.doi.org/10.1117/12.2273407.

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Zhao, Liang, Changhua Li, Dengfeng Chen, and Faning Dang. "Concrete CT Image Segmentation Using Modified Metropolis Dynamics." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5302060.

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Santos, Bruno O., Jonatas Valença, and Eduardo Júlio. "Detection of cracks on concrete surfaces by hyperspectral image processing." In SPIE Optical Metrology, edited by Jürgen Beyerer and Fernando Puente León. SPIE, 2017. http://dx.doi.org/10.1117/12.2269606.

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Zhang, Guangmin, and Yue Song. "Time reversal imaging method for damage detection in concrete." In 2016 IEEE International Conference on Signal and Image Processing (ICSIP). IEEE, 2016. http://dx.doi.org/10.1109/siprocess.2016.7888264.

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Mohammad, Fakher, Alaa AlZoubi, Hongbo Du, and Sabah Jassim. "A generic approach for automatic crack recognition in buildings glass facade and concrete structures." In Thirteenth International Conference on Digital Image Processing, edited by Xudong Jiang and Hiroshi Fujita. SPIE, 2021. http://dx.doi.org/10.1117/12.2601061.

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Qi, Shengxiang, Hui Xu, and Jian Ren. "Microwave Imaging of Reinforced Concrete and Design of a Broadband Antenna." In ICVIP 2017: International Conference on Video and Image Processing. ACM, 2017. http://dx.doi.org/10.1145/3177404.3177424.

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Xuhang Tong, Jie Guo, Yun Ling, and Zhouping Yin. "A new image-based method for concrete bridge bottom crack detection." In 2011 International Conference on Image Analysis and Signal Processing (IASP). IEEE, 2011. http://dx.doi.org/10.1109/iasp.2011.6109108.

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Yang, Jun. "Discussion on Section Construction Techniques for Continuous Bridge of Large-Span Concrete." In 2010 International Conference on Optoelectronics and Image Processing (ICOIP). IEEE, 2010. http://dx.doi.org/10.1109/icoip.2010.261.

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Zhang, Hong, Liqun Tang, Peng Ren, Jiansheng Guo, and Xiaochun Lao. "Internal steel strain measurement inside of reinforced concrete by FBGS." In Optical Technology and Image Processing fo rFluids and solids Diagnostics 2002, edited by Gong Xin Shen, Soyoung S. Cha, Fu-Pen Chiang, and Carolyn R. Mercer. SPIE, 2003. http://dx.doi.org/10.1117/12.510049.

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