O efeito das altas temperaturas na resistência à compressão de um betão com adição de fibras de aço e têxteis recicladas de pneu

O betão é um dos materiais responsáveis pelo aumento do consumo de cimento e agregados naturais na construção civil, levantando questões de sustentabilidade dos recursos naturais. Este facto conduz à necessidade de desenvolver tecnologias inovadoras e materiais alternativos para melhorar não só o nível de desempenho do betão mas, acima de tudo, apoiar a política de proteção ambiental. O objetivo deste trabalho é demonstrar, através de investigação experimental, que a adição de fibras de aço e têxteis provenientes da reciclagem de pneus é viável para produzir um betão com um comportamento satisfatório à temperatura ambiente e quando submetido a elevadas temperaturas.info:eu-repo/semantics/publishedVersio

Betão com adição de fibras de aço e têxteis reciclados de pneu sujeito a altas temperaturas

O betão é um dos materiais responsáveis pelo aumento do consumo de cimento e agregados naturais na construção civil, levantando questões de sustentabilidade dos recursos naturais. Este facto conduz à necessidade de desenvolver tecnologias inovadoras e materiais alternativos para melhorar não só o nível de desempenho do betão mas, acima de tudo, apoiar a política de proteção ambiental. O objetivo deste trabalho é demonstrar, através de investigação experimental, que a adição de fibras têxteis e de fibras de aço provenientes da reciclagem de pneus é viável para produzir um betão com um comportamento satisfatório quando submetido a elevadas temperaturas.info:eu-repo/semantics/publishedVersio

LEAST SQUARES MATCHING FOR COMPARISON OF DIGITAL TERRAIN MODELS AND ITS APPLICATION POTENTIAL FOR THE BRAZILIAN MODELS AND THE SRTM MODEL

Digital Terrain Models are being used for planning and hydrological applications, but also for visualization and many other tasks. For all applications, it is necessary to know the model quality, because it has an impact on the quality of the decisions that are drawn from the terrain model applications. In this paper we present a method that is suitable for comparing two terrain models to each other. Vertical, but also horizontal displacement of terrain features can be found automatically, which are systematic errors and are in the main focus of this paper. However, random errors can be quantified, too. This method allows establishing a vector field of differences between two models, measuring the deviation from one to the other. These deviations are a measure of quality of one model against the other. Emphasis will be put on comparing terrain model from NASAs Shuttle Radar Topographic Mission to terrain models of known quality in Brazil

A JOINT EFFORT OF SPEEDED-UP ROBUST FEATURES ALGORITHM AND A DISPARITY-BASED MODEL FOR 3D INDOOR MAPPING USING RGB-D DATA

In this paper, we present a method for 3D mapping of indoor environments using RGB-D data. The contribution of our proposed method is two-fold. First, our method exploits a joint effort of the speed-up robust features (SURF) algorithm and a disparity-to-plane model for a coarse-to-fine registration procedure. Once the coarse-to-fine registration task accumulates errors, the same features can appear in two different locations of the map. This is known as the loop closure problem. Then, the variance-covariance matrix that describes the uncertainty of transformation parameters (3D rotation and 3D translation) for view-based loop closure detection followed by a graph-based optimization are proposed to achieve a 3D consistent indoor map. To demonstrate and evaluate the effectiveness of the proposed method, experimental datasets obtained in three indoor environments with different levels of details are used. The experimental results shown that the proposed framework can create 3D indoor maps with an error of 11,97 cm into object space that corresponds to a positional imprecision around 1,5% at the distance of 9 m travelled by sensor

AUTOMATIC DETECTION OF PLANTED TREES AND THEIR HEIGHTS USING PHOTOGRAMMETRIC RPA POINT CLOUDS

This work aims to analyze the potential of the Photogrammetric Point Cloud (PPC) obtained from Remote Piloted Aircraft (RPA) optical images for detecting and obtaining tree heights in a loblolly pine plantation using a global maximum filter. The enhanced algorithm used in this study is then named STD (Single Tree Detection). Field surveys were conducted to count all the trees in the field (Forest Census) and measure the trees’ height with a vertex hypsometer. The results were faced to PCC outcomes. The detection rate (r) was equal to the precision rate (p), indicating that the algorithm reaches a high tree detection performance. In summary, the STD algorithm segmented 2,192 trees, representing 89% of trees recorded in the forest census. The retrieved tree height reached, on average, a height of 17.05 m, whereas slightly higher by the traditional forest inventory (17.42 m). The root-mean-square error (RMSE) and Bias were 47 cm (2.8%) and -37 cm (-2.2%), respectively. The Dunnett test showed that the tree height did not significantly differ between the results obtained by traditional forest inventory from those generated by the STD. It confirms the potential use of PPC for forest inventory procedures

Development of a Low-Cost Terrestrial Mobile Mapping System for Urban Vegetation Detection Using Convolutional Neural Networks

Urbanization brought a lot of pollution-related issues that are mitigable by the presence of urban vegetation. Therefore, it is necessary to map vegetation in urban areas, to assist the planning and implementation of public policies. As a technology presented in the last decades, the so-called Terrestrial Mobile Mapping Systems - TMMS, are capable of providing cost and time effective data acquisition, they are composed primarily by a Navigation System and an Imaging System, both mounted on a rigid platform, attachable to the top ofa ground vehicle. In this context, it is proposed the creation of a low-cost TMMS, which has the feature of imaging in the near-infrared (NIR) where the vegetation is highly discriminable. After the image acquisition step, it becomes necessary for the semantic segmentation of vegetation and non-vegetation. The current state of the art algorithms in semantic segmentation scope are the Convolutional Neural Networks - CNNs. In this study, CNNs were trained and tested, reaching a mean value of 83% for the Intersection Over Union (IoU) indicator. From the results obtained, which demonstrated good performance for the trained neural network, it is possible to concludethat the developed TMMS is suitable to capture data regarding urban vegetation

Foreword to the special section on recent advances in graphics and interaction

This special section on Recent Advances in Graphics and In- teraction features the papers submitted to Computers & Graphics (C&G) and presented at the 2021 edition of the International Conference on Graphics and Interaction – ICGI’2021 – which was held on November 4 and 5, 2021 at the Faculty of Engineering of the University of Porto, Portugal, as a joint organization with the Eurographics Portuguese Chapter — GPCG.FCT -Fundação para a Ciência e a Tecnologia(undefined

Automation of spatial resection of images with the use of highway hypothesis as field support derived from Laser Scanning System

A automação da resseção espacial de imagens exige a implementação computacional de algoritmos de reconhecimento semântico dos objetos (características ou significados dos objetos) presentes na imagem. Este problema é demasiadamente complicado e tem agregado esforços de várias áreas do conhecimento. Para isto, foi implementada uma metodologia para a construção automática de hipóteses de objetos semânticos (objetos rodovias), que combina várias técnicas de PDI e tratamento radiométrico da imagem digital (baseado em cores), e valores de intensidade extraídas da imagem de intensidade. A proposta deste trabalho, é implementar uma ferramenta automática para a resseção espacial de imagens digitais, onde serão integradas técnicas de PDI, Inteligência Artificial e Visão Computacional, aplicada em imagens adquiridas por diferentes sensores (câmara digital e laser), bem como o desenvolvimento de novos métodos no que tange cada aspecto que envolve as etapas de uma resseção espacial, tais como, problema de correspondência baseada em atributos, árvore de busca e relações entre as feições envolvidas, reconhecimento semântico de objetos e estimação dos parâmetros de orientação exterior.The automatic space resection of image needs to development of semantic objects recognition. This problem is a background and uses another sciences. So, we implemented an approach to automatic reconstruction of semantic objects that combine many techniques of Digital Image Processing and radiometric vision of image, and intensity laser scanner image to calculate the exterior parameters of camera automatically