Introdução ao estudo de durabilidade do betão auto-compactável

O âmbito desta dissertação centra-se na temática de estudos de durabilidade do betão auto-compactavel (BAC), cujo cálculo dos constituintes foi feito pelo método de Nepomuceno. Sobre amostras de 40, 55 e 70 MPa, produzidas segundo o método atrás identificado, foram feitos estudos químicos e morfológicos, de propriedades de transporte de mecanismos de degradação e de propriedades indirectas. Os três provetes em estudo de 40, 55 e 70 MPa, apresentam características satisfatórias a nível da microestrutura, propriedades de transporte, carbonatação, penetração de cloretos e análise de ultra-sons. Numa análise comparativa entre as três resistências mecânicas em estudo, verifica-se que as propriedades de durabilidade vão melhorando a medida que a resistência mecânica também aumenta, ou seja, os provete com 70 MPa apresentam as melhores características a nível de durabilidade e os de 40 as piores; os de 55 apresentam propriedades intermédias.

Análise comparativa de soluções adotadas para a reabilitação de pavimentos flexíveis que integram a rede rodoviária nacional

A presente dissertação tem como objetivo efetuar a análise comparativa de soluções adotadas de reabilitação de pavimentos flexíveis que integram a rede rodoviária nacional. No âmbito desta análise apresenta-se o estado da arte respeitante à reabilitação de pavimentos flexíveis, nomeadamente: mecanismos de degradação, famílias de degradações, avaliação da capacidade de carga dos pavimentos, metodologia utilizada no dimensionamento do reforço de pavimentos, sendo também efetuada uma análise comparativa de técnicas de reforço de pavimentos e dos tratamentos antifendas. Neste contexto, apresenta-se um caso de estudo no qual é efetuada uma análise de três soluções possíveis para a reabilitação estrutural do pavimento do IC 20 entre Almada e a Costa de Caparica. É feita a descrição da solução projetada pela EP, SA patenteada em concurso público lançado em 2007, a qual é de certa forma inovadora ao nível do tratamento retardador da reflexão de fendas. Aquela solução técnica é constituída pela aplicação de grelhas de fibra de vidro e grelhas de fibra de carbono, seguidas da colocação de uma camada de desgaste em mistura betuminosa rugosa com betume modificado com baixa percentagem de borracha reciclada de pneus usados (BBr - BBB). Complementarmente, é efetuada a análise da solução do projeto de reabilitação do IC 20, patenteado pela Subconcessionária do Baixo Tejo, que contemplou a aplicação de misturas betuminosas rugosas com betume modificado com média percentagem de borracha reciclada de pneus usados (BBr - BBM). Para além da solução patenteada pela subconcessionária, é analisada a solução do projeto de alterações (variante) apresentado pelo agrupamento de empresas construtoras, que foi adotado na execução da obra realizada no IC 20. A intervenção de reabilitação estrutural contemplou a utilização de uma camada de ligação em AC 16 10/20 (MBAM) e uma camada de desgaste em mistura betuminosa rugosa com betume modificado com média percentagem de borracha reciclada de pneus usados (BBr - BBM). Adicionalmente à caracterização de diferentes soluções de reabilitação de pavimentos flexíveis adotados em Portugal, é efetuada uma análise comparativa dos custos de ciclo de vida (construção, manutenção e conservação) de cada tipo de solução de reabilitação.

Durability performance of self-compacting concrete (SCC) with binary and ternary mixes of fly ash and limestone filler

The basic objective of this work is to evaluate the durability of self-compacting concrete (SCC) produced in binary and ternary mixes using fly ash (FA) and limestone filler (LF) as partial replacement of cement. The main characteristics that set SCC apart from conventional concrete (fundamentally its fresh state behaviour) essentially depend on the greater or lesser content of various constituents, namely: greater mortar volume (more ultrafine material in the form of cement and mineral additions); proper control of the maximum size of the coarse aggregate; use of admixtures such as superplasticizers. Significant amounts of mineral additions are thus incorporated to partially replace cement, in order to improve the workability of the concrete. These mineral additions necessarily affect the concrete’s microstructure and its durability. Therefore, notwithstanding the many well-documented and acknowledged advantages of SCC, a better understanding its behaviour is still required, in particular when its composition includes significant amounts of mineral additions. An ambitious working plan was devised: first, the SCC’s microstructure was studied and characterized and afterwards the main transport and degradation mechanisms of the SCC produced were studied and characterized by means of SEM image analysis, chloride migration, electrical resistivity, and carbonation tests. It was then possible to draw conclusions about the SCC’s durability. The properties studied are strongly affected by the type and content of the additions. Also, the use of ternary mixes proved to be extremely favourable, confirming the expected beneficial effect of the synergy between LF and FA. © 2015 RILEM.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

On independent component analysis applied to unmixing hyperspectral data

One of the most challenging task underlying many hyperspectral imagery applications is the spectral unmixing, which decomposes a mixed pixel into a collection of reectance spectra, called endmember signatures, and their corresponding fractional abundances. Independent Component Analysis (ICA) have recently been proposed as a tool to unmix hyperspectral data. The basic goal of ICA is to nd a linear transformation to recover independent sources (abundance fractions) given only sensor observations that are unknown linear mixtures of the unobserved independent sources. In hyperspectral imagery the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be independent. This paper address hyperspectral data source dependence and its impact on ICA performance. The study consider simulated and real data. In simulated scenarios hyperspectral observations are described by a generative model that takes into account the degradation mechanisms normally found in hyperspectral applications. We conclude that ICA does not unmix correctly all sources. This conclusion is based on the a study of the mutual information. Nevertheless, some sources might be well separated mainly if the number of sources is large and the signal-to-noise ratio (SNR) is high.

Durability performance of self-compacting concrete (SCC) with binary and ternary mixes of fly ash and limestone filler

The basic objective of this work is to evaluate the durability of self-compacting concrete (SCC) produced in binary and ternary mixes using fly ash (FA) and limestone filler (LF) as partial replacement of cement. The main characteristics that set SCC apart from conventional concrete (fundamentally its fresh state behaviour) essentially depend on the greater or lesser content of various constituents, namely: greater mortar volume (more ultrafine material in the form of cement and mineral additions); proper control of the maximum size of the coarse aggregate; use of admixtures such as superplasticizers. Significant amounts of mineral additions are thus incorporated to partially replace cement, in order to improve the workability of the concrete. These mineral additions necessarily affect the concrete's microstructure and its durability. Therefore, notwithstanding the many well-documented and acknowledged advantages of SCC, a better understanding its behaviour is still required, in particular when its composition includes significant amounts of mineral additions. An ambitious working plan was devised: first, the SCC's microstructure was studied and characterized and afterwards the main transport and degradation mechanisms of the SCC produced were studied and characterized by means of SEM image analysis, chloride migration, electrical resistivity, and carbonation tests. It was then possible to draw conclusions about the SCC's durability. The properties studied are strongly affected by the type and content of the additions. Also, the use of ternary mixes proved to be extremely favourable, confirming the expected beneficial effect of the synergy between LF and FA.

Cell-specific regulation of acetylcholinesterase expression under inflammatory conditions

Acetylcholine (ACh) has been shown to exert an anti-inflammatory function by down-modulating the expression of pro-inflammatory cytokines. Its availability can be regulated at different levels, namely at its synthesis and degradation steps. Accordingly, the expression of acetylcholinesterase (AChE), the enzyme responsible for ACh hydrolysis, has been observed to be modulated in inflammation. To further address the mechanisms underlying this effect, we aimed here at characterizing AChE expression in distinct cellular types pivotal to the inflammatory response. This study was performed in the human acute leukaemia monocytyc cell line, THP-1, in human monocyte-derived primary macrophages and in human umbilical cord vein endothelial cells (HUVEC). In order to subject these cells to inflammatory conditions, THP-1 and macrophage were treated with lipopolysaccharide (LPS) from E.coli and HUVEC were stimulated with the tumour necrosis factor α (TNF-α). Our results showed that although AChE expression was generally up-regulated at the mRNA level under inflammatory conditions, distinct AChE protein expression profiles were aurprisingly observed among the distinct cellular types studied. Altogether, these results argue for the existence of cell specific mechanisms that regulate the expression of acetylcholinesterase in inflammation.