983 resultados para mixture models


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For years, specifications have focused on the water to cement ratio (w/cm) and strength of concrete, despite the majority of the volume of a concrete mixture consisting of aggregate. An aggregate distribution of roughly 60% coarse aggregate and 40% fine aggregate, regardless of gradation and availability of aggregates, has been used as the norm for a concrete pavement mixture. Efforts to reduce the costs and improve sustainability of concrete mixtures have pushed owners to pay closer attention to mixtures with a well-graded aggregate particle distribution. In general, workability has many different variables that are independent of gradation, such as paste volume and viscosity, aggregate’s shape, and texture. A better understanding of how the properties of aggregates affect the workability of concrete is needed. The effects of aggregate characteristics on concrete properties, such as ability to be vibrated, strength, and resistivity, were investigated using mixtures in which the paste content and the w/cm were held constant. The results showed the different aggregate proportions, the maximum nominal aggregate sizes, and combinations of different aggregates all had an impact on the performance in the strength, slump, and box test.

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Concrete will suffer frost damage when saturated and subjected to freezing temperatures. Frost-durable concrete can be produced if a specialized surfactant, also known as an air-entraining admixture (AEA), is added during mixing to stabilize microscopic air voids. Small and well-dispersed air voids are critical to produce frost-resistant concrete. Work completed by Klieger in 1952 found the minimum volume of air required to consistently ensure frost durability in a concrete mixture subjected to rapid freezing and thawing cycles. He suggested that frost durability was provided if 18 percent air was created in the paste. This is the basis of current practice despite the tests being conducted on materials that are no longer available using tests that are different from those in use today. Based on the data presented, it was found that a minimum air content of 3.5 percent in the concrete and 11.0 percent in the paste should yield concrete durable in the ASTM C 666 with modern AEAs and low or no lignosulfonate water reducers (WRs). Limited data suggests that mixtures with a higher dosage of lignosulfonate will need about 1 percent more air in the concrete or 3 percent more air in the paste for the materials and procedures used. A spacing factor of 0.008 in. was still found to be necessary to provide frost durability for the mixtures investigated.

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Any transportation infrastructure system is inherently concerned with durability and performance issues. The proportioning and uniformity control of concrete mixtures are critical factors that directly affect the longevity and performance of the portland cement concrete pavement systems. At present, the only means available to monitor mix proportions of any given batch are to track batch tickets created at the batch plant. However, this does not take into account potential errors in loading materials into storage silos, calibration errors, and addition of water after dispatch. Therefore, there is a need for a rapid, cost-effective, and reliable field test that estimates the proportions of as-delivered concrete mixtures. In addition, performance based specifications will be more easily implemented if there is a way to readily demonstrate whether any given batch is similar to the proportions already accepted based on laboratory performance testing. The goal of the present research project is to investigate the potential use of a portable x-ray fluorescence (XRF) technique to assess the proportions of concrete mixtures as they are delivered. Tests were conducted on the raw materials, paste and mortar samples using a portable XRF device. There is a reasonable correlation between the actual and calculated mix proportions of the paste samples, but data on mortar samples was less reliable.

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Aim: Climatic niche modelling of species and community distributions implicitly assumes strong and constant climatic determinism across geographic space. This assumption had however never been tested so far. We tested it by assessing how stacked-species distribution models (S-SDMs) perform for predicting plant species assemblages along elevation. Location: Western Swiss Alps. Methods: Using robust presence-absence data, we first assessed the ability of topo-climatic S-SDMs to predict plant assemblages in a study area encompassing a 2800 m wide elevation gradient. We then assessed the relationships among several evaluation metrics and trait-based tests of community assembly rules. Results: The standard errors of individual SDMs decreased significantly towards higher elevations. Overall, the S-SDM overpredicted far more than they underpredicted richness and could not reproduce the humpback curve along elevation. Overprediction was greater at low and mid-range elevations in absolute values but greater at high elevations when standardised by the actual richness. Looking at species composition, the evaluation metrics accounting for both the presence and absence of species (overall prediction success and kappa) or focusing on correctly predicted absences (specificity) increased with increasing elevation, while the metrics focusing on correctly predicted presences (Jaccard index and sensitivity) decreased. The best overall evaluation - as driven by specificity - occurred at high elevation where species assemblages were shown to be under significant environmental filtering of small plants. In contrast, the decreased overall accuracy in the lowlands was associated with functional patterns representing any type of assembly rule (environmental filtering, limiting similarity or null assembly). Main Conclusions: Our study reveals interesting patterns of change in S-SDM errors with changes in assembly rules along elevation. Yet, significant levels of assemblage prediction errors occurred throughout the gradient, calling for further improvement of SDMs, e.g., by adding key environmental filters that act at fine scales and developing approaches to account for variations in the influence of predictors along environmental gradients.

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This guide provides a variety of tools that can help an educator, building staff or school district decide how to include environmental education in their curriculum.

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Predictive groundwater modeling requires accurate information about aquifer characteristics. Geophysical imaging is a powerful tool for delineating aquifer properties at an appropriate scale and resolution, but it suffers from problems of ambiguity. One way to overcome such limitations is to adopt a simultaneous multitechnique inversion strategy. We have developed a methodology for aquifer characterization based on structural joint inversion of multiple geophysical data sets followed by clustering to form zones and subsequent inversion for zonal parameters. Joint inversions based on cross-gradient structural constraints require less restrictive assumptions than, say, applying predefined petro-physical relationships and generally yield superior results. This approach has, for the first time, been applied to three geophysical data types in three dimensions. A classification scheme using maximum likelihood estimation is used to determine the parameters of a Gaussian mixture model that defines zonal geometries from joint-inversion tomograms. The resulting zones are used to estimate representative geophysical parameters of each zone, which are then used for field-scale petrophysical analysis. A synthetic study demonstrated how joint inversion of seismic and radar traveltimes and electrical resistance tomography (ERT) data greatly reduces misclassification of zones (down from 21.3% to 3.7%) and improves the accuracy of retrieved zonal parameters (from 1.8% to 0.3%) compared to individual inversions. We applied our scheme to a data set collected in northeastern Switzerland to delineate lithologic subunits within a gravel aquifer. The inversion models resolve three principal subhorizontal units along with some important 3D heterogeneity. Petro-physical analysis of the zonal parameters indicated approximately 30% variation in porosity within the gravel aquifer and an increasing fraction of finer sediments with depth.