5.3
Análise de Peneira e Curvas de Classificação
A análise de peneira é um método usado para determinar a distribuição do tamanho de partículas de materiais agregados. Este processo envolve as seguin…
A análise de agregados por peneira é realizada usando um conjunto de peneiras empilhadas em ordem decrescente de seu tamanho de abertura de cima para baixo.
Os tamanhos das peneiras variam para agregados grossos e agregados miúdos.
Uma amostra de agregados secos ao ar é pesada e peneirada através das peneiras, agitando-as mecanicamente ou manualmente por um tempo determinado.
No processo, cada peneira retém partículas mais grossas do que seu tamanho de malha, permitindo que partículas mais finas passem para a próxima peneira.
O peso das fracções retidas em cada peneira é registado, expresso em percentagem da massa total e representado num quadro.
Começando pela peneira mais fina e subindo, a porcentagem cumulativa que passa por cada peneira é calculada e arredondada para o um por cento mais próximo.
Além disso, um gráfico semi-logarítmico é usado para representar a porcentagem de passagem no eixo y e as aberturas da peneira no eixo x em escala logarítmica.
Neste gráfico, a porcentagem de passagem acumulada correspondente a cada tamanho de peneira é plotada para obter a curva de classificação, que indica a distribuição das partículas agregadas com base em seus tamanhos.
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Q1: How is sieve analysis performed on aggregate samples?
Sieve analysis begins with weighing an air-dried aggregate sample, then passing it through a stack of sieves arranged in descending order of aperture size from top to bottom. The sieves are shaken mechanically or manually for a specified time, allowing finer particles to pass through while coarser particles are retained. The weight of material retained on each sieve is recorded and expressed as a percentage of total mass.
Q2: What is the purpose of calculating cumulative percentage passing in sieve analysis?
Cumulative percentage passing is calculated from the finest sieve moving upward to establish the foundation for grading curve development. This calculation, rounded to the nearest one percent, represents the proportion of aggregate particles finer than each sieve size. These values are plotted on the y-axis of a semi-logarithmic graph to visualize particle size distribution.
Q3: Why is a semi-logarithmic graph used to represent grading curves?
A semi-logarithmic graph is used because sieve apertures are plotted on a logarithmically scaled x-axis while cumulative passing percentages are plotted linearly on the y-axis. This scaling approach effectively displays the wide range of aggregate particle sizes and produces a grading curve that clearly indicates the distribution of particles across different size ranges.
Q4: What information does a grading curve provide about aggregates?
A grading curve visually simplifies the distribution of particle sizes within an aggregate sample by plotting cumulative passing percentages against sieve apertures. This graphical representation is essential for ensuring that aggregates meet required grading specifications for construction projects and helps engineers assess whether the material is suitable for specific applications.
Q5: How do sieve sizes differ between coarse and fine aggregates?
Sieve sizes vary for coarse aggregates and fine aggregates to accommodate their different particle size ranges. Coarse aggregates require larger mesh openings, while fine aggregates use smaller mesh sizes. The specific sieve stack selected depends on the aggregate classification and the particle size distribution being analyzed.
Q6: What does the weight retained on each sieve represent in sieve analysis?
The weight retained on each sieve represents the mass of particles coarser than that sieve's mesh size. This weight is recorded after sieving and then expressed as a percentage of the total sample mass. These percentages are essential for calculating cumulative passing values and constructing the grading curve.
Q7: Why must aggregate samples be air-dried before sieve analysis?
Air-drying the aggregate sample ensures consistent and accurate results by removing surface moisture that could affect particle behavior during sieving. Moisture can cause particles to clump together or stick to sieves, leading to incorrect weight measurements and inaccurate particle size distribution data.