This sample paper on Sieve Analysis Of Fine Aggregate Lab Report Conclusion offers a framework of relevant facts based on recent research in the field. Read the introductory part, body, and conclusion of the paper below.
To conduct a vive analysis, samples are oven dried tort at least 24 hours. The soil is placed and shaken through a stack of sieves with openings of decreasing size from top to bottom. The mass of particles retained in each sieve is determined. Results showed that the particle-?size distribution curve of coarse aggregate is characterized by a steep curve.
This means the coarse aggregate is poorly or uniformly graded with small variation in size Particle-size distribution curve of fine aggregate is characterized by an S-curve. It is well graded and has a gradation of particle size that spans evenly the size from coarsest to finest.
Conclusions drawn from the interpretation of the particle-size distribution curve is supported by computed coefficients Of uniformity and gradation Which is 6.
79 and 1. 03 for (well graded) fine aggregates, and 1. 57 and 1. 05 for (poorly graded) coarse aggregate. Significance of the Experiment Particle size analysis is important because it determines the soil gradation, which is an indicator of other soil properties such as compressibility, shear strength, and hydraulic conductivity. A poorly graded soil will have better drainage because of more void spaces.
A well graded soil is able to be compacted more than a poorly graded soil. Standard Reference ASTM CLC 36-06 – Standard Test Method for Sieve Analysis of Fine and Coarse Aggregates’.
Pennsylvania, US: ASTM International, 2006. I. OBJECTIVES After performing this test, the students are expected to: 1. Determine the percentages of various size fraction on the basis of the total mass of the initial dry sample. 2. Determine effective grain size after plotting particle size distribution curve (percent finer versus particle diameter in millimeter). . Calculate coefficient of uniformity and curvature and classify aggregates into well graded or poorly graded aggregates based on given criteria or these two parameters. II. EXPERT MENTAL PROCEDURE A. Materials and Equipment Sieves No. 4, 8, 16, 30, 50, 100, and 200 for fine aggregates Sieves No. I-IM 3/4- IM, 1/2-in. , and 3/8-in, for coarse aggregates Balance accurate to C, I-g for fine and 0. 5 for coarse aggregates Oven Brush B. Methodology 1. Preparation of the Soil Sample. Minimum of 300-g fine and 5-keg coarse aggregates are obtained.
These samples are both oven dried for at least 24 hours. 2. Preparation of Equipment. All mass of sieves including the pan are determined. Then, sieves are nested in order Of decreasing size Of opening from top to bottom. . Sieving. The sample is placed in top sieve. The sieves are agitated by hand in a vertical and lateral motion. 4. For course aggregates, the sample is split into two or more batches, sieving each batch individually. The mass of the several batches retained on a specific sieve are combined before calculating the percentage of the sample on the sieve. . Sieving is continued for a sufficient period and in such manner that not more than 1 % by mass of the material retained on any individual sieve will pass that sieve during 1 minute of continuous hand sieving. 6. The mass of each size increment is determined on balance. The total mass of the material after sieving should check closely with original mass of sample placed on the sieves. NOTE: If the amounts differ by more than 0. 3 based on the original dry sample mass, the results should not be used tort acceptance purposes.
C. Data Analysis l. The mass of soil retained in each sieve is computed by getting the difference of mass of sieve with the retained soil, and the product of no of batches made and the mass of sieves. 2, The percent retained is computed by getting the ratio of mass of retained soil on each sieve, and the initial mass of the sample. 3. The recent finer is computed by getting the sum of mass of soil retained on smaller sieves, subtracting it from the total mass of sample, and dividing the sum by the total mass times 100. 4.
Percent finer is plotted on y-axis while the particle size diameter in logarithmic scale is plotted on x-axis. A curve connecting the points is drawn. Logarithmic scale is used to represent grain size information that typically spans many orders of magnitude. 5. Important parameters in computing coefficient Of curvature and uniformity such as effective grain size (DID), DID, and 060 are determined from the particle size distribution curve for fine and coarse aggregates. Ill. RESULTS OF EXPERIMENT Particle-Size Distribution Curve and Determination of 060, 030, and DISC Figure 1.
Particle-Size Distribution Curve (Fine Aggregates) Figure 2. Particle-Size Distribution Curve (Coarse Aggregates) lb. DISCUSSION Effective Grain Size (DID). It represents a grain diameter for which of the sample will be finer than it. It can be used to estimate the permeability. The effective grain sizes in fine and coarse aggregate in this test are C. 14 mm and 10. 4 mm respectively. These values are obtained from the particle size distribution curve shown in Figures I and 2. Coefficient of Gradation (C).
This parameter (also called as coefficient Of curvature) can be expressed as: where; DISC, 030, and DID the particle-size diameters corresponding to 10, 30, and 60 respectively, passing on the cumulative particle-size distribution curve. Fine and coarse aggregates are thought to be well graded fiftieth coefficient of curvature (C) is between 1 and 3. The calculated coefficient of gradation is 1. 03 for fine and 1. 05 for coarse aggregates. Coefficient of gradation is only one criterion in grading aggregates. Gradation also considers uniformity coefficient which will be discussed in next section.
Uniformity Coefficient (Cue). This is defined as ratio of the diameter of a particle of a size that is retained in sieve that allows 60% of the material to pass through, to the diameter of a particle of a size that is retained in a sieve that allows of the material to pass through. This can be simply expressed as: An aggregate is thought to be well graded if the coefficient of uniformity (Cue) is greater than 4 for coarse (gravel) and 6 for fine aggregate (sand). Calculated values for this parameter are 6. 59 and 1. 57 for fine and coarse aggregate. Classification.
Coarse aggregate is composed mainly Of gravel and crushed stones which pass 3-inches sieve but are retained on No. 4 sieve. Fine aggregates is mostly sand Which passes NO. 4 sieve but are retained on NO. 200 sieve. The experiment showed that the samples used are poorly graded gravel and well- graded sand. Both Of the aggregate pass the criteria for coefficient Of gradation which value should lie between 1 and 3. Fine aggregate is well graded sand with uniformity coefficient greater than 6. Coarse aggregate coefficient of uniformity is very small and did not exceed 4. Coarse aggregate sample is poorly graded ravel. V.
LABORATORY SUGGESTIONS Suggestions for Laboratory Improvement Here are some of my personal suggestions that I believe will help in improving the laboratory: Acquire New Lab Materials/Repair Old Materials. Some of the materials in the laboratory really need repair or replacement. Use and borrowing of some materials and equipment are sometimes on a first-come, first served basis because of limited availability. VI. SUMMARY AND CONCLUSION Coarse aggregate is composed mainly Of gravel and crushed stones While fine aggregate is composed of sand. Particle-size distribution curve of coarse aggregate is characterized by a Steep curve.
This means the coarse aggregate is poorly graded (uniformly graded) and has small variation in size. Particle-size distribution curve Of fine aggregate is characterized by an S-curve. Fine aggregate is well graded and has a gradation of particle size that spans evenly the size from coarsest to finest. This conclusion is supported by computed coefficients of uniformity and gradation which is 6. 79 and 1. 03 for (well graded) fine aggregates, and 1. 57 and 1. 05 for (poorly graded) coarse aggregate. Manual sieving procedures can be ineffective because the amount of energy seed to sieve the sample is varying.