Design and Materials Laboratory Report

  

 

 

 

 

 

KB2022 – Design and Materials

Laboratory Report

 

 

 

 

 

 

 

Module Leader

Dr Mohammadali Rezazadeh

 

Contents

Page…………………………………………. Aggregate Sampling

Page…………………………………. Batching Concrete and Determining Slump

Page……………………………………………Compressive and Rebound Hammer Non-Destructive Tests on Concrete Cubes

Page…………………………………………Flexural Bending Tests on Plain and Reinforced Beams

Page…………………………………………. Timber Beams Test

Page………………………………………………. Steel Tensile Test

Page…………………………………………………References

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Test 1 - Aggregate Sampling

In this test we will look at both the coarse and the fine aggregate in a sample and determine the contents via the shape and the size using sieve analysis only.  The purpose for this test is to classify what the aggregate we are using and can be used in different applications such as making concrete.

 

Method

 First, we would weigh each large sieves from the large sieve set (the opening sizes are >4.75mm) including the pan using a balance and record each of the weights on a table.

Next, we will place the bucket on the balance and then zero the weight so that we can measure the aggregate weight. We will take a random batch of 12kg coarse aggregate and weigh it to make sure its 12kg. Using a ruffle box, we evenly pour the sample into the box so it can separate the weight in half and taking half of the weight we pour it one more time to get approximately 3kg and weigh the sample and record it.

Making sure that the order of the larger sieves is correct (largest at top and smallest at bottom) we will then pour the sample aggregate evenly in the larger sieves, place the lid on top of the sieves.

Next, we would place the sieves in a vibration machine where it will separately apply force onto each sieve so the aggregate can fall to it’s determined sieve, we wait for 10-20 minutes.

Then we would take each of the sieves including the separated aggregate and weigh them again and record each weight of the aggregate and sieve on a table.

We would then do the same with the fine aggregate but the series of sieves would be less than or equal to 4.75mm opening size. Weigh each of the sieves and record the weight taking notice on the order size of the sieves.

 Take a beaker and place on the balance and zero the weight of the beaker. Then we need to scoop 3kg of the fine aggregate without using the ruffle box due to its size. Weigh the aggregate and record it.

Pour the fine aggregate on the small sieve tower and place a lid. Then we place the sieve in a smaller vibration machine so the aggregate can get to its sieve opening size and wait for 10-20 minutes. Take the sieves out of the machine and then weigh each of the sieves again including the pan and record the weight of the aggregate for the specified opening size.

 

 

After calculating the mass and the percentage of the aggregate passing through each sieve size for coarse and fine aggregate we then draw a particle distribution curve each for the fine and coarse aggregate due to the size differences.

 

 

Then we can calculate the uniformity coefficient () for both the fine and coarse aggerate distributions so we can classify the aggregate that we sampled.

Coarse Aggregate

 where C_u < 4 then the coarse aggregate sample is uniformly graded, in this case the aggregate sample has a small range of particle sizes.

Fine Aggregate

 Where C_u > 6 then the fine aggregate sample is well graded fine aggregate within the sample collected in the test.

We then add another column in the table of values and using each percentage passing we take 100 from this and finding the total we can calculate the fineness modulus for fine/coarse aggregate.

Coarse Aggregate

Fineness Modulus:

Fine Aggregate

Fineness Modulus:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Data

Fine Aggregate

Sieve size and weight		Mass retained on each sieve		Total mass passing each sieve (g)	Percentage passing each sieve	100-PP%
Size (mm)	Weight (g)	Pan + Mass	Mass	Total Mass
3.35	516.8	547.9	31.1	267.2	89.57%	10.43
2.36	479.5	507.5	28.0	239.2	80.19%	19.81
1.18	440.6	502.0	61.4	177.8	59.60%	40.4
0.60	392.8	449.6	56.8	121.0	40.56%	59.44
0.425	379.3	403.0	23.7	97.3	32.62%	67.38
0.300	369.2	391.6	22.4	74.9	25.11%	74.89
0.212	359.2	386.5	27.3	47.6	15.96%	84.04
0.150	282.6	325.5	42.9	4.7	1.58%	98.42
Pan	245.7	250.4	4.7	0.0	0.0%	100
Total			298.3			554.81

 

Coarse Aggregate

Sieve size and weight		Mass retained on each sieve		Total mass passing each sieve (g)	Percentage passing each sieve	100-PP%
Size (mm)	Weight (g)	Pan + Mass	Mass	Total Mass
37.5	1447.5	1447.5	0.0	3115.0	100%	0
28.0	1375.5	1375.5	0.0	3115.0	100%	0
20.0	1330.0	1492.5	162.5	2952.5	94.78%	5.22
14.0	1170.5	2553.0	1382.5	1570.0	50.40%	49.60
10.0	1163.0	2377.5	1214.5	355.0	11.40%	88.60
6.3	1019.5	1312.0	292.5	63.0	2.02%	97.98
5	993.0	1007.0	14	49.0	1.57%	98.43
Pan	691.5	740.5	49	0.0	0.0%	100
Total			3115.0			439.83

 

 

 

Conclusion

Coarse aggregate is mostly made up of gravel and broken stones, whilst fine aggregate is mostly made up of sand. The above-shown particle size distribution curve has a severe slope. This signifies that the coarse aggregate is weakly graded, which means that it is consistently graded and has very little variation in particle size on the sample. The fine aggregate particle-size distribution curve is characterized by an S-curve; the fine aggregates are highly graded, which means they have various sizes in that sample. The particle gradation is uniform, ranging from coarsest to finest. Because the aggregate sizes interlock effectively, the strength of the concrete will be much enhanced when such aggregate is utilized for concreting. Our finding is further corroborated and backed up by computer data, which show that the coefficients of uniformity and gradation are 6.8 and 1.04 for highly graded aggregates, respectively, and 1.57 and 1.06 for poorly graded coarse aggregates. However, there are some significant differences since we employed manual sieves; this gap is understandable because the manual technique is unsuccessful due to the variety in energy used. Finally, the conclusion based on the interpretation of the manual findings is a six, which is similar to the conclusion based on the calculated coefficients of uniformity and gradation.