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Accelerated Strength Testing of Portland Pozzolan Cement Concretes by the Warm Water Method

Background

Background.

Portland Pozzolan cement has an important place among the group of blended cements and it is well known that the type of cement is one of the important parameters that affect the accelerated test results. Modern and rapid construction practices and procedures require the assessment of concrete quality at an age earlier than the customary 28 days after the placement.

Experimentation and Data Collection.

Two separate deliveries of cement of the same type were made to the Materials Structural Laboratory at the Black Sea University in Turkey. The cement was Portland Pozzolan cement consisting of 70% Portland cement and 30% natural Pozzolan. It is very similar therefore to the cement type in ASTM C 595-76. The chemical and physical properties of the cement are shown in Table 1 below.

Table 1. Physical and Chemical Properties

Physical Tests

Delivery 1

Delivery 2

Specific gravity, g/cm3

3.08

3.05

Fineness

200 mm (passing), %

90 mm (passing), %

99.4

88.5

99.7

90.0

Time of setting (Vicat test)

Initial

Final

3 hours and 35minutes

5 hours and 35 minutes

3 hours and 15 minutes

5 hours and 50 minutes

28-day strengths

Flexural, MPa

Compressive, MPa

5.7

33.4

5.7

31.0

Chemical Analysis, %

SiO2

12.81

10.12

Al2O3

7.26

7.64

Fe2O3

3.62

4.36

CaO

53.17

49.27

MgO

1.23

1.40

SO3

2.04

1.81

Loss of Ignition

3.22

3.83

Insoluble residue

15.08

19.04


Using these deliveries, two separate experiments were carried out at the University.

Specific gravities of the crushed aggregates ranged between 2.58 and 2.67 and the maximum size of coarse aggregate was 30 mm. A specific concrete mix was used that consisted of an aggregate-cement ratio of 6.38, a water-cement ratio of 0.5 and a cement content of 300 kg/m3. For each cement delivery, ten 20 litre batches were prepared in a 30 litre capacity tilted drum mixer. An initial dry mixing for 2 minutes was followed by wet mixing for 5 minutes. From each batch, 2 specimens were cast in 150x 300 mm cylindrical reusable molds with steel caps. Consolidation of the specimens was achieved by external vibration on a vibrating table of 2800 rpm.

A standard 28-day cure was applied to each specimen. Curing tanks and molds conformed to ASTM C 684-74. A 60 ton capacity universal testing machine and a 0.25 MPa/sec loading rate were used for compression testing of each specimen and the strength results are shown in the data file 'Civil-project-data-2018.xlsx' along with this document.

This was the biggest experiment consisting of fifty-nine different concrete mixes.Specific gravities of the crushed aggregates ranged between 2.58 and 2.67 and the maximum size of coarse aggregate was 30 mm. The concrete mix proportions are shown in Sheet2 of the data file. For each concrete mix a 20-litre batch is prepared in a 30 litre capacity tilted drum mixer. An initial dry mixing for 2 minutes was followed by wet mixing for 5 minutes. From each batch, 2 specimens were cast in mm cylindrical reusable molds with steel caps. Consolidation of the specimens was achieved by external vibration on a vibrating table of 2800 rpm. Cement deliveries were randomly assigned to each batch, so that some batches were made from the cement obtained from the first delivery, whilst the other batches were made from cement obtained from the second delivery.

Experimentation and Data Collection

Accelerated cure was applied to one specimen, while standard 28-day cure was applied to the remaining specimen. The specimen reserved for the warm water method was placed in water at 35oC immediately after casting. After curing for 23.5 hours the cylinder was removed from the water tank, demolded, capped, and tested at 24 hours. (Curing tanks and molds conformed to ASTM C 684-74). A 60-ton capacity universal testing machine and a 0.25 MPa/sec loading rate was used for such compression testing. The strength results are shown in Sheet2 of the data file.

Modern and rapid construction practices and procedures require the assessment of concrete quality at an age earlier than the customary 28 days after the placement. This requires the use of accelerated testing methods, and you are required to write a mini project that carries out a detailed statistical investigation on the experimental data discussed above. The project should provide an answer as to whether the accelerated curing procedure, described above as the warm water method, is capable of producing accurate 28-day compressive strengths and whether there is an optimal concrete mix for the accelerated testing method.

When writing your report, structure it in a way that follows the style of a technical report with the format demonstrated in the attached template. The following structure is suggested (but not mandatory) to demonstrate the necessary components of such report:

  1. Abstract Give a brief summary of the technical problems addressed and how they were addressed, followed by your findings and conclusion (again, very brief).

  2. Introduction/Background what is/are the (civil engineering) problem(s) to be addressed. How the experiments were organised and the purposes of conducting the experiments. What kind of data have been produced and what is your intention to use these data?

  3. Methodology  Brief description of the statistical methods you used to address the problem(s), providing justification of the methods you choose to use.

  4. Analysis - This is the major part of the report. You should demonstrate the applications of the methods you chose to the data, the results and explanations.

  5. Discussion/Conclusion you should reflect back on whether your analysis has helped to answer the question and how that is answered. This again, has to be presented succinctly.

For the analysis itself, you need to cover and address all the following aspects in a way that reveals a progressively greater understanding of the two experimental data sets.

  1. Describe the sources of variability present in each data set of Experiment 1. Then using appropriate data displays, describe each data set in Experiment 1, highlighting any similarities or differences that may exist between the 2 deliveries of concrete to the university.
  1. Using the data sets collected in Experiment 1, construct an appropriate parametric and non-parametric test to assess the claim that the typical 28-day compressive strength is the same for each delivery. When writing up your analysis of this claim state any assumptions that need to be made in conducting these tests, and if appropriate carry out tests to validate these assumptions. Discuss also the advantages and disadvantages of each test.
  1. Using the data set collected in Experiment 2 and the technique of multiple linear regression, estimate the parameters of the following second order response surface model:

second order response surface model

where Y2 is the accelerated compressive strength and is the prediction error or residual. is the water-cement ratio, is the Aggregate-cement ratio and is the cement content.

When writing up your analysis, describe how well this model fits the data, which variables are statistically significant (important), what meanings can be attached to the b parameters. State any assumptions that need to be made is assessing such statistical significance, and if appropriate carry out tests or construct scatter plots to validate these assumptions. Use the model to find the concrete mix that maximises the accelerated compressive strength, making full use of any suitable plots to explain these conditions.

  1. Finally, consider ignoring the concrete mix and estimate the parameters of the following simple model:

simple model

where y1 is the 28-day compressive strength and is the accelerated compressive strength and is the residual.

When writing up your analysis, describe how well this simplified model fits the data, the meaning of all the parameters, the degree of accuracy achievable when predicting the 28-day compressive strength from accelerated test data (as described by a 95% confidence interval on an actual vs prediction plot).

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