Quantitative Assessment of Infrared Analysis of Concrete Admixtures

Concrete admixtures are used constantly in civil engineering projects. Most State Department of Transportation (DOT) specifications require these admixtures be tested and approved for quality and identification. These tests are important to ensure that the products have not been altered in any way to hamper their performance prior to application on the job site. One test method is to use infrared spectrophotometry scan (IR Scan) to verify the uniformity and equivalence of the job samples with the reference scan from manufacturer samples (ASTM C494/C494M-11).

In general concrete admixtures are used to enhance the concrete performance in the field. In this project, 23 of the most commonly used concrete admixtures in New Jersey Department of Transportation construction projects were selected. These include air-entrainers, accelerators, retarders, water reducers, and other combinations of these admixtures (NJDOT Material Specifications, 2011). Admixtures can accelerate/slow the setting time, improve workability, enhance frost and sulfate resistance, and help control strength development. About 80 % of concrete produced in North America contains one or more types of admixtures (Ramachandran 1995).

It is often required by the government agencies (DOT’s) to monitor the integrity of these admixtures so that they can guarantee the quality of materials that are used in their projects. ASTM (2012) C494 requires testing of concrete admixture in accordance with Table 1 (ASTM 494) designated as Level 1 testing. It also requires Level 3 testing needed for uniformity and equivalency. Level 3 testing is established using the following requirements: (1) Infrared analysis, (2) Residue by oven drying, and (3) Specific gravity. The work done in this study focuses on the infrared analysis. ASTM C494/C494M-11 Sect. 6.1.1 requires that the absorption spectra of the initial sample and the test sample be essentially similar. This section does not provide specific criteria for acceptance or rejection of the test sample.

Infrared spectroscopy is used both to gather information about a compound’s structure and as an analytical tool of assessment for qualitative and quantitative analyses of the conformity of mixtures (Fernandez-Carrasco et al. 2012). These scans can be used to interpret both organic and inorganic compounds (Coates 2000). Infrared radiation is absorbed by molecules and is converted into energy of molecular vibrations. When the radiant energy matches the energy of a specific molecular vibration, absorbance occurs (Fernandez-Carrasco et al. 2012). This absorbance would then hold unique information of a specific sample (spectrum).

It is possible to obtain an IR spectrum from samples in many different forms, such as liquid, solid, and gas. However, many materials are opaque to IR radiation and must be dissolved or diluted in a transparent matrix in order to obtain spectra. Alternatively, it is possible to obtain reflectance or emission spectra directly from opaque samples (Sherman Hsu and Settler 1997).

IR spectroscopy is typically used in cases where the sample (or spectrum) is a ‘‘total unknown’’ and an identification is required, the sample (or spectrum) is an unknown and it needs to be characterized or classified, and the sample generally is known but the existence of a specific chemical class needs to be determined (Sherman Hsu and Settler 1997). IR spectroscopy can also be used when, and for the purpose of this project, the sample is a complete known and the interpretation is required to confirm the material composition and/or quality. This would include product quality control of chemical compounds such as concrete admixtures and structural steel paints.

The Louisiana DOT outlines test methods for infrared spectrophotometric analysis ( Louisiana DOT 1994). The method is used for a variety of materials such as paint, epoxy resin systems, anti-strip additives, concrete admixtures, thermo-plastics, solvents and other materials that occur as a solid, low volatile liquid, or highly volatile liquid. DOTD TR 610M-94 outlines sample preparation procedures for solid samples and liquid samples. The interpretation of results is qualitative based on a favorable comparison of the infrared spectrum to that of the original sample. According to LADOT memo DOTD TR 610M-94, a sample is considered rejected if its IR spectrum exhibits significant nonconformity to the IR spectrum of the original sample, i.e. if there are different absorption valleys in the two spectra or if an absorption valley in one spectrum is significantly displaced from that in the other one.

The California Department of Transportation (Caltrans) published tests methods for concrete admixtures in 2007. In their CA Test 416, Caltrans outlines the testing procedure for IR scan of concrete admixtures. This procedure is somewhat different from ASTM C494/C494M-11. According to the Caltrans criteria (2007), test results are used for comparison purposes only and each spectrum is compared with samples run previously. Two materials are considered similar if all of the absorption peaks match the wavelength and relative magnitude (California Department of Transportation 2007).

The Illinois Department of Transportation (IDOT) published a list of approved concrete admixtures and specifications that outlines the submittal process for the approval of new concrete admixtures (Illinois DOT Bureau of Materials and Physical Research (2011). Among these specifications are those for the submittal of an infrared spectrophotometer trace (IR) of current production material, no more than 5 years old. The IR scan should be labeled with the date the scan was performed, the product name, and the manufacturer’s name. However, the IDOT specifications do not provide information on quantitative methods for acceptance of IR scans of concrete admixtures.

The New Jersey Department of Transportation (NJDOT) uses a quantitative assessment of IR scans based on correlation to accept or reject job samples. They use a correlation value equal to 0.975 for all admixtures based on the manufacturer recommendation. Although this may seem like a fairly high and relatively safe correlation to abide by, every admixture possesses their own unique chemical and physical properties, and may not have the same acceptable correlation values. Furthermore, the basis for using this correlation coefficient for quantitative assessment of concrete admixtures quality control was not established.

This study is seeking to establish acceptance criteria based on a rigorous testing program and statistical analyses to establish acceptable correlations as basis for quantitative assessment of infrared scans. This will help verify whether the concrete admixtures received from the job sites are acceptable using a quantitative approach.

The objective of this investigation is: (1) establish correlations coefficients and acceptable tolerances for standard manufacturer samples of concrete admixtures, (2) verify the established acceptance criteria by testing job samples, and (3) provide interpretations of IR scans of concrete admixtures including the factors that may influence them. This paper will present the findings from this study, discuss limitations and applications of developed correlation coefficients, and make recommendations for future tests that can be preformed to better understand and identify what causes the nonconformity of the IR Scans for concrete admixtures.

Quality control of concrete admixtures is very important and is enforced by State DOT specifications, special provisions, and project specifications. Concrete admixtures need to be tested by creating standard concrete mix designs in which the admixture is added to see if it enhances the desired concrete property and achieved the required specifications. Once the admixtures is qualified and approved, subsequent testing is required to confirm uniformity and equivalency of the product so that it will produce the same effects on concrete as originally approved. ASTM C494/C494M-11 specifies infrared scanning (IR) as one method to test for uniformity and equivalency. Infrared spectroscopy scans require precision and consistency for reliable IR results. Any minor inconsistencies in the testing method or impurities can skew the data and lead to erroneous interpretations. For this reason, there have been some variability and inconsistency in interpreting the IR scans of concrete admixtures which made it difficult to establish acceptability criteria. This study attempts to simplify and accurately interpret IR Scans for the purpose of quality control of concrete admixtures using a quantitative approach using extensive amount of test data.

In order to accomplish the objectives of this study, a number of admixtures were selected for this experimental study. Twenty-three concrete admixtures, most commonly used by contractors on construction projects in New Jersey were selected for this study (NJDOT 2007). The admixtures, their suppliers and their function are shown in Table 1.

Once all of the concrete admixtures have been identified, IR Scans were performed on the manufacturer’s samples to establish baseline data, correlations, and acceptable tolerances. The experimental procedure for the IR scans followed the ASTM C494/C494M-11 specifications which will be discussed later. To create the correlations coefficients for the selected admixtures, it was decided at the onset of the research program that three batches provided by the suppliers at three different dates will be used. For each admixture in each batch, four scans were performed. With these scans, an extensive data library was created and had enough scans to establish acceptable correlation coefficients for all the concrete admixtures. After all of the samples have been scanned and correlation coefficients have been found, job samples were tested to verify the applicability of the tolerances found from the research. Five job samples from the concrete admixtures were selected to be compared to the created correlation library. Three scans from each job sample were prepared.

Testing concrete admixtures is important to ensure that they have not been adversely modified or altered. The most effective and timely method of testing these chemical admixtures is by using an IR scan as outlined by ASTM C494/C494M-11. Since ASTM C 494/C494M-11 is the standard procedure, this study must stay within the boundaries of this ASTM Standard. Parts of this standard are slightly unclear and had to be interpreted as more and more scans were made. Another requirement was the drying time for the Ottawa sand. It was found at the onset of this study that for the sand used, a drying time of 10 h was sufficient to achieve the same dry weight after 17 h of drying time. Several admixtures were dried at 10 h and at 17 h and the results showed the difference in the dry weight from both drying time was negligible. Reducing the drying time from 17 to 10 h was important given the hundred of samples that were tested. These tests were performed using an infrared spectrometer. These tests were performed using the Perkin-Elmer infrared spectrometer shown in Fig. 1. The individual pellets are placed carefully inside the spectrometer and the machine passes a beam of infrared light through the sample. The spectrometer analyzes the amount of transmitted light and records how much energy is absorbed at each wavelength.

The computer then will record the sample’s wavelength versus transmittance (or absorbance) spectrum and shows a plot similar to the scan shown in Fig. 2. Figure 3 shows IR scans for twelve samples from three batches for admixture Pozzolith 200-N. These absorption characteristics provide information about the molecular structure of the sample. Therefore, the IR scan is unique to every material.

To interpret the data obtained from the IR scans, correlation coefficients were determined for each admixture based on all scans from all batches. These correlation coefficients were then used to establish acceptance criteria and tolerances. If a specific sample achieves the established correlation threshold, this would indicate that the concrete admixture has not been altered during the manufacturing process or storage prior its use. The formula for determining the correlation coefficient of a typical admixture is based on the following statistical relationship given in Eq. (1):

where, r = correlation factor, X = absorbance values of scan A of admixture/paint, \( \overline{x} \) = average of the absorbance values of scan A of admixture/paint, Y = average absorbance values of all scans from all three batches of admixture/paint \( {\bar{\text{Y}}} \) = average of the average absorbance of all scans from all three batches of admixture/paint.

The correlation coefficients obtained for the concrete admixtures from Eq. (1) are given in Table 2. These correlations coefficients correlate the average absorbance values of all 12 scans of an admixture to the individual absorbance values of each scan of that admixture. These correlation coefficients are very close to 1.0 as expected.

As mentioned in the introduction, state DOT’s are using different methods for the assessment of IR scan test results of concrete admixtures from job sites. Few State DOT’s have a quantitative assessment procedure in place for infrared analysis. The NJDOT is currently using a target correlation coefficient of 0.975 for acceptance criteria for all admixtures (Najm et al. 2011). This value was recommended by the manufacturer of the IR spectroscopy system; however, the basis of this target value was not established. The coefficient of determination (r²) of the correlation coefficient provided by the manufacturer is (0.975)² = 0.9506. This means about 95 % of the total variation in absorbance can be explained by the linear relationship. Accepting a correlation coefficient of 0.975 thus means accepting that the other 5 % of the total variation remains unexplained or determined by other variables or by chance. These unexplained data can also be looked at as an “error” in r². Examining the data obtained in this study for r and r² in Table 2 indicates that the coefficient of determination r² will vary from 0.98314 for admixture Eucon 91-R to 0.84987 for admixture Sika Air. The average error for all the admixtures in the last column in Table 2 is about 6.9 %. This average error is used to establish target value for r² as follows:

Therefore, the corresponding target correlation r is given by the square root of r². In this case, the target correlation r is equal to 0.965. Thus using average values from all admixtures and accepting an error of about 6.9 %, the target correlation value of all admixtures tested in this study will be 0.965. To be more specific, one can establish a target correlation for individual admixtures using the data in Table 3. For example, the target correlation for admixture Daracem55 will be 0.9698 with an error of about 6 % while that of admixture Glenium 7500 will be 0.9757 with an error of 4.8 %. The use of specific target correlation values for individual admixtures is more accurate. On the other hand, using an average correlation of 0.965 for all admixtures in this study is also acceptable given the value of the average error compared to the errors of the individual correlations.

The established (target) correlation coefficients for all admixtures evaluated in this study were tabulated in column (1) in Table 3. Also tabulated are the coefficients of determination r². The established correlations will be used to quantitatively assess job samples from road and bridge construction sites. Five job samples of admixtures were tested against the established target correlations to observe the applicability and the reliability of these correlations in providing quantitative quality assurance and quality control of concrete admixtures. Three IR scan tests were performed for each job sample. The five job samples were designated as follows:

These admixtures were actual job samples supplied by the NJDOT from several of their construction projects. Three IR scans from each job sample were prepared and compared to the target correlation coefficient of each admixture. Comparison of the individual correlations of the three job samples (total 15 scans) to the target correlation are shown in Table 4. One way to compare the results is, if any one of the job sample individual correlation coefficients is equal to or higher than the established correlation coefficient, then the job sample will be approved (pass); otherwise it will be rejected (fail). Table 4 shows that when comparing the correlation values of the individual scans to the target correlation, 10 out of 15 scans passed (4 out of the 5 job samples). Comparison of the individual correlations of the three job samples (15 scans) to a proposed average target correlation value of 0.965 for all admixtures is shown in Table 5. The comparison in Table 5 shows 9 out of 15 scans pass (4 out of the 5 job samples). Finally, comparing the average correlations of the three scans of each of the 5 job samples to the target correlation (0.965) in Table 6 shows that 4 out of 5 samples pass.

Quantitative assessments using the average correlations of the job samples and a target correlation of 0.965 seems to be acceptable acceptance criteria for most admixtures. The average error this case will be 6.9 % based on 276 performed IR scans for a total of 23 admixtures. As shown in Table 3 earlier, error levels vary for different admixtures and for certain admixtures lower correlation values may be used based on observations from job sample tests. More testing of job samples is needed to verify the consistency of the test results and to have more confidence in using individual target correlations instead of using an overall target correlation coefficient equal to 0.965. Also further testing from additional manufacturer samples and batches is needed for further investigation and justification of the target correlations and for continuous improvement of the target correlations.

The ASTM procedure for testing concrete admixtures with Infrared Spectrophotometry Scanning is very precise and depends on several factors and parameters including the human factor. This was observed by several State DOT engineers when testing samples and was also observed by the authors of this paper. Therefore, it is important to identify and evaluate the factors that affect these IR scans and try to minimize their influence. In this study, two factors were investigated. They include the effects of drying time and moisture content and the effects of potassium bromide (KBr).

Correlation coefficients for concrete admixtures were obtained using IR scans from three different batches from the manufacturers supplied at three reasonable separated time intervals. The reliability of this quantitative approach to test job samples was analyzed and evaluated by testing several randomly selected job samples using the established target correlations. The quantitative approach seems to be a more reliable method for determining whether or not concrete admixtures are acceptable and will perform the tasks required of the material. The quantitative approach can be used to support assessments made by the qualitative method. The limitation of this method is bound by the library created from the supplied manufacturer admixtures. This library of scans and correlation values needs to be updated whenever concrete admixtures are altered by the manufacturer. The level of accuracy of this approach is also dependant on the acceptance criteria and the target error level for admixtures. More testing of job samples is needed to verify the selected target correlation. Further testing of manufacturer samples and batches may be needed for further investigation and justification of the target correlations and error level. The study showed that the drying time may be increased to 24 h for air entraining admixtures, but more testing should be done in order to confirm this drying time. Potassium bromide (KBr) has a significant effect on the correlation coefficients and must be consistent throughout the entire library and job sample testing process. Future research is needed to find what other factors, in addition to the KBr, that may influence the IR analysis such as sample mixing time, press time, volume of KBr, and sample volume.