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18 Chapter 3. Laboratory Measurements (Phase II) The following sections describe the sampling and testing program we implemented during Phase II. We used data from the laboratory test program to study the precision and bias of measurements from different test procedures. We also compared the measurements from electrochemical properties to observations of corrosion rates with respect to the same sources to evaluate the veracity of the corresponding characterizations of corrosion potential. We used this information to develop recommendations and propose protocols for sampling earthen materials, proper testing, and characterizations of corrosion potentials. 3.1. Introduction The objectives of Phase II were to determine when test results from the current AASHTO test procedures are most applicable to characterize the steel corrosion potential of earthen materials and when alternative test methods for measurements of geochemical and electrochemical properties should be applied. During Phase II, we studied alternative laboratory test procedures for measuring the electrochemical properties of soils applied to a sampling domain incorporating a broad range of materials (mostly those commonly used in MSE wall constructions). The data include characterization of different sample sources (e.g., maximum particle size and gradation) along with the measurements of geochemical and electrochemical properties of the samples including resistivity, pH, and chloride and sulfate contents. In this chapter, we summarize the laboratory data obtained from 27 different samples of earthen materials. We documented performance data (i.e., corrosion rates) of plain and galvanized steel specimens, embedded in 19 of these sources. While electrochemical test results were used to characterize the corrosion potential of each source, the performance data were used to correlate these characterizations to the corrosion rates. We compared the results from applying different test standards with those obtained from equivalent AASHTO tests and identified the reasons for the observed differences (i.e., the AASHTO tests were used as a reference). A brief description of the data set used in this chapter (the 27 different material samples) is presented in the next section. This is followed by the key results obtained from different test methods in the form of resistivity/conductivity, pH, and chloride/sulfate content. Finally, we discuss the trends observed within the data sets and compare the results from the alternatives with the results from AASHTO test methods. The test procedures and details about precisions and biases are tabulated and presented in Appendix B. Salient details are presented in what follows. 3.2. Description of Data Set Table 3-1 summarizes the materials that were included in the laboratory test program for NCHRP 21-11, and their sources. We collected samples from various sources throughout North America for the laboratory test program. These sources were from New York (5 sources), North Carolina (3), South Carolina (2), Florida (1), Louisiana (1), Arkansas (1), Texas (10), British Columbia (1), and Calgary (1). Overall, we obtained 27 samples from these 25 sites. The minerology of aggregate sources included limestone (13), granite (2), sandstone (1), natural sands/silica (6), glacial till (1)
Florida EL Paso MSE M-U-D NY South Carolina LWF PIP South Carolina GB Pharr TX Beaufort NC Rochester NY El Paso TX Calgary AB Prince George BC Ashdown AR Temple TX Sprain Brook NY Raleigh NC Garden City TX Maple Rd NY Wake Forest NC Round Rock TX El Paso Coarse MSE Louisiana LWF Crushed Waco TX Bastrop TX TYPE /USCS Sand/SP Sand/SP Sand/SP Expanded Clay/SW Sand/SW Limestone/SW Limestone/SP Limestone/GP Sand/SP Limestone/SW Sand/GW Glacial Till/GW Sandstone/GW Limestone/GW Limestone/GW Granite/GP Limestone/GP Limestone/GW Granite/GP Limestone/GP Limestone/GP expanded clay/SW Limestone/GW Limestone/GP LATITUDE 29Â°10'44.60"N 31Â°44'30.5"N 43Â° 8'12.86"N 33Â°41'4.49"N 41Â° 2'16.19"N 33Â°41'4.49"N 31Â°56'15.7"N 34Â°43'37.57"N 43Â° 6'36.33"N 31Â°56'15.7"N 50Â°53'33.50"N 53Â°38'35.60"N 33Â°46'18.5"N 31Â°06'11.0"N 41Â° 3'44.43"N 35Â°52'24.89"N 31Â°51'39.9"N 42Â°59'28.56"N 35Â°57'55.56"N 31Â°47'16.8"N LONGITUDE 82Â° 8'40.85"W 106Â°22'26.6"W 75Â°16'14.39"W 78Â°57'35.90"W 73Â°56'58.68"W 78Â°57'35.90"W 106Â°32'38.8"W 76Â°39'51.89"W 77Â°36'0.36"W 106Â°32'38.8"W 114Â° 3'15.88"W 122Â°39'54.35"W 94Â°10'54.0"W 97Â°21'32.2"W 73Â°48'25.95"W 78Â°34'6.30"W 101Â°35'32.2"W 78Â°47'18.76"W 78Â°32'30.93"W 106Â°31'13.6"W LOCATION ON WALL MSE wall is an in-line abutment spanning between two bridge approaches and a median. MSE walls serve as the abutment facing and as a grade separation along the approach. The in- line abutment spans the median. MSE walls support the approaches to the viaduct, and include a facing that spans the median that separates this divided highway. MSE walls support the approaches and serve as facing at the viaduct abutments. A creek flows in front of the abutments. MSE walls support the approaches to the viaduct, and include a facing that spans the median that separates this divided highway. MSE walls support the approach to the viaduct crossing the intercoastal waterway. MSE walls support the approach and serve as facing to the abutments for a viaduct that crosses several highways at an intersection. The MSE wall serves as an abutment to the viaduct which crosses a highway. 2000 feet long soil nail wall supports a side hill cut parallel to the Frazier River below the wall. MSE walls support the approaches and the abutment to a viaduct. MSE walls serve as abutments to the viaduct with sloping wingwalls. MSE walls serve as abutments to the viaduct with sloping wingwalls. MSE walls serve as abutments to the viaduct with sloping wingwalls. Sample sent to UTEP from producer in Louisiana. This sample was tested as-is, and after crushing. Extremely low Chloride and Sulfate content Extremely low Chloride and Sulfate content Extremely low Chloride and Sulfate content Extremely low Chloride and Sulfate content Extremely low Chloride and Sulfate content Extremely low Chloride content and low Sulfate content Low Sulfate and Chloride content High Chloride content over acceptance limit and low Sulfate content High Sulfate content just below acceptance limit and high Chloride content above acceptance limit High sulfate and low chloride ion content Extremely low sulfate and chloride ion content High Sulfate and Chloride content over acceptance limit Extremely high Sulfate content and low Chloride content WATER RUNOFF Extremely low Chloride and Sulfate content Extremely low Chloride content and low Sulfate content High Chloride content, over acceptance limit. Low Sulfate content. High Sulfate content over acceptance limit and extremely low Chloride content Extremely low Chloride content and low Sulfate content Extremely high Chloride content and low Sulfate content HIGH CHLORIDE/SULFATE? Extremely low Chloride and Sulfate Content Low Chloride and Sulfate content Extremely low Chloride and Sulfate content Extremely high Sulfate content and low Chloride content Surface runoff from the pavement is directed to the side slopes of the approach embankment. The viaduct spans across a railway and there are no surface waters nearby. The surrounding area is relatively flat. Paved median was originally constructed to be sloped away from the MSE wall face, but ponded water was observed. Super- elevation of the highway pavement also directs water towards the median in some locations. Subdrains installed within the median direct stormwater to the low end of the approach (away from the abutments). The surrounding area is relatively flat. Surfacewater directed to the paved shoulders and into drainage inlets. The viaduct crosses a creek and the bases of the MSE walls are within the stream banks. Subdrains and drainage inlets located within MSE fill at some locations. No drainage inlets were observed along the MSE walls. Samples taken from side hill cut near corrosion monitoring stations that were installed to monitor the performance of hollow bar soil nails that serve to stabilize the cut. Sample came from quarry (Hanson Aggregates) and was sampled from a stock pile. Corrosion rate measurements were obtained from laboratory measurements on steel specimens embedded within material from the stockpile sample. Sample was taken from an MSE wall that was deconstructed. Subdrains installed within the median direct stormwater to the low end of the approach (away from the abutments). The surrounding area is relatively flat. Stormwater directed towards the MSE wall face due to the pavement superelevation. The surrounding area is relatively flat. Waterway is in front of the abutments and the edges of the approach are within a coastal wetland. Subdrains direct stormwater away from the viaduct and abutments. Runoff from the superelevated bridge decks will travel towards DI's or to embankment slopes along the bases of the walls. Surrounding area is relatively flat. Stormwater is directed down the side slopes of the approach embankment or collected at a point behind the abutment and directed into the pavement subdrain. Drainage inlets are installed at several locations along the tops of the soil nail walls. Storm and meltwaters will run down the surface of the hillside behind the soil nail wall and into a swale at the top of the wall. The swale directs water to DI's that run down the face of the soil nail wall and discharge into the Frazier River in front of the wall. Sample came from a quarry (Terra Firma Materials) and was sampled from a stock pile. Sample was obtained from the quarry that was the source during construction. NCDOT installed corrosion monitoring stations within the MSE wall fill during construction (2016). DRAINAGE INLET NEARBY No subdrain behind the wall. Pavement edge drains and drainage inlets are located within the MSE fill. Subdrains and drainage inlets located within MSE fill at some locations. Subdrains are located within the median carrying stormwater to the low end of the approach, away from the abutments. Samples were taken from test pits advanced behind the sloping wingwalls of the abutment for the viaduct, near the locations of corrosion monitoring stations installed behind the abutments by NCDOT during construction (2005). Sample was taken from a MSE wall that was deconstructed Sample was taken from existing MSE wall and UTEP installed corrosion monitoring stations at this site. Samples were taken from test pits advanced beneath the paved shoulder. Metal loss and corrosion rates were observed from reinforcement samples that were exhumed and examined after the wall failure. Sample came from quarry (Laredo Paving, Garden City) and was sampled from a stock pile. Corrosion rate measurements were obtained from laboratory measurements on steel specimens embedded within material from the stockpile sample. Samples were taken from test pits advanced behind the sloping wingwalls of the abutment for the viaduct, near the locations of corrosion monitoring stations installed behind the abutments after the wall was constructed (1986) by NYSDOT in 1988. Drainage inlets capture runoff from the viaduct and direct the water away from the walls. Drainage inlets are included within the embankment behind the wall and thought to have contributed to the failure in 2016 No noticeable drainage inlet behind MSE wall. Drainage inlets are located within the median. Stormwater runoff collected by the DI's is directed away from the wall. Stormwater runoff follows along the superelevated paved shoulders to the drainage inlets located within the median or at the shoulder. Stormwater runoff is collected from the end of the bridge deck and directed down the embankment slopes via a subdrain. The topography surrounding the site is relatively flat. Stormwater runoff will be captured by paved ditch behind the sloping wingwall. This is a raised embankment and the topography of the surrounding area is flat. CoarseFine Medium UNDER ASPHALT/CONCRETE Samples were taken from within the median, near a corrosion monitoring station established by FDOT in 1997. Sample was taken from within an existing MSE wall. UTEP installed corrosion monitoring stations at this site. Samples retrieved from beneath a paved median, near a corrosion monitoring station that was installed during construction (2000) by NYSDOT. Samples were taken from within the median while corrosion monitoring stations were being installed one year after construction (2016). LWF was located beneath the granular base that capped the top of the MSE wall fill. Samples taken from borings advanced beneath the shoulder of the pavement and into the MSE fill, near the locations of corrosion monitoring stations installed by NYSDOT during construction (2000). Samples were taken from within the median while corrosion monitoring stations were being installed one year after construction (2016). GB was placed as a cap over the expanded clay LWF. Samples taken from borings advanced beneath the shoulder of the pavement and into the MSE fill, near a corrosion monitoring station that was installed after construction (1980) by NYSDOT in 2000. Sample came from a quarry (Jobe Avispa Quarry) and was sampled from a stock pile. Corrosion rate measurements were obtained from laboratory measurements on steel specimens embedded within material from the stockpile sample. Samples were obtained from a test embankment constructed from the same sources of materials used to construct the MSE walls. Samples were taken from test pits advanced behind the sloping wingwalls of the abutment for the viaduct, near the locations of corrosion monitoring stations installed behind the abutments by NCDOT during construction (2004). Drainage inlets located behind the wall face. Subsurface drainage is installed behind the MSE wall face. Table 3-1. Summary of sample sources. 19
20 and expanded clay light weight fill (2). In addition, we collected three separate samples from different depths at the Palisade Interstate Parkway (PIP) site in Orangeburg, New York. We present details of each sample in Figure 3-1 in terms of (a) composition, (b) grading number (GN), and (c) percentage passing the No. 10 sieve. The samples represent a broad range of gradations and compositions ranging from fine sand to coarse, clean, and open-graded, gravel. The composition is described in terms of the percentages of gravel (% retained on Â¼ inch sieve), coarse to medium sand (passing Â¼ inch and retained on the No. 40 sieve), fine sand (passing the No. 40 sieve and retained on the No. 200 sieve) and fines (% passing the No. 200 sieve). Sieve analyses were conducted in accordance with Tex-110-E whereby the percent passing the No. 200 sieve was determined by dry sieving. We summarize the composition of the materials as follows: â¢ Three samples were predominately (i.e., more than 50%) fine sand, â¢ Six samples were predominately coarse to medium sands, â¢ Two samples were mixtures of fine and coarse particles, where none of the components were equal to or greater than 50% of the total, â¢ Sixteen samples were predominately gravel varying from sandy gravels to clean and open graded gravels with no sand content, and â¢ None of the samples had more than 5% passing the No. 200 sieve. The grading number (GN) expresses the coarseness of the sample with a number ranging from 0 to 7. The GN is computed using Equation (3-1) (Oman 2004): GN = 1/100 Ã (ðððð1 ðððð + ðððð3 4 ðððð + ðððð3 8 ðððð + ðððð#4 + ðððð#10 + ðððð#40 + ðððð#200) (3-1) where PP signifies percent passing. The value of GN increases with respect to fineness of the sample. For example, GN equal to 0 represents a very coarse sample (> 1â), and GN equal to 7 represents a sample in which 100% of the material passes the No. 200 sieve. Values of GN in this study ranged between 0.15 and 5.65, with an approximate median of 3 (i.e., about half of the samples had GN greater than 3 and the rest had GN smaller than 3). In this study samples with GN < 3 are gravels, in which less than 12 percent of the sample (% by weight) passes the No. 40 sieve (i.e., includes little fine sand). The percentage passing the No. 10 sieve (2 mm) is of particular interest because the current test procedures for measurements of electrochemical properties specified by AASHTO are performed on the samples after they have been separated on a No. 10 sieve. We summarize the PP#10 from the sample domain as follows: â¢ Five samples had PP#10 > 60; â¢ Twelve samples had 25 < PP#10 < 60; â¢ Three samples had 10 < PP#10 < 25; and â¢ Seven samples had PP#10 <10
21 (a) Sample composition (b) Grading numbers of the samples (c) Percent passing No. 10 sieve Figure 3-1 Characteristics of the sample domain used in the laboratory investigations. 0% 20% 40% 60% 80% 100% Fl or id a EL P as o M SE M -U -D N Y So ut h Ca ro lin a LW F PI P @ 1 5f t PI P @ 1 0f t So ut h Ca ro lin a G B PI P @ 5 ft Ph ar r T X Lo us ia na L W F Cr us he d R oc he st er N Y El P as o TX C al ag ar y A B Pr in ce G eo rg e BC A sh do w n A R Te m pl e TX Sp ra in B ro ok N Y R al ei gh N C G ar de n C ity T X M ap le R d NY W ak e Fo re st N C R ou nd R oc k TX Lo us ia na L W F Un cr us he d W ac o TX El P as o C oa rs e M SE Sa n An to ni o B as tr op Fine Medium Coarse Pe rc en ta ge Fines Fine Sand Coarse Sand Gravel 0 1 2 3 4 5 6 Fl or id a EL P as o M SE M -U -D N Y So ut h Ca ro lin a LW F PI P @ 1 5f t PI P @ 1 0f t So ut h Ca ro lin a G B PI P @ 5 ft Ph ar r T X Lo us ia na L W F Cr us he d R oc he st er N Y El P as o TX C al ag ar y A B Pr in ce G eo rg e BC A sh do w n A R Te m pl e TX Sp ra in B ro ok N Y R al ei gh N C G ar de n C ity T X M ap le R d NY W ak e Fo re st N C R ou nd R oc k TX Lo us ia na L W F Un cr us he d W ac o TX El P as o C oa rs e M SE Sa n An to ni o B as tr op Fine Medium Coarse G ra di ng N um be r ( G N ) 0 10 20 30 40 50 60 70 80 90 100 Fl or id a EL P as o M SE M -U -D N Y So ut h Ca ro lin a LW F PI P @ 1 5f t PI P @ 1 0f t So ut h Ca ro lin a G B PI P @ 5 ft Ph ar r T X Lo us ia na L W F Cr us he d R oc he st er N Y El P as o TX C al ag ar y A B Pr in ce G eo rg e BC A sh do w n A R Te m pl e TX Sp ra in B ro ok N Y R al ei gh N C G ar de n C ity T X M ap le R d NY W ak e Fo re st N C R ou nd R oc k TX Lo us ia na L W F Un cr us he d W ac o TX El P as o C oa rs e M SE Sa n An to ni o B as tr op Fine Medium Coarse % P as si ng # 10 s ie ve
22 3.3. Comparison of Results from Different Test Methods We compared results obtained from different test procedures in terms of: (a) precision/repeatability, (b) bias relative to those obtained from the current AASHTO tests, and (c) trends we identified from the data. We made these comparisons to check whether any of the procedures perform better than others in terms of repeatability, precision, and bias. We also identify cases where the results from different test methods are similar, and where the results are different. For cases where differences in results are observed, we performed further analyses to identify the best result for characterizing the steel corrosion potential. In this section, first, we describe the precision and bias from measurements of resistivity for different samples/specimens, then describe measurements of salt contents and pH. Samples in this report are considered to be the material that was retrieved from the source in bulk (i.e., including all particle sizes), and specimens to be the portions of the samples that are prepared (e.g., passed through a certain sieve number) for testing as prescribed by a given test standard. 3.3.1 Resistivity We summarize the statistics describing the precision observed from testing replicates and the bias of each test procedure with respect to AASHTO T-288 in Figure 3-2. The bar graphs and error bars shown in Figure 3-2 represent means and standard deviations, respectively (mean values are presented in an ascending order). Five of the test procedures, included on the left-hand side of Figure 3-2, directly measure the resistivity of a compacted soil specimen in a soil box. The remaining three test procedures, shown on the right-hand side of the figure, measure the conductivity of a leachate extracted from a soil/water solution (the resistivity of this solution is the reciprocal of conductivity). Soil box tests allow for the effects on resistivity from level of compaction, moisture content, and texture of the soil to be investigated. Test methods performed on the specimens compacted in a soil box include AASHTO T-288 (2016), ASTM G-187 (2018), Tex-129-E (1999), Tex-129-M, and ASTM WK 24621. These test procedures vary in terms of the particle sizes included in the test specimen, the specimen preparations prior to testing, the size of the test box (depends on the maximum particle size), and the moisture content at which the minimum resistivity is reported (see Table B-2 in Appendix B for a summary of the different test procedures). The process for determining the resistivity from a soil box test is described in Section 220.127.116.11 (see Figure 1-1). Tests involving measurements of conductivity from leachates extracted from the solids (Tex-620-J, Tex-620-M and SCDOT T-143) include (a) preparing a measured amount of dry material for testing, (b) adding a measured volume of distilled or DI water, and (c) measuring conductivity of the extracts. In leaching tests, differences include dilution ratios (i.e., mass of water per mass of soil), methods of agitation, and the resting times, at which no agitation is applied before starting the conductivity measurements. Resistivity of the leachate is computed as the reciprocal of conductivity. Results from leaching tests cannot be directly correlated to those obtained from compacted specimens. This is because other factors including tortuosity of the electrical current path through an actual compacted specimen significantly affect the resistivity measurements.
23 (a) precision (b) bias with respect to AASHTO T-288 Figure 3-2 Summary of the test results for measuring resistivity/conductivity. We discuss details about the precision and bias of the test results obtained from different test procedures in the following subsections. 18.104.22.168 Precision/Repeatability for Individual Test Methods We tested three to five replicates from nine different samples, for each test method. The nine samples represent a range of characteristics in terms of coarseness (gradation), source, mineralogy, and corrosivity (range of resistivity). We tested five replicates using samples from Florida; South Carolina (light-weight fill); and El Paso, Texas. We tested three replicates from six more samples from Marcy-Utica-Deerfield (M-U-D), NY; South Carolina (granular base); Pharr, Texas; Rochester, NY; Raleigh, NC; and Wake, NC. We maintained consistency between replicates by controlling the gradation of each replicate. We broke each sample down into individual grain size components and then recombined them into replicates such that that each satisfied the overall
24 gradation of the sample. We did this to minimize the effects of sample error on the test results such that the variation in results was mostly related to differences among the test procedures. We computed the mean (Âµ), standard deviation (Ï), and coefficient of variation (COV = Ï/Âµ) from the results obtained from each set of replicates. We generated further statistics from the replicate COVs to obtain the means (Âµcov) and standard deviations (Ïcov) of the COVs observed between samples. We used the coefficients of variation between measurements to describe the precision of each test method with a ranking index (RI) as shown in Equation (3-2). RI = Î¼COV + ÏCOV (3-2) Lower RIs correspond to better repeatability of the results for a given test method. In Figure 3-2 (a), the RI values correspond to the upper limit of the error bars. The obtained RIs of the resistivity test methods range between 6.8 and 13.2 percent. We made the following observations from the laboratory resistivity test data: â¢ The lowest RIs (best repeatability) are observed from Tex-620-J (2005), Tex-129-E (1999), Tex-129-M, and Tex-620-M with RI values ranging from 6.8% to 7.6%. â¢ The RIs from the other test methods are higher, ranging between 9.1% and 13.2%. Results from ASTM WK 24621, with an RI of 13.2%, have the poorest repeatability compared to the other test methods for resistivity. â¢ Results from tests performed on leachate extracted from a soil water mixture (Tex-620-J (2005), Tex-620-M and SCT 143 (2008)) have repeatability comparable to what is achieved from the soil box tests (Tex-129-M, Tex-129-E (1999), ASTM G-187 (2018), AASHTO T-288 (2016), and ASTM WK 24621). â¢ Results obtained from the Texas modified procedures for measurement of resistivity/conductivity (Tex-129-M and Tex-620-M) have better repeatability compared to the results obtained from AASHTO T-288 (2016), SCT 143 (2008), ASTM G-187 (2018), and ASTM WK 24621. In the following section we compare resistivity measurements from alternative test procedures to the AASHTO tests and identify data trends. 22.214.171.124 Comparison of Different Resistivity Tests with AASHTO T-288 (2016) We have plotted resistivity test results from Tex-129-E (1999), ASTM G-187 (2018), Tex-129-M, and ASTM WK 24621 against the resistivities obtained from the AASHTO T-288 standard (2016) in Figure 3-3. In general, the data appear to be positivity correlated; meaning materials with relatively higher resistivity values via AASHTO T-288 (2016) also showed high resistivity values via the alternative test procedures. An important observation is that the resistivities from Tex-129- E (1999) and ASTM G-187 (2018) are more strongly correlated to those obtained from AASHTO T-288 (2016) (Figure 3-3 (a) and Figure 3-3 (b)) compared to the resistivities from Tex-129-M and ASTM WK 24621 (Figure 3-3 (c) and Figure 3-3 (d)).
25 (a) Tex-129-E vs. AASHTO T-288 (b) ASTM G-187 vs. AASHTO T-288 (c) Tex-129-M vs. AASHTO T-288 (d) ASTM WK 24621 vs. AASHTO T-288 Figure 3-3 Comparisons of soil box test results relative to AASHTO T-288. We normalized data from alternative test methods with respect to results obtained from the same samples tested via AASHTO T-288 (2016). We define the ratio between results from an alternative test method to that from AASHTO T-288 (2016) as the bias for the alternative test method. In Figure 3-4 we summarize the bias statistics from the alternative test procedures, where the bars represent the mean bias (Âµbias) in Figure 3-4 (a), and the coefficient of variation of the bias (COVbias) in Figure 3-4 (b). The whiskers in Figure 3-4 (a) represent the standard deviations of the biases (ÏBias). (a) bias means and standard deviations 0 5000 10000 15000 20000 0 5000 10000 15000 20000 Te x- 12 9- E ( â¦ -c m ) AASHTO T-288 (â¦-cm) Line of equal values 0 5000 10000 15000 20000 0 5000 10000 15000 20000 AS TM G 18 7 (â¦ -c m ) AASHTO T-288 (â¦âcm) 0 10000 20000 30000 40000 50000 0 10000 20000 30000 40000 50000 Te x- 12 9- M (â¦ -c m ) AASHTO T-288 (â¦âcm) 0 10000 20000 30000 40000 50000 0 10000 20000 30000 40000 50000 AS TM W K2 42 61 ( â¦ -c m ) AASHTO T-288 (â¦âcm)
26 (b) coefficients of variation (COV = Ïbias Î¼bias ) Figure 3-4 Statistics of resistivity test bias with respect to AASHTO T-288 (2016). We make the following general conclusions based on the presentation of data in Figure 3-4: â¢ The mean bias is approximately one for Tex-129-E (1999) with a COV of 22%. These statistics are manifested in the relatively narrow band and proximity to the line of equal values depicted in Figure 3-3 (a). We expect results from Tex-129-E (1999) and AASHTO T-288 (2016) to be close because of the similarities between these test methods. The two tests differ in terms of the sieve size used to separate the specimen from the sample (No. 8 vs. No. 10) and the 12-hour curing period prescribed by AASHTO T-288 (2016) for the first moisture increment. For the materials tested in this study, these differences did not have a significant impact on the results. Other test procedures show mean bias values that are noticeably higher than 1.00 (as high as 5.22 in Tex-620-M) with COVs generally higher than 50%. â¢ The mean bias is greater for test procedures that involve coarser gradations (i.e., ASTM G- 187 (2018), Tex-129-M, and ASTM WK 24621). ASTM G-187 (2018) includes particle sizes up to 1/4â, but Tex-129-M and ASTM WK 24621 both include particle sizes up to 1 3/4â. This is reflected in the mean bias values, which are higher for results obtained from ASTM WK 24621 and Tex-129-M compared to those from ASTM G-187 (2018). The bias from ASTM WK 24621 is higher than that from Tex-129-M due to the manner in which measurements are taken after the sample is drained for ASTM WK 24621. â¢ The mean bias for the tests on compacted soil specimens and tests performed with leachates are 2.13 and 2.95, respectively. We expect differences between results obtained with these techniques because we cannot include the effects from tortuosity using conductivity measurements from leachate. â¢ Biases from tests on leachate are all greater than one, even for samples that are separated into finer components (e.g., for Tex-620-J (2005) the sample is separated on a No. 40 sieve). This is due to the different dilution ratios, and methods of mixing and extracting leachates used in different leaching tests compared to soil box tests.
27 We grouped the data from each test method according to the fineness of the samples (fine sand, coarse sand, and gravel), as previously described in Figure 3-1. We summarized these data in Figure 3-5 including the mean (Figure 3-5 (a)) and coefficient of variation (Figure 3-5 (b)) of the bias observed from all samples included in each fineness group. We could not perform SCT 143 (2008), with fine samples due to the lack of settlement of the finer particles during the specified standing time. We conclude that, as the coarseness of the sample increases, the mean bias and the COV increase. Considering the materials characterized as fine sand, and results from soil-box tests, the average bias is close to one with a relatively low COV (average COV = 8%). On the other hand, the biases for coarse sand and gravel are 1.6 and 3.1, respectively, considering results from Tex-129-M which includes coarse particles within the test specimen. Also, the COVbias increases incrementally for materials characterized as coarse sands and gravels, where COVs in excess of 30% are observed. The means of the biases of the results from leaching tests performed on fine samples are higher than one, due to the manner in which samples are diluted for leaching, compared to the moisture contents that prevail for compacted, saturated samples. For compacted samples, the moisture contents are generally less than 50% by weight, but dilution ratios as high as 10:1 (water: solids) are commonly used for leaching tests. The bias from Tex-620-M is more than twice the bias from Tex-129-M. Since the same specimen gradation is included in the both tests, the observed differences are due to the manner in which leachate is prepared and tested for Tex-620-M compared to tests conducted on compacted specimens (Tex-129-M). The mean biases of the test results from Tex-620-J (2005) do not trend with respect to coarseness of the sample. This is because the sample is separated on a No. 40 sieve, and only the finer portion is included in the test specimen. (a) mean bias
28 (b) coefficients of variation Figure 3-5 Resistivity measurements from samples with different textures. We identified trends within the data to reconcile the variability (high COVs) of the bias from testing coarse sand and gravel materials. These trends distinguish parameters that influence the resistivity of geomaterials and describe how parameters correlate with the measured values. The bias of Tex-129-M and ASTM WK 24621 with respect to AASHTO T-288 (2016) were investigated in this study using a model developed at The University of Texas at El Paso (Arciniega et al., 2018; 2019). The model shows how resistivity of soil can be described in terms of sample gradations and salt ion concentrations. We derived non-dimensional scaling parameters from this model that show how results from different resistivity test procedures may be compared and correlated to one another (the model and the bias statistics are presented in Appendix B). We reviewed these correlations to identify trends in the data that are then related to easily observed material characteristics as shown in Table 3-2. Table 3-2 Resistivity test bias correspondence with GN and PP#10. Sample Types Bias < 1.5 1.5 < Bias < 3.0 Bias > 3 GN PP#10 GN PP#10 GN PP#10 Gravel - - 2.0 - 3.0 6 - 40 3.0 - 3.6 24 - 40 Coarse Sand 4.5 - 4.8 60 - 70 3.9 - 4.5 50 - 60 - - Fine Sand 5.0 - 6.7 > 80 - - - - We draw the following conclusions based on the information presented in Table 3-2: â¢ If the sample has more than 60 percent passing a No. 10 sieve, then bias is close to one, and results from testing in accordance with Tex-129-M and AASHTO T-288 (2016) are similar. â¢ When the grading number of the sample is greater than 3 and there is less than 40 percent passing the No. 10 sieve, the bias in resistance measurements is greater than 3 (higher bias).
29 Thus, a relatively large difference in results is obtained with Tex-129-M compared to AASHTO T-288 (2016) from materials with these characteristics. In general, these can be described as gravels with significant amounts of coarse and fine sand, where the percent gravel is approximately 50% and the coarse sand component is approximately 30%. Tex-129-M appears to be a good alternative to AASHTO T-288 (2016) to evaluate the effect of the coarseness and gradation of the sample on measurements of resistivity. The test procedure is like AASHTO T-288 (2016) and considers how moisture content and degree of saturation affects the resistivity of a compacted sample. 3.3.2 Salt Contents In Figure 3-6, we summarize the precisions of salt content measurements observed from testing replicates. The samples and replicates are the same as those described for evaluating the precision of resistivity tests and identified in Section 3.3.1. The bar graphs and whiskers in Figure 3-6 represent ÂµCOV and Ïcov, respectively. We have included measurements of salt contents via AASHTO T-290 (2016), AASHTO T-291 (2013), Tex-620-J (2005), and Tex-620-M. A common measurement technique (Ion Chromatography) was applied for all test standards, such that the comparisons presented herein depict differences due to sample preparations including dilution ratios and methods of mixing. For the comparison between results from different test methods described here, we measured chloride and sulfate ion concentrations via the IC method for all of the test procedures. The ASTM D4327 (2017) standard, is employed to simultaneously measure chloride and sulfate concentrations as well as other anions (e.g., bicarbonate anion) using the ion-exchange chromatograph. Ion chromatography is a more accurate and reproducible technique for measuring salt concentrations compared to traditional methods (e.g., titrations, photo-spectrometry). The IC method is more automated, less expensive, and indicates potential interferences, which are not identified by the current AASHTO tests. However, sample treatments described in AASHTO T- 290 and T-291 are needed to prepare an extract for IC measurements. We only included samples with salt contents greater than 10 mg/kg (ppm) for computations of the COV statistics (Âµcov and Ïcov). These include two samples from South Carolina (LWF and GB), one sample from Rochester, NY, and one sample from El Paso, TX. Measurements less than 10 mg/kg are not reliable because the resolution of the measurement device is large compared to the measurements. We make the following observations based on the COV statistics depicted in Figure 3-6: â¢ Differences between precision and repeatability among the tests are more distinct between the tests for chloride compared to those from measurements of sulfate. â¢ For sulfate measurements, AASHTO T-290 (2016) rendered repeatability equal to or better than the other test methods that were evaluated. â¢ Measurements of chloride from Tex-620-M are less repeatable compared to other methods.
30 Figure 3-6 Tests for measurements of salt content, and observations of precision (only included results from testing sulfate and chloride with > 10 mg/kg, n=4). Salt contents measured via Tex-620-J (2005) were high compared to those from Tex-620-M and the AASHTO tests because of the method used for the sample preparation, which includes pulverizing the sample to pass a No. 40 sieve and heating to 150Â°F before extracting the leachate. Tex-620-J (2005) was originally used by TxDOT for measurements of chloride contents in concrete samples. Although TxDOT has applied this test for evaluating fills for MSE walls, it does not appear to be applicable. Therefore, data from Tex-620-J (2005) will not be included in the forthcoming comparisons. We will discuss and compare results obtained from the AASHTO (AASHTO T-290 (2016) (sulfate) and T-291 (2013) (chloride)) and Texas modified procedures (Tex-620-M) in what follows. We tested 26 samples, in which 21 samples were tested via AASHTO and Texas modified procedures and five samples were only tested via Tex-620-M. The five coarse samples were not tested via AASHTO tests because of the lack of sufficient constituents passing the No. 10 sieve. We compare salt contents obtained from Tex-620-M and AASHTO tests in Figure 3-7. Except for samples with a high content of particles passing a No. 10 sieve, the Tex-620-M procedure renders lower salt contents compared to AASHTO tests. This is due to the larger particle sizes included in Tex-620-M compared to the AASHTO tests that are performed on the finer fraction of the sample (passing a No. 10 sieve). The black dotted line, which shows the best fit for sulfate measurements by Tex-620-M (R2 = 0.79), indicates that sulfate content measured by Tex-620-M are approximately 70 percent of those measured by AASHTO tests. Similarly, chloride contents measured by Tex-620-M (R2 = 0.76) are approximately 50 percent of those measured by AASHTO tests. For measurements of low salt content (â¤ 10 mg/kg), results from Tex-620-M agree well with those from AASHTO tests. There are also a few measurements at higher salt contents where the results from the three tests are approximately the same. There are four observations where sulfate contents measured via Tex-620-M are significantly lower than those measured by AASHTO T-290 (2016) (about one-fifth). 10.1 10.7 11.8 3.7 7.5 12.9 0 2 4 6 8 10 12 14 16 18 20 AASHTO T-290 Tex-620-M Tex-620-J Tex-620-J AASHTO T-291 Tex-620-M Sulfate Content Chloride Content P re ci si on C O V (% )
31 Figure 3-7 Correlation between salt content measurements from Tex-620-M and AASHTO T- 290 & T-291. We computed bias as the ratio of âequivalent total salt contentâ obtained from Tex-620-M divided by the âequivalent total salt contentâ computed from the results of AAAHTO T-290 (2016) and T- 291 (2013). Equivalent total salt contents consider the combining power of chloride and sulfate in solution in terms of their milliequivalent units, and is useful to check trends between salt content and resistivity, as we describe in the next subsection. In general, lower salt contents are measured via the modified test procedures so the bias is less than one. The lowest values of bias are from samples that have a low percentage of particles passing a No. 10 sieve. We identified the trend in bias with respect to percent passing the No. 10 sieve for each sample as follows: â¢ The lowest biases are from samples with less than 25 percent passing a No. 10 sieve. â¢ For samples with more than 60 percent of the particles passing a No. 10 sieve, the bias is close to one. For these samples, the fines and fine sand components dominate the leaching of salts from samples tested via either the AASHTO or Texas modified procedures. â¢ Other factors, in addition to the percent passing the #10 sieve, that are related to the test technique may also affect the bias. Higher dilution ratios and different methods of mixing may render measurements of salt contents from Tex-620-M that are higher compared to AASHTO T-290 (2016) and T-291 (2013) even for samples with a large fraction passing a No. 10 sieve. 126.96.36.199 Correlations between Salt Contents and Resistivity Salts affect the electrical resistivity of an aqueous solution because salts dissociate into their components (ions) when dissolved in water and create an electrically conductive solution. The resistivity decreases as the solution becomes more concentrated with ions (higher ion mobility). 620-M = 0.71(T-290) RÂ² = 0.79 620-M = 0.51(T-291) RÂ² = 0.76 0 200 400 600 800 1000 0 200 400 600 800 1000 Te x- 62 0- M ( m g/ kg ) AASHTO T-290 & 291 (mg/kg) Chloride Sulfate Line of equal values
32 Thus, measurements of resistivity are negatively correlated with the measurements of salt content. We evaluated correlations between salt content and resistivity measurements to assess the veracity of these measurements. If the results do not show a consistent trend between the salt contents and resistivity, then the tests used for measurements of salt contents or electrical resistivity may not provide a true measurement. This may also be related to the unknown presence of other ions, besides chloride or sulfate ions, that affect the measured resistivity. We paired resistivity measurements with the measurements of salt contents. Data pairings between resistivities and salt contents include the AASHTO test series (AASHTO T-288 (2016) for resistivity and T-290 (2016) and T-291 (2013) for measurements of salt contents), the current TxDOT test procedures (Tex-129-E (1999) and Tex-620-J (2005)), and the proposed TxDOT modified test procedures (Tex-129-M and Tex-620-M). ASTM tests for measurements of resistivity, including ASTM G-187 (2018) and ASTM WK 24621, are paired with salt contents measured via AASHTO T-290 (2016)/T-291 (2013) and Tex-620-M, respectively. In this manner, test results from measurements of resistivity and salt concentrations are performed on specimens that have been separated from the sample into similar particle sizes before testing. For Tex-620- M, conductivity/resistivity and salt content measurements were performed on the same specimen. This is unique compared to measurements of resistivity from compacted soil specimens paired with salt contents measured from leachate. We performed regression analysis to assess the coefficients of correlation (R2) between the resistivity and salt content measurements (mg/kg). A power law, as shown in the Equation (3-3), was found to provide the best fit to the data. For Equation 3, chloride and sulfate ion contents were combined to render equivalent salt contents in terms of mg/kg as described in Appendix B. Ï (â¦ â cm) = A( mg kg )âB (3-3) We summarized the model parameters and coefficients of correlation for each of the pairings in Table 3-3. The highest coefficient of correlation (R2 = 0.79) is achieved comparing the results from salt content and conductivity measurements using the Tex-620-M procedure. The coefficients of correlation from measurements of resistivity on compacted soil specimens are lower and range from 0.36 to 0.64. The best correlations from resistivity measurements observed on compacted soil specimens are with the AASHTO test series (R2 = 0.64), and the worst are from testing with ASTM WK 24621 and Tex-620-M (R2 = 0.36).
33 Table 3-3 Resistivity model parameters (ppm of salts). Test procedures A B R2 Leachate Tex-620-M conductivity and salt 122,000 0.53 0.79 Soil box AASHTO T-288/T-290/T-291 27,000 0.47 0.64 ASTM G-187 and AASHTO T-290/T-291 22,000 0.35 0.50 Tex-129-E and Tex-620-J 48,000 0.49 0.52 Tex-129-M and Tex-620-M 41,000 0.47 0.43 ASTM WK 24621 and Tex-620-M 57,000 0.44 0.36 Subsequently, AASHTO test procedures were applied to samples with at least 22 percent passing a No. 10 sieve, and the Texas modified procedures to samples with less than 22 percent passing a No. 10 sieve. This resulted in improved correlations between resistivity and salt contents. We performed similar analyses using milliequivalent units to express the salt contents (mEq instead of ppm). We also included measurements of alkalinity in terms of part per million of CaCO3 for the calculation of mEq units, in which the carbonate ions are considered as another salt component in the sample mixtures. Alkalinity is commonly determined as the capacity of water to buffer acids (acid-neutralizing capacity of water), where the major acid buffer constituents in water are bicarbonate (HCO3-) and carbonate (CO32-) ions. Table 3-4 shows the correlations obtained using mEq units. By comparing the determination coefficients in Table 3-3 and Table 3-4, utilizing mEq units results in significant improvements in the correlations. If we consider data parings from Tex-129-M and Tex-620-M, R2 increases from 0.43 to 0.59; and for measurements of both conductivity and salt via Tex-620-M, R2 increases from 0.79 to 0.88. Table 3-4 Resistivity model parameters (mEq of chloride, sulfate and alkalinity). Test procedures A B R2 Leachate Tex-620-M conductivity and salt 142,105 -1.13 0.88 Soil box AASHTO T-288/T-290/T-291 13,500 -0.82 0.64 Tex-129-M and Tex-620-M 57,000 -1.12 0.59 We draw the following conclusions from the results presented in this section: â¢ Precision/repeatability are similar between test methods for measurements of salt contents. â¢ Higher salt contents are measured via Tex-620-J (2005) compared to the AASHTO tests; salt contents from Tex-620-M are generally lower than others. â¢ The best correlations between salt content and resistivity measured on a compacted specimen are obtained from the AASHTO test standards. â¢ Milliequivalent units are the best way to express salt contents and to allow consideration of the effects from other salts besides chloride and sulfate on the resistivity measurements.
34 3.3.3 Measurements of pH We have summarized the statistics describing the precision observed from testing replicates and the bias of each test procedure with respect to AASHTO T-289 (2018) in Figure 3-8. Precision is observed from testing replicates from the same nine sources as those described for resistivity testing and measurements of salt contents. The test procedures for measuring pH vary in terms of (1) whether or not the sample is air-dried, (2) the particle sizes included in the test specimen, (3) the dilution ratios used in the sample preparation, (4) methods of mixing water with the sample including whether or not the mixture is heated, and (5) the period that the soil-water mixture is allowed to stand before making the first measurement. In Figure 3-8, the bar graphs and whiskers represent means and standard deviations, respectively; the means and standard deviations are in pH units. The pH values measured from all samples range between 7.6 and 9.5. The mean of the measurements from AASHTO T-289 (2018) is 8.34, which means that a COV of 1% corresponds to an average standard deviation difference of approximately 0.08 pH units. Higher pH values are measured via Tex-620-M with a mean pH value of 8.95. Based on the data shown in Figure 3-8, the repeatability of the different measurement techniques is similar between the test procedures described by NCHRP 21-06 (2009), Tex-620-J (2005), and Tex-128-E (1999). Tex-620-J (2005) and Tex-128-E (1999) are similar in terms of the particle sizes included in the test specimens, the use of higher dilution ratios, and the heat applied during the mixing procedure. Application of heat appears to improve the repeatability of the test results. However, there are significant differences between these procedures and the NCHRP 21-06 (2009) procedure. In NCHRP 21-06 (2009), heat is not applied during mixing, and this is the only procedure whereby the sample is not air-dried as a part of sample preparations. Including moisture, which has been a part of the mixture over time, may result in more consistent extractions and corresponding measurements of pH from NCHRP 21-06 (2009). ASTM D 4972 (2019) and AASHTO T-289 (2018) have lower, but similar repeatability. These procedures are similar except for the methods of mixing. However, the two procedures differ from previously discussed methods in terms of dilution ratios (1:1) and mixing the sample without the application of heat. The poorest repeatability is observed from measurements of pH obtained via Tex-620-M, which incorporates gravel sized particles within the test specimen. Figure 3-8(b) shows that, except for Tex-620-M, the bias of results from all of the test methods with respect to data from AASHTO T 289 are close to one. The biases of the results from Tex- 620-M were observed to increase with respect to sample coarseness; i.e., the bias from testing a gravel sample is higher compared to that from a sample which includes coarse or fine sands. We plotted results from measurements of pH in Figure 3-9, whereby pH values obtained from Tex- 620-J (2005), NCHRP 21-06 (2009), ASTM D 4972 (2019), Tex-128-E (1999), and Tex-620-M (vertical axes) are compared to those from AASHTO T-289 (2018) (horizontal axes). From Figure 3-9 we observe that the pH values measured by Tex-620-J (2005) and NCHRP 21-06 (2009) are lower, measurements from ASTM D 4972 (2019) are nearly equal, and measurements from Tex- 128-E (1999) and Tex-620-M are higher compared to measurements from AASHTO T-289 (2018). Other methods render a stronger correlation with results from AASHTO T-289 (2018) (R2> 0.6), compared to the correlation with Tex-620-M (R2 = 0.33).
35 We draw the following conclusions from the results presented in this section: â¢ Measurements of pH from Tex-620-M are less repeatable compared to measurements from other test methods investigated in this study. â¢ In general, Tex-620-M renders pH values that are higher compared to those obtained from the other test methods investigated in the study. â¢ Results from NCHRP 21-06 (2009) are more repeatable compared to AASHTO T-289 (2018) and do not have a significant bias with respect to results obtained from AASHTO T-289 (2018). (a) precision (b) bias with respect to AASHTO T-289 (2018) Figure 3-8 Summary of the test results for measuring pH. . 1 0.7 0.9 0.9 1.1 1.2 0 0.5 1 1.5 2 2.5 3 3.5 AASHTO T-289 NCHRP 21-06 Tex-128-E Tex-620-J ASTM D 4972 Tex-620-M P re ci si on C O V (Âµ CO V ) 0.99 0.98 0.97 1.03 1.1 0 0.2 0.4 0.6 0.8 1 1.2 ASTM D 4972 NCHRP 21-06 Tex-620-J Tex-128-E Tex-620-M B ia s w. r.t . A A S H TO T -2 89 (Âµ )
36 (a) AASHTO T-289 vs. Tex-620-J (b) AASHTO T-289 vs. NCHRP 21-06 (c) AASHTO T-289 vs. ASTM D 4792 (d) AASHTO T-289 vs. Tex-128-E (e) AASHTO T-289 vs. Tex-620-M Figure 3-9 Comparisons of pH measurements relative to AASHTO T-289 (2018). 3.4. Characterization of Corrosion Potential and Correlations with Corrosion Rates In the previous sections, we compared results from testing samples in accordance with current AASHTO tests and other alternative procedures for preparing specimens and making measurements. Given the better precision observed from results with the Tex-129-M test and its similarities with the AASHTO T-288 test procedure, we considered Tex-129-M as an alternative to AASHTO T-288 for testing coarse materials. Based on the distribution of bias in the results we made the following observations: 5 6 7 8 9 10 5 6 7 8 9 10 Te x- 62 0- J AASHTO T-289 620-J = 0.96 (T-289) R2 = 0.48 Line of equal values 21-06 = 0.98 (T-289) 5 6 7 8 9 10 5 6 7 8 9 10 N C H R P 2 1- 06 AASHTO T-289 R2 = 0.70 D 4972 = 0.99 (T-289) 5 6 7 8 9 10 5 6 7 8 9 10 A S TM D 4 97 2 AASHTO T-289 R2 = 0.69 128-E = 1.03 (T-289) 5 6 7 8 9 10 5 6 7 8 9 10 Te x- 12 8- E AASHTO T-289 R2 = 0.63 5 6 7 8 9 10 5 6 7 8 9 10 Te x- 62 0- M AASHTO T-289 620-M =1.08 (T-289) R2 = 0.33
37 â¢ When more than 60% of the sample is passing a No.10 sieve, similar results are obtained from measurements of resistivity and salt content via current test standards or the modified Texas procedures (Tex-129-M for resistivity and Tex-620-M for salt contents). â¢ Significant differences in measurements of resistivity and salt content were observed for materials having less than approximately 25% passing a No.10 sieve. â¢ The largest differences were observed from testing sandy gravels with 3.0 < GN < 4.0 and less than 40 % passing a No.10 sieve. 3.4.1 Correlation between Resistivity and Performance Data Up to this point, we have focused on identifying differences between test results and the factors affecting these differences. The next step is to determine whether and when measurements that are different from AASHTO T-288 (2016) may render a better result. We modeled performance by relating observations of performance to site conditions, and identifying parameters that provide the best correlations with performance data. In this case, performance is in terms of the durability of earth reinforcements as quantified by observations of metal losses and corrosion rates (CR). Site conditions include the environment surrounding the earth reinforcements, most notably resistivity of the fills or native soils. We used the coefficient of determination, R2, between the corrosion rate and resistivity measurements as an index to rank the accuracy of the results from each of the resistivity tests that were included in the test program. Resistivity is often considered to be an indicator of corrosivity as this single parameter is correlated with numerous factors that affect corrosion reactions, including salt and moisture contents (King 1977; Romanoff 1957). The data set for the regression analysis includes measurements from 19 sample sources incorporating 28 measurements of corrosion rates. Observed corrosion rates include 18 data points from galvanized steel specimens and 10 data points from plain steel specimens. Measurements presented herein are the maximum observed from each site/source. We use the maximums to consider the durability of the most vulnerable elements. The data set includes in-situ corrosion rate measurements from the field and corrosion rates measured from laboratory tests. In-situ measurements of corrosion rates from field studies involved variable moisture contents and corrosion rate measurements from locations near the tops and bases of the MSE walls. We used the linear polarization resistance (LPR) technique (Jones 1996; Tait 1994) for in-situ measurements of corrosion rates. As many as 30 samples were monitored at a given site and the maximum values were observed from sample locations where conditions for corrosion were more severe (e.g., higher moisture contents, cycles of wetting and drying, availability of oxygen). Laboratory tests included samples embedded within fills under the most severe conditions that may be encountered in the field. Moisture contents were maintained near optimum moisture contents for compaction, as well as saturated conditions. The observed corrosion rates are considered to be extremes/maximums compared to what is likely to occur in the field. In general, corrosion rates tend to attenuate with respect to time when conditions favor the development of a protective scale on the steel surface, however the majority of the attenuation occurs within the first year (Romanoff 1957). Corrosion rate measurements presented herein are from samples that have been embedded in fill for at least one year (i.e., with relatively stabilized corrosion rates). Considering each of the resistivity test methods, we plotted measurements of
38 corrosion rates versus measurements of resistivity for plain steel and galvanized elements separately. We obtained the best fit to the data using the power law shown in Equation (3-4). Table 3-5 summarizes the regression coefficients (A and B) and the coefficient of determination (R2) obtained from each regression analysis. We will discuss these data by grouping them according to the gradation of the specimens prepared for testing. ð¶ð¶ð¶ð¶ ï¿½ ðððð ð¦ð¦ð¦ð¦ ï¿½ = ð´ð´ Ã ðð(Î© â ðððð)âðµðµ (3-4) Group I includes results from AASHTO T-288 (2016), Tex-129-E (1999) and ASTM G-187 (2018). For Group I, samples were separated and the finer portion (passing a No.8, No.10 or Â¼ inch sieve) was included in the specimen for measurement of resistivity. Group II included tests whereby the specimen was more representative of the source, including particle sizes up to 1-3/4â (Tex-129-M, ASTM WK 24621, Tex-620-M). We observed that Group I tests render better correlations for galvanized steel and Group II tests render better correlations for plain steel specimens. This may be because galvanized surfaces are more uniform compared to the surfaces of plain steel specimens. Correlations between resistivity and corrosion rates are affected by the variability of the soil samples and the specimens included in Group II have more variability compared to Group I. The characteristics of the metal along the surface of plain steel are more variable and compared to those with galvanized steel the correlations are less affected by the uniformity of the finer specimens included in Group I. The correlation with respect to the performances of galvanized and plain steels are discussed in the following subsections. Table 3-5 Regression of observed corrosion rates and resistivity measurements. Test Method Galvanized steel plain Steel A B R2 A B R2 Group 1 AASHTO T-288 9,945 0.93 0.46 9,073 0.90 0.20 Tex-129-E 12,380 0.95 0.38 1,492 0.70 0.08 ASTM G-187 24,613 1.00 0.40 386,844 1.30 0.16 Group 2 Tex-129-M 1,102 0.67 0.19 55,542 1.05 0.31 ASTM WK 24621 9,169 0.88 0.33 88,746 1.08 0.27 Tex-620-M 140,664 1.11 0.32 467,000 1.16 0.30 188.8.131.52 Performance of Galvanized Steel For Group I, corrosion rates from galvanized elements are negatively correlated with resistivity (0.38 < R2 < 0.46). The regression coefficient, B, is very similar between the different test methods ranging from 0.90 to 1.3. The A coefficient is more than twice as high for ASTM G-187 (2018) compared to those from AASHTO T-288 (2016) or Tex-129-E (1999) (i.e., results from ASTM G-187 correlate to higher corrosion rates). This is directly related to the bias of the resistivity measurements from ASTM G-187 (2018) compared to AASHTO T-288 (2016).
39 For Group II, corrosion rates do not correlate as well with corrosion rate measurements compared to the correlations obtained from Group I. These correlations can be described as low to moderate (0.19 < R2 < 0.33). The lower degrees of correlation are because the tests in Group I, and the AASHTO T-288 test in particular, are suited to a broader range of materials compared to the range of materials for which results from Tex-129-M and other tests in Group II are applicable. Better correlations are obtained by culling the data set to only include materials that have more than 22 percent passing a No.10 sieve in the test results from AASHTO T-288, and only include those with less than 22 percent passing in the test results from Tex-129-M. The selection of a threshold of 22 percent is consistent with the observation that trends between the salt contents and resistivity measurements are more prevalent when materials are grouped based on whether or not there are more than 22 percent passing a No.10 sieve, as discussed in Section 3.3.2 and the high bias values that are observed from measurements of resistivity and salt contents when the percent passing a #10 is less than 22% as discussed in Sections 184.108.40.206 and 3.3.2. In the proposed protocol, in Section 3.5 and Appendix A, the threshold of 22% is rounded up to 25%. There are seven samples within the Phase II test program with less than 22 percent passing a No.10 sieve and corresponding measurements of corrosion rate. These include samples from Wake Forest, NC; San Antonio Texas; Bastrop Texas; Maple Road, Amherst; New York; Waco, Texas; Garden City Texas; and samples of coarse aggregate from an MSE wall in El Paso Texas. We show the correlations corresponding to AASHTO T-288 (2016) and Tex-129-M in Figure 3-10 and Figure 3-11, respectively. Figure 3-10 Galvanized steel corrosion rates and resistivity measurements from samples with more than 22% passing the No.10 sieve (via AASHTO T-288 (2016)). CR = (2877)Ï-0.73 RÂ² = 0.50 0 5 10 15 20 25 30 35 40 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 C or ro si on ra te (Âµ m /y r) Resistivity (Î©-cm)
40 Figure 3-11 Galvanized steel corrosion rates and measurements of resistivity from samples with less than 22% passing the No.10 sieve (via Tex-129-M). Figure 3-10 depicts data from AASHTO T-288 (2016) not including the coarse samples. The data collected for the lightweight fill (LWF, which was primarily expanded clay) from South Carolina was also removed. LWF samples were considered different from natural materials due to their absorption and chemical compositions. The regression showed some improvement, R2 = 0.50, compared to the case where all of the samples are included, R2 = 0.46. The regression coefficients were changed (A= 2733 and B = 0.73) such that the computed corrosion rates were different. We observed that corrosion rate measurements became more disperse with decreasing resistivity, which caused the regression to decrease. Figure 3-11 depicts data from application of Tex-129-M to samples with less than 22% passing a No.10 sieve. Compared to Figure 3-10 and the resistivity measurements from AASHTO T 288 (with > 22% passing #10), lower corrosion rates are depicted in Figure 3-11 for all levels of resistivity measured via Tex-129-M (with < 22% passing #10). Corrosion rate measurements from Waco Texas, and an MSE wall in El Paso Texas with coarse fill are very low (<< 1 Âµm/yr). The low measurements of corrosion rates are likely due to the fact that these were field measurements from sites that were very dry (desert locations) at the time of measurements. Thus, the resistivity measurements obtained from samples that are saturated do not apply very well to these data. More data including measurements of corrosion rates and resistivity from sites located throughout North America and Europe are available from a database catalogued as part of NCHRP 24-28 (Fishman and Withiam, 2011). These data include the maximum corrosion rates observed from each site. However, the resistivity measurements from Tex-129-M are not available for this data set. We culled these data such that coarse samples with less than 22 percent passing a No.10 sieve were removed from the data set. The culled data are presented in Figure 3-12 with 36 data points
41 including 10 data points coincident with the samples included in the Phase II laboratory testing for this study (NCHRP 21-11). Figure 3-12 Galvanized steel corrosion rates and measurements of resistivity from worldwide data. Testing with AASHTO T-288 (2016) from samples with more than 22% passing the No.10 sieve. Regression analysis using the data from Figure 3-12 showed similar regression coefficients and correlations compared to the data presented in Figure 3-10 that only includes data collected from Phase II of this study. The regression from the broader database renders A = 5267 compared to A = 2733; B = 0.84 compared to B = 0.73, and R2 = 0.62 compared to R2 = 0.50. We used these sets of coefficients with Equation (3-4) and resistivity between 200 â¦-cm and 50,000 â¦-cm as input. Differences in computed corrosion rates were within 3 Âµm/yr. for computed corrosion rates in excess of 15 Âµm/yr. and the difference decreases to 0.5 Âµm/yr. for computed corrosion rates of approximately 1 Âµm/yr. These similarities indicate that the model is robust and fits well to the data that were not included in the set initially used to determine regression coefficients. This provides confidence that the model obtained from regression analysis is not limited to describing those data collected in the Phase II of this project, and has a broader application. 220.127.116.11 Performance of Plain Steel We consider the correlations between test results from Group I and corrosion rates measured on plain steel specimens to be low (0.08 < R2< 0.20). This may be due to the paucity of corrosion rate measurements available from plain steel specimens placed within MSE wall fill. The correlation using results from Tex-129-M is better (R2 =0.31), and considered moderately correlated (because, 0.25 < R2 < 0.49). Figure 3-13 depicts data from correlating results of AASHTO T-288 to measurements of corrosion rate on plain steel specimens for samples with more than 22% passing the No.10 sieve. We achieved a good correlation (R2 = 0.4) with outliers removed, whereby higher corrosion rates are CR = (5267)Ï-0.84 RÂ² = 0.62 0 5 10 15 20 25 30 35 40 0 5000 10000 15000 20000 25000 30000 35000 40000 45000 C or ro si on ra te (m m /y r) Resistivity (â¦-cm)
42 observed (Prince George, BC Canada and M-U-D, NY). The correlation coefficients from this regression are A = 1470 and B = 0.68. There are only two data points with corrosion rate measurements and resistivity measured via Tex- 129-M for materials with less than 22% passing the No.10 sieve. For these two points, the higher corrosion rate corresponds to the lower measurement of resistivity. Figure 3-13 Plain steel corrosion rates and measurements of resistivity from AASHTO T-288 (2016) and samples with more than 22% passing the No.10 sieve. We conclude that results from Tex-129-M apply well to materials with less than approximately 22% passing the No.10 sieve. For materials with more than 22 percent passing a No.10 sieve, AASHTO T-288 is appropriate for measurement of resistivity. We used these observations to develop the proposed protocol discussed in Section 3.5 and presented in Appendix A. 3.4.2 Classification of Soil Corrosivity 18.104.22.168 Characterization Scheme Proper characterization of corrosion potential needs to consider the nature and physical characteristics of the material, its electrochemical properties, and various factors related to the site conditions. Characterizations of corrosion potential may be done by setting threshold limits for individual electrochemical parameters (e.g., electrical resistivity, pH, sulfate, and chloride content) similar to AASHTO, or may involve ranking according to a multivariate approach. A number of schemes exist for screening and characterizing corrosion potential of earthen materials. These schemes are often developed for specific applications (e.g., MSE walls, piles, culverts, and pipelines) that may involve aspects of the installations, site conditions, and electrochemical properties. Some of the most common schemes that use a multivariate approach are summarized below: CR = (1470)Ï-0.68 RÂ² = 0.39 0 5 10 15 20 25 30 35 40 45 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 C or ro si on ra te (Âµ m /y r) Resistivity (Î©-cm)
43 â¢ German Gas and Water Works Engineersâ Association Standard (DVGW GW9), which is one of the earliest corrosion assessment methods applied to pipeline construction in Europe (Shreir et al. 1994). â¢ Eyre and Lewis (1987), modified the German scheme, which was then adopted in a slightly revised form by the UK Highways Agency in their Design Manual for Roads and Bridges (2000). However, this revised scheme does not consider the beneficial effect from the presence of carbonates on corrosivity, which was originally included in DVGM GW9. â¢ Jones (1985) developed a method for characterizing the corrosivity of soils considering a number of variables related to soil type, site conditions, and electrochemical properties. â¢ The standard method used in Great Britain (GB) to assess the design life requirements of buried galvanized steel structures; culverts in particular, is based on a multi-variate classification system which rates different environments in contact with the structure according to their corrosivity (Brady and McMahon, 1994). The classification scheme used in GB considers characteristics of the soil including mechanical properties described in terms of particle size, and plasticity; and electrochemical parameters including resistivity, pH, and the presence of sulfate, chloride and sulfide. In this study we focus on the application of AASHTO criteria to characterize corrosion potential (presented in Table 1-1) and the DVGW GW9, which considers the protective effects associated with the presence of carbonates. Table 3-6 presents the DVGW GW9âs characterization scheme, in which a number of factors are involved in the corrosivity assessment including physical and electrochemical properties of the earthen material (soil), site conditions, ground water levels, and the presence of industrial fills. Points/marks are assigned for each factor and the marks are summed to calculate an overall score. This score is used to assess corrosivity, in which lower (more negative) scores indicate more severe corrosion conditions. The scheme considers the benefits (positive score) from the presence of carbonates on the corrosion behavior of buried metals. The sum of the points assigned to each category can range from a best of +4 to a worst case of -25. From this overall score and using Table 3-7 and Table 3-8, corrosivity and expected corrosion rates can be evaluated.
44 Table 3-6 Characterization scheme from DVGW GW9. Item Measured value Marks Soil composition Calcareous, marly limestone, sandy marl, not stratified sand +2 Loam, sandy loam (loam content 75% or less) marly loam, sandy clay soil (silt content 75% or less) 0 Clay, marly clay, humus -2 Peat, thick loam, marshy soil -4 Ground water level at buried position None 0 Exist -1 Vary -2 Resistivity > 10,000 â¦-cm 0 5,000 â 10,000 â¦-cm -1 2,300 â 5,000 â¦-cm -2 1,000 â 2,300 â¦-cm -3 < 1,000 â¦-cm -4 Moisture content < 20% 0 > 20% -1 pH > 6 0 < 6 -2 Table 3-6 Characterization scheme from DVGW GW9 (continued). Item Measured value Marks Sulfide and hydrogen sulfide None 0 Trace -2 Exist -4 Carbonate > 5% +2 1% - 5% +1 < 1% 0 Chloride < 100 mg/kg 0 > 100 mg/kg -1 Sulfate < 200 mg/kg 0 200 mg/kg â 500 mg/kg -1 500 mg/kg â 1,000 mg/kg -2 > 1,000 mg/kg -3 Cinder and coke None 0 Exist -4
45 Table 3-7 Soil corrosivity/aggressiveness (for carbon steel) DIN 50 929 Part 3. Total score Category Soil corrosivity Risk deep/wide pitting Risk general corrosion â¥ 0 Ia Virtually not corrosive Very low Very low 0 to -4 Ib Slightly corrosive Low Very low -5 to -10 II Corrosive Medium Low < -10 III Highly corrosive High Medium Table 3-8 Expected corrosion forms/rates (for carbon steel) DIN 50 929 Part 3. Total score Category General corrosion rate (Âµm/yr.) Range (Âµm/yr.) Localized (pitting) corrosion rate (Âµm/yr.) Range (Âµm/yr.) â¥ 0 Ia 5 2.5 â 10 30 15 â 60 0 to -4 Ib 10 5 â 20 60 30 â 120 -5 to -10 II 20 10 â 40 200 100 â 400 < -10 III 60 30 - 120 400 200 - 800 We computed corrosion indices using the DVGW GW9 scheme for all sample sources included in Phase II of the test program. We incorporated multiple electrochemical measurements from earth materials including pH, resistivity, and soluble chloride and sulfate ion contents to compute a corrosivity index. Correspondingly, we computed corrosivity indices with results from the AASHTO test series, current TXDOT test procedures, proposed modified TXDOT test procedures and data from ASTM test procedures. We also computed indices according to the following criteria for application of the appropriate electrochemical test methods. We selected the appropriate test standards depending on the character of the material under test, and based upon the percent passing the No.10 sieve. â¢ If the sample has more than 25 percent passing a N.10 sieve or a GN <3, then AASHTO T-288 (2016) applies; â¢ If the sample has less than 25% passing the #10 sieve, and GN greater than 3, then Tex- 129-M is applied. The GN is included with the screening to restrict use of Tex-129-M to coarse textured samples with a relatively high gravel content. We compared the characterizations of corrosivity with measurements of corrosion rate from galvanized and plain steel elements. In what follows, we will compare correlations with performance considering characterizations of corrosivity from DVGW GW9. Characterizations based solely on resistivity, as discussed in Section 3.4.1, are also included in the comparisons.
46 22.214.171.124 Correlation between Results of Characterization Scheme and Performance Data Formulas determined from regression analysis do not depict how corrosion rates vary within selected ranges of resistivity, or other material characteristics. Alternatively, data clusters are useful to quantify the variations and uncertainties associated with data within selected regions of a sample domain. A data cluster is a group of objects that are more similar to each other compared to those in other groups (clusters). In this context, clusters are in terms of similar material characteristics (e.g., gradation, maximum particle size, electrochemistry, or corrosivity index). We will identify distinct ranges of performance, as measured by corrosion rates that are associated with each data cluster. We are using cluster analysis to demonstrate the advantage of the proposed protocol over existing test standards for characterizing the steel corrosivity of earthen materials. In Table 3-9, we present data clusters that are arranged according to resistivity measured via AASHTO T-288, Tex-129-M or ASTM WK 24621 as prescribed in the last section. ASTM WK 24621 is applied to expanded clay, for which the particles are porous, and the 24-hour soaking period included in the ASTM WK 24621 procedure allows moisture to be absorbed before the test. This assures that all measurements are made with moisture occupying the pore spaces within the solid particles. We grouped resistivity measurements (Ï ) into three clusters as Ï > 10,000 â¦-cm, 3000 â¦-cm < Ï < 10,000 â¦-cm; and 1000 â¦-cm < Ï < 3000 â¦-cm. We observed distinctly different ranges of corrosion rate measurements within these defined clusters for galvanized and plain steel elements. Corresponding ranges of corrosion rates, that we show in Table 3-10 are similar to those described in DIN 50 929 Part 3 (Brady and McMahon, 1994) corresponding to noncorrosive, slightly corrosive, and corrosive conditions. Two exclusions are evident out of 28 measurements depicted in Table 3-9 (these exclusions are marked with red colored texts). In Table 3-11 we present data clusters that are arranged according to corrosivity index as determined from DVGW-GW9. We grouped the corrosivity indices (â(I)) into three clusters as â(I) â¥ 0, -3 â¤ â(I) < 0; -5 â¤ â(I) < -3. Table 3-12 describes the ranges of corrosion rates corresponding to each cluster. Compared to the clustering with resistivity measurements, these clusters result in a tighter range of observed performance within each cluster. We observed four exclusions that are marked in red in Table 3-11.
47 Table 3-9 Data clustering according to resistivity and observed rates of corrosion. Cluster Sample GN PP#10 Test method (proposed protocol) Ï (â¦-cm) CR (Âµm/yr.) Galv. Plain Ï > 10 ,0 00 â¦ -c m San Antonio, TX 0.18 2 Tex-129-M 42666 1.0 NAA Wake Forest, NC 2.21 8 Tex-129-M 31651 0.3 < 0.1 Bastrop, TX 0.15 2 Tex-129-M 24155 0.4 NA Ocala, FL 5.65 91 AASHTO T-288 16535 1.8 3.8 Ashdown, AR 2.88 36 AASHTO T-288 13958 1.8B NA 30 00 â¦ -c m < Ï < 10 ,0 00 â¦ - cm M-U-D, NY 5.24 82 AASHTO T-288 9064 4.8 39 LWF, South Carolina 4.83 68 ASTM WK 24621 7045 1.2 8.4 Maple Rd., NY 2.50 22 Tex-129-M 3817 3.7 16 TTC, NC 3.51 24 AASHTO T-288 5056 5.8 1.6 Waco, TX 1.26 7 Tex-129-M 4499 0.3 NA Prince George, BC 2.89 32 AASHTO T-288 4527 NA 20 Garden City, TX 2.52 22 Tex-129-M 3613 4.3 NA El Paso Coarse/MSE 0.22 2 Tex-129-M 3307 0.2 NA El Paso Fine/MSE 5.52 87 AASHTO T-288 3026 21C NA GB, South Carolina 4.48 56 AASHTO T-288 2486 3.2D 5.8 10 00 â¦ -c m < Ï < 30 00 â¦ -c m PIP, NY 4.62 61 AASHTO T-288 1872 37 30 Sprain Brook Pkwy, NY 2.54 27 AASHTO T-288 1720 33 NA Quarry; El Paso, TX 3.64 41 AASHTO T-288 914 14.8 NA Rochester, NY 3.85 49 AASHTO T-288 679 9.6 20 A NA = not available B CR measured from moist and saturated samples. Results is from moist samples to be consistent with other measurements presented in this table. Measurement from saturated sample is 7.7 Âµm/yr. C This reading is from the top of the MSE wall. The CR measured near the base of the MSE wall was much lower, 2.1 Âµm/yr. D One outlier equal to 35 Âµm/yr. that appears to be dubious. Next highest is 3.2 Âµm/yr. Table 3-10 Ranges of corrosion rate according to resistivity. Resistivity clusters Observed corrosion rates, CR Galvanized steel Plain steel Ï > 10,000 â¦-cm CR < 2 Âµm/yr. CR < 5 Âµm/yr. 3000 â¦-cm < Ï < 10,000 â¦-cm 0 Âµm/yr. < CR < 6 Âµm/yr. 1.0 Âµm/yr. < CR < 20 Âµm/yr. 1000 â¦-cm <Ï < 3000 â¦-cm 10 Âµm/yr. < CR < 35 Âµm/yr. 10 Âµm/yr. < CR < 40 Âµm/yr.
48 Table 3-11 Data clustering relating corrosivity rankingsA to observed rates of corrosion. Cluster Sample GN PP#10 Test method (proposed protocol) Corros. RankA â(I) CR (Âµm/yr.) Galv. Plain N ot c or ro si ve â (I ) â¥ 0 San Antonio, TX 0.18 2 Tex-129-M 1.0 NAB Wake Forest, NC 2.21 8 Tex-129-M 2 0.3 < 0.1 Bastrop, TX 0.15 2 Tex-129-M 2 0.4 NA Ashdown, AR 2.88 36 AASHTO T-288 2 1.8C NA TTC, NC 3.51 24 AASHTO T-288 1 5.8 1.6 Ocala, FL 5.65 91 AASHTO T-288 0 1.8 3.8 LWF, South Carolina 4.83 68 ASTM WK 24621 0 1.2 8.4 El Paso Coarse/MSE 0.22 2 Tex-129-M 0 0.2 NA Waco, TX 1.26 7 Tex-129-M 0 0.3 NA Sl ig ht ly co rr os iv e -3 â¤ â (I ) < 0 M-U-D, NY 5.24 82 AASHTO T-288 -1 4.8 39 Garden City, TX 2.52 22 Tex-129-M -1 4.3 NA GB, South Carolina 4.48 56 AASHTO T-288 -1 3.2D 5.8 Maple Rd., NY 2.50 22 Tex-129-M -2 3.7 16 El Paso Fine/MSE 5.52 87 AASHTO T-288 -2 21E NA Prince George, BC 2.89 32 AASHTO T-288 -3 NA 20 C or ro si ve -5 â¤ â (I )< -3 PIP, NY 4.62 61 AASHTO T-288 -4 37 30 Sprain Brook Pkwy, NY 2.54 27 AASHTO T-288 -4 33 NA Quarry; El Paso, TX 3.64 41 AASHTO T-288 -4 14.8 NA Rochester, NY 3.85 49 AASHTO T-288 -5 9.6 20 A German Method DVGW-GW9 B NA = not available C CR measured from moist and saturated samples. Results is from moist samples to be consistent with other measurements presented in this table. Measurement from saturated sample is 7.7 Âµm/yr. D One outlier equal to 35 Âµm/yr. that appears to be dubious. Next highest is 3.2 Âµm/yr. E This reading is from the top of the MSE wall. The CR measured near the base of the MSE wall was much lower, 2.1 Âµm/yr. Table 3-12 Ranges of corrosion rate and corresponding ranges of â(I). Corrosivity clusters Observed corrosion rates, CR Galvanized steel Plain steel â(I) â¥ 0 CR < 2 Âµm/yr. CR < 5 Âµm/yr. -3 â¤ â(I) < 0 2 Âµm/yr. < CR < 5 Âµm/yr. 5 Âµm/yr. < CR < 20 Âµm/yr. -5 â¤ â(I) < -3 10 Âµm/yr. < CR < 35 Âµm/yr. 20 Âµm/yr. < CR < 40 Âµm/yr. After we apply clustering to these data, the benefits of the proposed protocol are apparent. We observe distinct clusters of data that can be useful for relating characterizations of corrosivity to performance.
49 3.5. Recommended Protocol We incorporated recommendations into the proposed protocol (presented in Appendix A) that are based upon results from our analyses of the data collected in Phase II. We summarized the proposed protocol in the form of a flowchart shown in Figure 3-14. In general, the proposed protocol describes application of the current AASHTO test series for samples with GN > 3, or if the percent passing the No.10 sieve is greater than 25%. Otherwise, if the GN < 3, and the percent passing the No.10 sieve is less than 25%, the Texas modified procedures are recommended (i.e., Tex-129-M and Tex-620-M). We considered four factors to select methods for the proposed protocol including a) precision and repeatability of the test methods, b) compatibility between parameters (e.g., salt contents and resistivity), c) correlations between geochemical and electrochemical properties, corrosivity and corrosion rates, and d) the utility of the test results. All of these factors support the implementation of the AASHTO and Texas modified procedures within the proposed protocol. The statistics included in the evaluation of the test methods describe the repeatability of the test results and correlations between measurements of corrosivity/resistivity and corrosion rates. These statistics demonstrate that the proposed protocol renders results that correlate best with observations of corrosion rates, although observed differences are not large. Also, the repeatability of the Texas modified test series is the best compared to the other test methods. This is partially used to justify recommending the AASHTO test series and the Texas modified test series in the proposed protocol. Other considerations include the observed trends between parameters such as salt contents and resistivity and the utility of the test results, which favors use of the AASHTO or Texas modified tests. Coefficients of correlation and the statistics of the measurements are not the only factors considered in the selection of test recommendations. There are benefits to obtaining the relationship between moisture content and resistivity. These benefits include the ability to relate laboratory and field measurements of resistivity where the field moisture content is known. Correlation between laboratory and field measurements also requires that the gradation of the material tested in the laboratory and in the field are similar. Tex-129-M satisfies these needs because the test specifies that resistivity measurements be obtained for a range of moisture contents up to saturation, and the test includes all particle sizes up to 13 4 inches. ASTM G-187 (2018) is only performed at moisture contents corresponding to as-received or saturated, and only includes particle sizes up to 1 4 inches. Therefore, for the MSE wall application, ASTM G-187 (2018) is not as desirable as Tex-129-M. ASTM G-187 may be desirable for other applications where water content is not variable with respect to time or is maintained at particular levels such as always saturated. ASTM WK 24621 is also not applicable to a range of moisture contents but may apply to some materials that drain freely. ASTM WK 24621 may be particularly applicable for materials that absorb moisture, because saturation requires soaking. and a 24-hour soaking period is specified by WK 24621.
new material is received determine the gradation of material GN > 3 or PP#10 > 25% GN < 3 and PP#10 < 25% PP#10 > 15% determine LL, PL, and SE determine electrochemical properties < 15% AASHTO T-288 (Ïmin, Ïsat) T-289 (pH) T-290 ([SO4]) T-291 ([Cl-]) PP#200 > 5%< 5% keep adding water to reach Ïmin add water to reach Ïsat determine Ïcorr evaluate the relation between Ïcorr and salt content correlation coefficient (R) > 0.5 R < 0.5measure alkalinity (ppm of CaCO3) determine electrochemical properties 620-M ([Cl-], [SO4]) Tex-129-M (Ï) NCHRP 21-06 (pH) select the box size (function of MAS) PP#200 > 5%< 5% keep adding water to reach Ïmin add water to reach Ïsat determine Ïcorr evaluate the relation between Ïcorr and salt content R < 0.5 evaluate the relation between Ïcorr and mEq of CaCO3 R > 0.5add a criteria for CaCO3 content to characterize corrosion potential R < 0.5 use thresholds of [SO4] and [Cl-] to characterize corrosion potential correlation coefficient (R) > 0.5 Figure 3-14 Flow chart of the proposed protocol. 50