soil corrosivity analysis part 3corrosionsurvey.co.kr/viewer/pdf/n_02.pdf · · 2010-03-16other...
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1
SOIL CORROSIVITY ANALYSIS
1. INTRODUCTION
All corrosion phenomena including MIC that occurs in soil environments
are closely related to soil parameters as mentioned previously. Therefore, it is the
most important to survey, analyze and evaluate the soil parameters for the
assessment of soil corrosivity. If the corrosivity of soil is known, it can provide
useful information for the selection of pipeline paths, the methods of corrosion
control in the stage of design, and the maintenance of underground metallic
structures.
In terms of ecology, the growth of MIC-causing SRB totally depends on
the availability of nutrients from surrounding soils, which means that it is very
meaningful to assess the soil parameters for the prediction of SRB-corrosion risk
also.
The objectives of this study were to conduct extensive field surveys for a
better understanding of MIC and to develop a predicting equation that can be
used to classify new sites of an unknown corrosive class.
2. EXPERIMENTAL DESIGN AND PROCEDURES
2.1. Field Survey
Sixty-nine sites spread over Korea were investigated during 1998 to 2000.
Figure 1 shows the location of these sites as dots.
2
Seventeen environmental factors were measured where the coating
defects of pipelines were detected by direct current voltage gradient (DCVG)
survey or in-line magnetic flux leakage (MFL) pigging.
2.2. Environmental Variables
The variables measured in this field study were clay content (Clay),
burial depth (BD), disbonded area (DA), soil resistivity (ρ), water content (Wc),
content of sulfate ion (SO42-), content of chloride ion (Cl-), alkalinity (Alk.), pH,
number of SRB (SRB), number of APB (APB), total organic carbon (TOC),
reduction-oxidation potential (Eh), pipe-to-soil potential (P/S) and maximum pit
depth (Pmax).
ρ and P/S were obtained by in-situ measurement in the field before
excavation. BD, Eh, DA and Pmax were measured after excavation, whereas other
factors were analyzed in the laboratory by examining sampled soils. Sampled
soils were that attached to coating defects directly, i.e., contacted to the bare
metal surface. ρ was measured by the ASTM G57 Wenner 4-point method [92]. Eh
was measured as a potential of platinum electrode using a saturated copper-
copper sulfate reference electrode [93], and this value is presented with respect to
standard hydrogen potential (SHE) at pH 7 according to equation (5.1).
3
Figure 1. Location of field survey sites
4
)7pH(59242)CSE/mV(E)SHE/mV(Eh −×++= (5.1)
In the final step of the field survey, Pmax was measured using depth gauge or
ultrasonic thickness gauge after removing corrosion products in case corrosion
occurred.
TOC was analyzed by dry combustion method using air-dried soil [94].
Clay content was measured by mechanical sieving [95-96]. Soil pH was measured
after mixing of sampled soil and deionized water in volume ratio of 1:5, followed
by stirring for 5 min [97]. Wc was measured by gravimetric method with oven
drying at 1100C for 24 hours [98]. Water-soluble anion content was analyzed by
ion chromatography [99]. The population of SRB and that of APB were
enumerated by most probable number (MPN) techniques as described in Chapter
3. Other soil parameters were analyzed by conventional soil analysis methods
[100]. Table 5-1 summarized measured environmental factors.
2.3. Quantitative Corrosivity Assessment
After all measurement and analysis, quantitative corrosivity assessment
was conducted using ANSI/AWWA C105/A21.5 method [101] to evaluate the
corrosivity of soil. Originally, this method was developed to determine whether
polyethylene (PE) encasement should be applied for the corrosion protection of
ductile iron pipes buried in soil. However, this method is relatively simple and
includes evaluation factors closely related to SRB-related MIC. Moreover, other
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Table 1. Environmental factors measured or investigated in field study
Variables Analytical Method Symbols
Clay Content (%) Sieve analysis Clay
Soil resistivity (Ω⋅cm) Wenner 4-point method ρ
Water content (%) Gravimetric method with oven drying Wc
pH pH meter pH
*Chloride content (ppm) Ion chromatography Cl-
*Sulfate content (ppm) Ion chromatography SO42-
*Alkalinity (ppm) Titrimetry Method Alk.
Redox potential (V/NHE) Potential of platinum electrode Eh
Population of SRB (cells/g-soil) Culture technique & MPN method SRB
Population of APB (cells/g-soil) Culture technique & MPN method APB
Total organic carbon (%) Dry combustion method TOC
Burial depth (m) Observation at field BD
Burial period (y) Investigation of Design Specification BP
Disbonded area of coating (cm2) Measurement at field DA
Maximum pit depth (mm) Measurement at field, thickness gauge Pmax
Maximum corrosion rate (mm/y) [Pmax/BD] CR
Pipe-to-Soil potential (V/CSE) Potential measurement P/S
*Measured by Korea Testing and Research Institute for Chemical Industry (KOTRIC),
Yongdeungpo-Dong, Seoul, Korea
6
quantitative assessment methods [102-103] applicable to buried carbon steel
structures are complex and time-consuming. Therefore, this method was used
whether quantitative method, which is practically used in design stage of
underground pipeline, could predict the risk of MIC effectively or not. If the total
score of evaluation is above 10 points, this soil is regard as a corrosive soil, then
the application of protective PE encasement is recommended. Evaluation items
and scoring methods are listed in Table 5-2.
3. EXPERIMENTAL RESULTS AND DATA ANALYSIS
Data on soil-related environmental factors and maximum corrosion
depth obtained from this survey were analyzed using graphical methods,
quantitative corrosivity assessment, linear regression analysis (LRA), principal
component analysis (PCA) and multiple regression analysis (MRA). Finally, the
predicting equation for the number of SRB and that of maximum corrosion rate
were presented.
3.1. Field Survey Results
Tables and plots of the field survey data are summarized in Appendix A.
Figure A-1 to A-18 shows the locational distribution of burial depth, burial
period, the number of SRB, the number of APB, soil resistivity, water content,
sulfate content, chloride content, alkalinity, TOC, Eh, pH, clay content, P/S, Pmax,
maximum corrosion rate. Figure A-19 to A-36 shows the histograms of
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Table 2. ASNI/AWWA C105/A21.5 Soil-test evaluation [101]
Soil Characteristics Points Resistivity (Ω⋅cm):
< 700 700-1000 1000-1200 1200-1500 1500-2000 >2000
10 8 5 2 1 0
pH: 0-2 2-4 4-6.5 6.5-7.5 7.5-8.5 >8.5
5 3 0 0* 0 3
Redox potential: > +100mV +50 to +100mV 0 to +50mV Negative
0 3.5 4 5
Sulfides: Positive Trace Negative
3.5 2 0
Moisture Poor drainage, continuously wet Fair drainage, generally moist Good drainage, generally dry
2 1 0
* If sulfides are present and low or negative redox-potential results are obtained, give 3
points for this range.
8
environmental factors investigated. The results showed that the soil was
characterized by:
Broad spectrum for the values of environmental factors
Iron sulfides and underground water were found at every corrosion site
High levels of chloride at corrosion sites
High levels of SRB and APB at corrosion sites
The positive dependence of corrosion depth on P/S and DA
3.2. Quantitative Assessment Results
Figure 2 shows the relationship between maximum corrosion rate and
ANSI corrosivity index.
It is evident that the ANSI index has linear relationship with corrosion rate and
was relatively accurate when the corrosion was small; however, as the index
reaches above 10 points, which are the criterion for severe corrosivity of soil, the
corrosion rate is unpredictable using this method. Therefore, qualitative ANSI
assessment method can only tell the probability of the occurrence of corrosion,
but cannot tell how much corrosion will occur.
3.3. Linear Regression Analysis (LRA)
Generally, the results of corrosion experiments and tests often show
more scatter than many other types of tests because of a variety of factors as
9
0.1 1 10 1000.0
0.2
0.4
0.6
0.8
1.0
Max
imum
Cor
rosi
on R
ate
(mm
/y)
ANSI Corrosivity Index
Figure 2. Maximum corrosion rate vs. ANSI corrosivity index
10
mentioned earlier. Statistical analysis can be very helpful in allowing
investigators to interpret such results, especially when test results differ from one
another significantly [104]. This is in the case of underground corrosion. This is a
difficult task when a variety of parameters involves in corrosion process, but
statistical methods provide a rational approach to this problem.
Regression analysis is the examination of the relationship between one
dependent variable, such as the number of SRB, or maximum corrosion depth,
and another sets of variables, such as pH, resistivity, redox potential etc.
Initially linear regression analysis was used to determine the variables
closely related to the number of SRB and maximum corrosion depth. Note that a
log transformation (base 10) was used to adjust the range of the six variables such
as the number of SRB, the number of APB, resistivity, content of sulfate, chloride
and alkalinity, because of the wide range of observations (See Table A-2).
Table 3 shows the Pearson correlation coefficients (r) between each
environmental factor. These coefficients indicate the degree to which two
parameters act independently of one another. Values of 1.000 or –1.000 indicate
perfect positive or negative correlation, respectively, while a value of 0.000
indicates an absolutely random relationship between two parameters. Results for
some parameters are omitted because of poor correlation with P0
(Pmax/Pmax_average). Parameter pairs having significant tends were selected based
on a confidence level of 99% (i.e., a significance level of 1%) [105]. Generally, the
reliability of the results of LCA is strongly related to the size of sample [106].
11
Table 3. Values of Pearson correlation coefficient among environmental factors
P0a Alka P/S DA SO4a TOC pH SRBa Cla APBa Wc Eh ρa Clay
P0a 0.793 0.773 0.682 0.615 0.504 -0.502 0.441 0.408 0.386 0.358 -0.321 -0.292 -0.030
Alka 0.164 0.097 0.565 0.700 0.398 0.111 0.431 0.513 0.313 -0.156 -0.443 0.195
P/S b 0.391 0.308 0.465 -0.392 0.289 0.375 0.479 0.339 -0.240 -0.290 0.112
DA 0.538 0.187 -0.486 0.511 0.566 0.404 0.249 -0.590 -0.384 0.095
SO4a 0.690 -0.046 0.467 0.484 0.467 0.250 -0.363 -0.375 0.079
TOC -0.304 0.110 0.279 0.268 0.161 -0.057 -0.247 0.242
pH -0.156 0.079 -0.351 -0.046 0.068 -0.109 0.131
SRBa 0.525 0.555 0.579 -0.580 -0.661 0.608
Cla 0.343 0.394 -0.366 -0.562 0.298
APBa 0.419 -0.340 -0.342 0.386
Wc -0.339 -0.637 0.278
Eh 0.638 -0.350
ρa -0.416
Clay
a: Log value.
b: Pairs having significant correlation at a confidence level of 99% (i.e., a significance level of 1%)
12
Figure 3 shows that the dependence of r-value required for 5% significance level
on the size of sample. Through this procedure, parameters having significant
trends with a significance level of 1% was extracted and expressed as black
circles in Table 5-3 considering the effects of sample size.
The analysis showed that close correlation was exhibited between the
corrosion depths and following variables; P/S, DA, SO42-, pH, SRB. R-values
were 0.773, 0.682, 0.615, -0.502, 0.441, respectively. Figure 5-4 to 5-8 show the
respective relationship between P0 and P/S, DA, SO42-, pH, SRB. It is also
remarkable that the population of SRB is closely related with the population of
APB, which comprises the MIC-related microbial community as mentioned
earlier and with the anaerobic nature of soil, i.e., high clay content, low redox
potential and high water content of soil, etc. Figure 5-9 to 5-14 show the
respective relationship between the population of SRB and resistivity, clay
content, redox-potential, water content, the population of APB and the
concentration of chloride ion, respectively.
From these results, it is evident that the corrosion behavior of carbon
steel in soil is closely related to the environmental factors, and it is possible to
extract key variables related to corrosion by linear correlation analysis technique.
This is also for the case of the population of SRB.
On the other hand, some of the variables were highly correlated to each
other (i.e., high colinearity), e.g., TOC-Cl--SO42-. High colinearity can yield large
estimated variances (or standard deviations) and it make difficult to detect the
13
0 10 20 30 400.0
0.2
0.4
0.6
0.8
1.0
Reliable
UnreliableAbs
olut
e Va
lue
of r
Req
uire
d fo
r Sig
nific
ance
at t
he 5
% L
evel
N (Size of Sample)
Figure 3. Relationship between absolute value of r required for significance
at the 5% level and size of sample (N) (redrawn from table in ref.
[106])
14
-2.0 -1.8 -1.6 -1.4 -1.2 -1.00
1
2
3
P 0
P/S (V/CSE)
Figure 4. P0 vs. pipe-to-soil potential
15
0 20 40 60 80 100 1200
1
2
3
P 0
Disbonded Area (cm2)
Figure 5. P0 vs. disbonded area of coating
16
100 101 102 103 1040
1
2
3
P 0
[SO42-] (mg/g of soil)
Figure 6. P0 vs. concentration of sulfate ion
17
4 5 6 7 8 9 100
1
2
P 0
pH
Figure 7 P0 vs. soil pH
18
103 104 105 106 107 108 1090
1
2
3
P 0
SRB (cells/g of soil)
Figure 8. P0 vs. the population of SRB
19
102 103 104 105 106101
102
103
104
105
106
107
108
109
SRB
(cel
ls/g
of s
oil)
ρ (Ω ·cm)
Figure 9. population of SRB vs. soil resistivity
20
0 10 20 30 40 50 60100
101
102
103
104
105
106
107
108
109
SRB
(cel
ls/g
-soi
l)
Clay Content (%)
Figure 10. the population of SRB vs. clay content
21
-0.2 0.0 0.2 0.4 0.6 0.8100
101
102
103
104
105
106
107
108
109
SRB
(cel
ls/g
-soi
l)
Eh (V/NHE)
Figure 11. the population of SRB vs. redox potential
22
0 10 20 30 40 50100
101
102
103
104
105
106
107
108
109
SRB
(cel
ls/g
-soi
l)
Water Content (%)
Figure 12. the population of SRB vs. water content
23
102
103
104
105
106
107
108
109
102 103 104 105 106 107 108 109
SRB (cells/g-soil)
APB
(cel
ls/g
-soi
l)
Figure 13. the population of SRB vs. the population of APB
24
10-2 10-1 100 101 102 103101
102
103
104
105
106
107
108
109
SRB
(cel
ls/g
-soi
l)
Chloride (ppm)
Figure 14. the population of SRB vs. the concentration of chloride ion
“significant” regression coefficients [107]. Because of this interaction effect, it is
25
unreliable to predict the corrosion rate using parameters extracted from linear
correlation analysis, which are believed to be closely related to corrosion process.
Therefore, principal component analysis (PCA) was conducted for the extraction
of variables more precisely.
3.4. Classification of Environmental Factors - Principal Component Analysis
From the LCA result, it was found that there are correlation between
maximum corrosion depth and some variables. However, it was difficult to select
outstanding factors correlated with severe corrosion because so many factors
influence each other as shown in Table 3. Therefore, a second approach, principal
component analysis (PCA), was used to understand the nature of this complexity
and to extract the key variables. The aim of PCA was to determine more precisely
the interrelating with controlling factors affecting corrosion, and to verify the
validity of the previous discussion by predicting P0 from the controlling factors .
PCA is widely used in statistics, signal processing and neural computing.
The basic goal of PCA is a technique to reduce the number of variables. The
factor loadings, i.e., correlating coefficients between variables and principal
components, were plotted as shown in Figure 15.
26
SRB
APBClay
ρWc
SO4
Cl
Alk
Eh
TOC
pH
P/S
ANSI
DA
P0
-1
-0.5
0
0.5
1
-1 -0.5 0 0.5 1
Primary Principal Component
Seco
ndar
y Pr
inci
pal C
ompo
nent
I
II
I
Figure 15. Relation of variables obtained by PCA
27
It was judged from this plot that a group of variables encircled by a broken line
belongs to one group having common characteristics shown in Table 5-4.
Table 4. Characteristics of variable groups
Group Variable Feature
I P0, Cl-, Wc, SRB, APB, Clay, P/S, Eh, pH,
ρ, ANSI, DA
Closely related to
corrosion
II SO42-, TOC, Alk. Related to soil chemistry
Two groups were found in this case and therefore maximum corrosion rate can
be predicted by only considering “group I” and by ignoring “group II” with little
error. From these variables in group I, ANSI, and APB are closely related to soil
parameters and can be express as a function of other variables. Therefore, these
factors were also not considered for the prediction of corrosion rate.
3.5. Prediction of Corrosion Rate
(1) Model Equation
The stepwise multiple regression analysis was conducted with corrosion
ratio, P0, i.e., the maximum corrosion depth divided by the average maximum
corrosion depth, as the criterion variable. The independent variables were
selected by taking the result of PCA into consideration and adding judgment by
28
proper techniques.
In order to predict the corrosion amount, a mathematical model
expressing the relation between the quantity of corrosion and the reasons is
required. It is well known that the rate at which corrosion pits grow in the soil
under a given set of conditions tends to decrease and follows a power-law
equation [109].
nktP = (2)
where P is the maximum corrosion depth in time k and n are constants. If k and n
are determined, it becomes possible to predict the progress of corrosion in depth.
Figure 16 shows the variation of P in various soil environment reported by
Romanof. He reported that k and n values varied according to soil parameters.
Therefore, it can be possible to relate k and n values with soil parameters
extracted in Section 5.3.4. From Figure 15, the constant n depends on the state of
aeration, i.e., Eh. However, all corrosion phenomena occurred in anaerobic soil in
field survey. Thus, it is reasonable to assume that n is independent of soil
parameters, only k depends on soil properties in this case, and n can be
expressed as a constant regression coefficient.
From this deduction, equation (1) was adopted as a basic model equation.
This equation can be expressed by equation (2) by rewriting (1) using
quantitative variables xj’s extracted by PCA.
29
0 5 10 15 20 25 300
1
2
3
4
5
6
P=ktn
very poor aeration
poor aeration
fair aeration
good aeration
Pit D
epth
(mm
)
Time (year)
Figure 16. Maximum pit depth of steel vs. time in various soils
30
∑ ∑∑= =
+=
ε+α+α+α+α=q
1j
q
1kii1qkjjk
q
1jjj0i tlogxxxPlog (3)
wherein, q: the number of variables
xj: environmental variables
εi: error
i=1, 2, …, n: the number of samples
This predicting equation considered the effect of single variables (αjxj) and the
interaction effects between variable (αjkxjxk) – which was proved from the result
of LCA - on the corrosion process. In the multiple regression analysis, firstly
coefficients αj and αjk of the environmental factors were determined by the least
square method, and then a coefficient αq+l of log ti was its residual as the criterion
variables.
(2) Multiple Regression Analysis
Multiple regression analysis was conducted to obtain the predicting
equation. Note that the variables used in the regression analysis excluded APB
because this variable is strongly related to SRB. DA was excluded because it
cannot be measured by conventional survey without excavation. ANSI was also
excluded because it is qualitative and depends on other variables. Therefore, total
seven variables such as Cl-, Wc, Clay, P/S, Eh, pH and ρ was used as independent
31
variables. As mentioned previously, a log transformation was used to adjust the
range of variables, Cl-, and ρ because wide range of observations from 0.1 to 220
and from 628 to 23,738, respectively.
The stepwise multiple regression method was used to determine subsets
of soil variables that best describe the maximum corrosion depth. In this method,
variables are added one by one to the model, and the F statistics for a variable to
be added must be significant at the certain α level (conventionally 0.05, i.e., 95%
of confidence level). After a variable is added, however, the stepwise method
looks at all the variable that does not produce an F statistic significant at the
certain α level [111-112].
Stepwise multiple regression results form the field study data are
summarized in Table 5.
Table 5 shows that 0.887 of R2 was obtained by introducing the variables such as
Log (SRB), P/S, Log (Cl-), Eh × Clay, pH × Log (ρ).
)(LogpH014.0ClayE050.0)Cl(Log203.0S/P749.0)SRB(Log069.0700.0LogP hc ρ×−×−+++= −
(4)
(Pc: predicted value of P0)
(3) Prediction of Corrosion Rate
Therefore, k in equation (2) was obtained by (4), i.e., the first three terms
in equation (3). In order to predict the corrosion rate, or corrosion depth
32
Table 5. Stepwise regression results from field data
Log P0 = 1.318 + 1.034 P/S R2 = 0.627
Log P0 = 0.983 + 0.983 P/S + 0.224 Log (Cl-) R2 = 0.725
Log P0 = 0.681 + 0.048 Log (SRB) + 0.934 P/S + 0.184 Log (Cl-) R2 = 0.756
Log P0 = 0.434 + 0.104 Log (SRB) + 0.840 P/S + 0.174 Log (Cl-)
– 0.011 Eh × Clay R2 = 0.873
Log P0 = 0.700 + 0.069 Log (SRB) + 0.749 P/S + 0.203 Log (Cl-)
– 0.050 Eh × Clay – 0.014 pH × Log (ρ) R2 = 0.887
33
as a function of time, it is necessary to determine n in equation (2). Thus, n was
determined as the regression coefficient for the linear model of equation (3) with
the residual of equation (5.4) as the criterion variable.
tlognAPlogPlog c0 +=− (A: constant) (5)
From this regression process, P0 can be predicted from the corrosivity of the
environment and the burial period by equation (5.6).
372.0ccal,0 tP500.0P = (6)
wherein P0,cal: the predicted value of P0
Pc: the evaluation value of the environment in corrosiveness
(according to (3))
t: the burial time (y)
The correlation coefficient between P0 and P0,cal was 0.947. Figure 16 shows the
scatter diagram of P0 and P0,cal. Data fell closely onto the straight line with the
slope of 0.860.
In addition, in order to confirm the existence of defects or biased prediction by
the regression equation obtained, the standardization residual (εs) was plotted
against P0,cal as shown in Figure 17.
34
0 1 2 3 4 5 6 70
1
2
3
4
5
6
7
Mea
sure
d Pi
t Dep
th (m
m)
Predicted Pit Depth (mm)
Figure 16. Relationship between P0 obtained from multiple regression
analysis and that obtained from field survey.
35
SPP cal,00
s−
=ε (7)
wherein, S: the standard deviation of the residual (P0-P0,cal)
As shown in Figure 17, there is some tendency that the standardized residual
increases as the corrosion ration increases. However, equation (5) is statistically
acceptable because |εs| ≤ 3, which means there is no potential outlier. Therefore,
the result of statistical analysis supported satisfactorily the validity of the model
in this study.
It is also found in this study that the underground corrosion of steel was
affected mainly by three factors as shown in equation (5): (1) chemical factors
such as Cl-, pH × Log (ρ), (2) biochemical (microbial) factors such as Log (SRB)
and Eh × Clay and (3) CP factors such as P/S. This means that the corrosivity of
soil can be evaluated quantitatively by analyzing the chemical and biochemical
properties of soil itself.
The contributions of these three factors to total corrosion could be
evaluated semi-quantitatively from equation (5) if ignoring the interaction of
each factor. Figure 18 shows the relative contributions of each factor. The
contribution of microbial, chemical, CP factors are in a range of 45 to 75%, 20 to
45% and 2 to 7%, respectively. It is evident that the contribution of microbial
factor (about 45 to 75%) is most important in anaerobic environment as shown in
Figure 18.
36
0.0 0.5 1.0 1.5 2.0 2.5-3
-2
-1
0
1
2
3
Stan
dard
ized
Res
idua
l εs
Corrosion Ratio P0,cal (Predicted)
Figure 17. Predicted corrosion ratio against standardized residual
37
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Relative Contribution
Sample No.
CP Chemical Microbial
Figure 18. Relative contribution of environmental to corrosion
38
Therefore, it is concluded that the main cause for the anaerobic corrosion of
buried steel pipeline is the activity of microorganism, i.e., SRB.
However, it is important that the corrosion did not occurs at all defects of
coated pipe steel even if the adjacent soil is corrosive, i.e., high activity of SRB,
low ρ, low Eh, low pH, high level of chloride, etc. This is because CP prohibited
the progress of corrosion. In the other hand, the efficiency of CP is greatly
affected by soil properties, geometry of coating defects and so on. In this study,
all corrosion phenomena occurred inside the disbonded coating where CP
current could not penetrate and was sufficiently anaerobic for active growth of
anaerobic bacteria including SRB. This effect was included as P/S term in the
resultant predicting equation (5).
In Figure 5-18, the contribution of P/S is relatively small, but this is because the
measured value is not the real polarized potential, but the pipe-to-soil potential
which having some error due to the IR drop in soil, and which is the averaged
value over the large area, not the value representing the very defect point alone.
If the polarized potential adjacent to the defect point is considered, the
contribution of CP may be larger.
This is also confirmed by the fact that the measured P/S potential in field site is
under –0.85 V/CSE, which are the CP criterion for buried pipeline. Nevertheless,
the corrosion occurred. This means that the steel surface beneath the disbonded
region was not cathodically protected. However, there are no methods to
measure the potential beneath disbonded coating from the state-of-the-art
39
technique in practice. Therefore, it was inevitable to use of P/S term in this
model. Rather, more severe CP criterion, e.g., lowering the criterion potential of –
0.85V/CSE to more negative values, should be considered.
4. CONCLUSIONS
From the field survey conducted, the following conclusions can be
drawn:
(1) From field survey, it was found that the corrosion of underground
steel structures occurred at the steel surface under the disbonded coating. The
maximum corrosion depth of 6.54 mm, which corresponded to the maximum
corrosion rate of 0.8 mm/y was found. The corrosion site is mainly correlated
with the anaerobic site characterized by the precipitation of biogenic iron sulfide.
(2) Quantitative method for the evaluation of corrosivity such as ANSI
method is useful to evaluate the probability of the occurrence of corrosion.
However, this method cannot give quantitative information.
(3) A model has been presented to predict the maximum corrosion depth
of steel pipes in soil environments. The multiple regression model with k in
P=ktn reflecting the environmental factors and n as the regression coefficient has
been established. The results showed that the predicting equation explained
successfully the field corrosion phenomena.
40
(4) The underground corrosion is mainly affected by chemical and
biochemical properties of soil such as pH, resistivity, redox potential, clay
content, the level of chloride and the activity of SRB. It was also found that the
main factor affecting underground corrosion is the action of SRB, which implies
the risk of MIC, should be investigated thoroughly for the integrity of
underground structures.
(5) The effectiveness of cathodic protection should be considered
together for the precise evaluation of the risk of underground corrosion.