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.

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Page 1: Soil Corrosivity Analysis part 3corrosionsurvey.co.kr/viewer/pdf/n_02.pdf ·  · 2010-03-16Other soil parameters were analyzed by conventional soil analysis methods [100]. Table

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.

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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).

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Figure 1. Location of field survey sites

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)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

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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.

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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

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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

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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].

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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%)

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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

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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])

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-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

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0 20 40 60 80 100 1200

1

2

3

P 0

Disbonded Area (cm2)

Figure 5. P0 vs. disbonded area of coating

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100 101 102 103 1040

1

2

3

P 0

[SO42-] (mg/g of soil)

Figure 6. P0 vs. concentration of sulfate ion

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4 5 6 7 8 9 100

1

2

P 0

pH

Figure 7 P0 vs. soil pH

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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

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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

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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

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-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

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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

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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

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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

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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.

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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

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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

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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.

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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

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∑ ∑∑= =

+=

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

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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

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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

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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.

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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.

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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.

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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

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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

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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

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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.

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(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.