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Degradation models and measurements of corrosion in marine environment:

state of the art and challenges Franck Schoefs

LUNAM Universit, Univ. of Nantes, FranceInstitute for Research in Mechanical & Civil Engineering,

UMR CNRS 6183Sea and Litoral Research Institute, FR CNRS 3473

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Contents

Objectives of corrosion modeling for strutural maintenance of steel structures

Key factors involved in the corrosion process

Review of existing models for LT corrosion

The Euromarcorr data base

Stochastic modelling:randomness and spatial variability

Probabilistic modelling of NDT results and inspection based decision

Repair efficiency08/06/2012 2

ICDS12 International Conference DURABLE STRUCTURES: from construction to rehabilitation LNEC Lisbon Portugal 31 May - 1 June 2012

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Objectives of modeling for maintenance

Use of steel in harbour structures

Long term prediction: 10 to 50 years

Modeling of the influence of key factors

Modeling the variability (for reliability and risk analysis)

Accurate/Suitable for updating

Suitable for repair modeling

Computation cost reduction (optimisation of repair, )08/06/2012 3

Sheet-piles wall

On-pile wharf

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Key factors involved

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Marine environment

Bacteria

SolarEnergy

+ CO2+photosyntheticpigments

Minerals saltsH2O

ParameterspH

Temperature

Salinity

>>dissolved O2

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Main mechanism

Oxo-hydroxyde Fe(+III)

MagnetiteFe(+II/+III)

Steel

O2

O2

O2

Fe2+

Fe2+

O2 + 2H2O + 4e- 4OH-

Fe Fe2+ + 2 e-Fe2+ + 2OH- Fe (OH)2

Steel

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Othereffects

Actual knowledge:Identification of underlying mechanims, validateassumption in lab., relative quantification // on site: non-independent mechanims (overlaying, different kinetics, competition, effect of environment, nutrients: govern by Stochastic factors)

[S. Borenstein, 1994] [P. Roberge, 1999]


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Spatial dependence

Relationship between depth and loss of steel

[Marsh 1999]

Bacteria

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Spatial dependence

Relationship between depth and loss of steel

MUD

IMMERSION

SOIL

SPRAYO2high

O2low

TIDE

Area very sensitives to corrosionLoss ofweight (151dKureBeach,USA)

[Larrabee, LaQue 1950]


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Spatial dependence

Spray zone

Splash zone

Tide zone

Immersion zone

Soil zone

BMM

PMM

Low see level

Mud zone

7 zone of exposure

Vertical theoretical profile of the velocity of loss of thickness (mm/year) near the coasts

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Main modelsType 1. Empirical models

Immersion and tidal zone

[Melchers, Jeffrey, 2008]

Immersion zone (shipcorrosion)

[Guedes Soares, Garbatov 1999]

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CoV constant or decreasing

20


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Main modelsType 1. Empirical models


[Guedes Soares, Garbatov 1999]


[Paik 2003 2004]

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Main modelsType 2. Physico-chemical

Immersion zone[Evans 1996, Tomashev 1966,

Chernov & Ponomarenko 1999]

Where: K: loss of thicknessP : coefficient accouting for

protective properties of corrosion layer (m2/an).

For instance, P = (1054,04 121,35) [1+(0,045 0,008)t0]

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Main modelsType 3. Phenomenological

Immersion zone[Melchers 2008]

Where:c(t,E) : loss of thickness fn(t,E) :

mean of loss of thickness (mm)

(t,E) : variability of loss of thickness (mm)

b(t,E) : bias (error of modeling)E : vector of environmental

factors (physical, chemical & biological).

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Key factors involvedStrategy for LT modelling

Challenges for the LT modeling: The corrosion process is hard to model: It is affected by a lot of time-variant and space-dependent

factors : Temperature, Dissolved Oxygen, Salinity, Tide level, Suspended materials-nutrients (bio-corrosion), pollution, water flow/waves, abrasive materials.

Only few on-site measurements are available and not always well documented (context+monitoring).

On-site measurements are costly and difficult to realize. Complex and uncertain mecanisms: simultaneous and in

competition.

Need to gather data in a well documented data base

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The Euromarcor Data Base

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The Euromarcor Data Base: the protocol

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ZI : Immersion Zone

ZT : Tidal Zone

Soil

ZA : Aerial Zone

ZE : Spray Zone

ZM : Mud Zone

ZL : Low Seawater Zone

Beam

Sheet-pile wall

Lateral and frontal section of the structure

Tie-rod

Measurement zone (average of readings)

Residual thickness measurements by using ultrasonic testing

Vertical axil along the structure

Z-axis

Z-axis

X-axis


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Structures composed by sheet-piles Concerns 4 harbors : Boulogne-sur-Mer & Le Havre (Channel), Port-la-Nouvelle & Ste (Mediterranean sea)

Data distribution by structures aging

Age (years)

Num

ber o

f ND

T co

ntro

ls

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23 studied structures 35 000 measures

Harbors

Stru

ctur

es n

umbe

r


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23 studied structures 35 000 measures for sheet piles

Harbors

Measurement collects distribution of loss of thickness in function

of exposure zones for studied structures

Mea

sure

s nu

mbe

r

ZI : Immersion Zone

ZT : Tidal Zone

Soil

ZA : Aerial Zone

ZE : Spray Zone

ZM : Mud Zone

ZL : Low Seawater Zone

Num

ber o

fmea

sure

men

ts

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Summary

100 000 measurements: The data base is now documented

Nb and location of measurements

Type of maritimeEnvironment / owner

Chemicalcharacteristics


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Data AnalysisSynopsis

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Data AnalysisSpatial evolution (with depth)

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Harbor HA : Basin, Tidal effect

Spatial trend of corrosion as a function of Z-axis

Loss of thickness (mm)

Mar

ine

cots

(m)


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PDF retained for representing loss of thickness in function of time

Age (years)

Num

ber o

f Mea

sure

sGamma pdfthe best one

Data AnalysisStatistical analysis

Best fit: Maximum Likelihood Estimate


Stochastic ModellingOf corrosion

General model

ZI

ZM

Soil

ZA

ZE

ZB

ZBE

c(x,zj,t,) = T(x,zj,t) + c(0,zj,t,)

Loss of thickness c (mm) as a function of x, exposal zone zj & time t, is the hazard

Centered determinist trend T (mm) of stochastic process as a function of x


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Gamma distribution of parameters & ( & > 0):c(x,zj,t,) gamma distributed

ZI

ZM

Soil

ZA

ZE

ZB

ZBE



c(x,zj,t,) gamma distributed

Temporal evolutions of loss of thickness from &

increases and reaches a constant value

ZI

ZM

Soil

ZA

ZE

ZB

ZBE Five zones


CoV isdecreasingwithtime


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c(x,zj,t,) gamma distributed

Distribution for t = 25 years

ZI

ZM

Soil

ZA

ZE

ZB

ZBE


[Schoefs, Boero, Melchers, 2010]



General model

ZI

ZM

Soil

ZA

ZE

ZB

ZBE

c(x,zj,t,) = T(x,zj,t) + c(0,zj,t,)

Loss of thickness c (mm) as a function of x, exposal zone zj & time t, is the hazard

Centered determinist trend T (mm) of stochastic process as a function of x


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Data AnalysisSpatial variability

- Pile scale - Structural scale - Harbour scaleSea water

OutspanInspan

Wing

Sea water

Ratio Outspan/Inspan

Tidal zone

1000 m

2 mm

1 mm

7 mm

4 mm

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28 cmFluctuation parameter

0,2

0

0,2

0,4

0,6

0,8

1

50,50 51,50 52,50 53,50 54,50 55,50

Corrlation(

)

Distance(m)

Depth2.5 Depth2 Depth1.5Depth1 Depth0.5 Depth0.0Depth0.5

0,35

0,6


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Optimisation of the distance betweeninspection

[conf. Tran et al. 2012]

Number of sections of 10 meters length

18 meas.

15 meas.

NsNt

Min N=Nt*Ns

PDF

c=95%, =3

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Probabilistic ModellingOf Inspection results

Structural network

Limit states

RiskAssessment

Consequenceanalysis

RBI(optimize the planning)

Bad decisions (over-costs)- Non necessary repair (detection of a non existing defect)- Failure (non detection of an existing defect)

In harsh environmentBad detections exist(detection of a non existing defect, non detection of an existing defect)


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IMdR 09 Cachan, France 13 octobre 2009 08/06/2012

Zone immerge

Zone de marnage

Sol de fondation

Zone arienne

Zone dclaboussures

Zone de boues

Zone basses eaux

Corrosion

Error of measurements = f(z)

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E1

Decision

Corrosion

No corrosion

E2

E3E4

4 conditional probabilities:- P4: probability of presence of corrosion, conditional to defect detection- P3: probability of presence of corrosion, conditional to no corrosion detection- P2: probability of no presence of corrosion, conditional to corrosion detection- P1: probability of no presence of corrosion, conditional to no corrosion detection

: probability of defect presence (from expert judgment for instance)

Introduction of inspection in decision process

Good decisions

Failure

Useless repair

E4

P(X=1) = ; P(X=0) = 1-

[Rouhan & Schoefs, 2003]


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Ultra-Sonic measurements on steel structures

Three cardinal pointsUncertainty on measurements

Loss of thickness

Statistical analysis

Signal + Noise

Error (Noise)Assumption (expert judgement):Real value = mean value

(no systematic bias)

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[Boero & Schoefs, 2009-2011]

P3: Brushing of the rust

P2: Grinding of the rust without quality control

P1: Grinding of the rust with quality control

CostFailure 1.0000Repair 0.0100Inspection Practice 1 (P1) 0.0025Inspection Practice 2 (P2) 0.0020Inspection Practice 3 (P3) 0.0010

Error


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[Boero & Schoefs, 2009-2011]Corrosion in the mud zone after 25 years

[Schoefs et al., 2010]

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[Boero & Schoefs, 2009-2011]

Knowledge of is essential

Significant discrepancies

Fair discrepancies


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Repair efficiency

Ranking of 5 main coating products performance

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Fixing (4 months) Paint 1 Paint 2 Paint 3 Paint 4 Paint 5

Visual aspect(10 months) Paint 1 Paint 2 Paint 3 Paint 4 Paint 5

Porosity (10 months) Paint 1 Paint 2 Paint 3 Paint 4 Paint 5

Pb : head of pile

Zinc polyurethane Mono-component + mixed resin polyurethane + hydrocarbon

Epoxy coatingEpoxy-polyamide or polyester coating + flakeglass

------- : Bad

------- : Medium

------- : Good

Results of EC Interreg project (2007))

3 lim

it st

ates

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Conclusion

A lot of existing models able fit the trend: the propagation of uncertainties is another challenge

Need for future connections between models: phenomenological and probabilistic

Gather data: duratiNet data base => key long term factors (propagation of uncertainties) First year monitoring

More data from various structures/environment to understand the key factors for LT modelling

Resilient questions:Updating models when errors of measurements occurTo know more: publications in Struct Safety, CACAIE,

Paralia, NSIE


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Thanks to

Medachs Project (EC Interreg project 2005-2009)

Gerom Project(French Collaborative project 2006-2010)

Oxand society (http://www.oxand.com/), PhDs, Msc

08/06/2012 41http://www.nantes.fr/


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