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Corrosion Mapping And Modelling
GS Trinidad &IS Cole
CSIRO Building, Construction and Engineering, Victoria, Australia
Summary: This paper describes possible approaches to mapping and modelling corrosion, namely:
generalised tables and graphs, regression modelling, use of artificial neural network, and process
simulation. The paper briefly presents advantages and disadvantages of each method. It does not,
however, recommend any particular method.
The paper also describes the impacts of emerging technologies such as geographic information
systems and the Internet that have allowed more efficient modelling, and information delivery of
corrosion maps and other related information. It also describes a WWW-enabled corrosion
mapping system that has resulted from CSIRO corrosion mapping and modelling work.
Keywords. Corrosion maps, geographic information systems, World Wide Web applications.
1
INTRODUCTION -CORROSION MAPPING
Corrosion maps are traditionally constructed using two basic methods. Both methods are based on field observations from a set
of sites distributed within the study area, and located in such a way that they are representative of the variability of the
corrosion rates within the study area.
If enough data points are available, the most straightforward and accurate approach is to construct a surface model of the
corrosion rate (e.g. triangulated irregular network, grid, contour lines). In this approach, arranging the sites in some sort of grid
pattern is desirable. This approach was used in producing several corrosion maps for Melbourne (see Fig. 1), Newcastle, The
Hunter Valley and South Australia. Unfortunately, this approach is very costly both in terms of financial cost and time. This
cost constraint is a significant drawback in the production of any wide-area corrosion maps.
If there are not enough data points to adequately cover the spatial extent of the study area, an alternative approach is to
construct a mathematical model, either based on statistical methods, artificial intelligence techniques or process simulation, or
combinations thereof. Selecting the set of input variables to the corrosion model is difficult and could vary from one case to
another. For example, estimating corrosion using only distance from the coast, implicitly assumes that air pollution is not
relevant (see Fig. 2). This implicit assumption has resulted in the underestimation of corrosion in industrial areas (see Fig. 3).
Hence, parametric models tend to vary in form from one area to another.
Figure 1. Observed corrosion of galvanised steel in south-east Melbourne, 1983-85 (in µm per year).
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Figure 2. Estimated corrosion of galvanised steel based on distance from the coast (in µm per year).
Figure 3. Estimated corrosion of galvanised steel based on distance from the coast and SO2 level.
2
GIS–BASED CORROSION MODELLING
The emergence of geographic information systems (GIS) makes it possible to construct corrosion models quicker and cheaper.
A GIS allows:
• Various data sets from different sources to be integrated into one unified framework.
• An incremental and interactive (possibly visual) process of building the corrosion model.
A corrosion model based on both statistical and process simulation was developed at CSIRO Building, Construction and
Engineering. This model was based on the following simple assumptions:
•
Corrosion is influenced by two basic factors – salinity and moisture (time of wetness).
•
If industrial pollution is neglected, then distance from the coast, topography, prevailing winds and the type of coast (surf
or bay) will influence salinity levels.
• Moisture is basically influenced by climatic parameters such as relative humidity and temperature.
The ‘time of wetness’ (TOW), or the time a metal surface is assumed to be wet, is computed from relative humidity and
temperature records in 136 Bureau of Meteorology sites using a method suggested by the International Standards Organization.
An Australia-wide surface model of TOW is constructed from the 136 values using a geo-statistical technique called ‘Kriging’.
Figure 4 illustrates the resulting surface model for the TOW.
The corrosion rate in each of the 14,700+ localities (towns or suburbs) in Australia is estimated by:
•
•
Representing each locality as a point defined by a pair of coordinates (longitude, latitude).
TOW at each locality is estimated from the surface model shown in Fig. 4.
•
The amount of salt transported to each locality from the coast is simulated (see Fig. 5).
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•
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A surface model of corrosion rate is derived from the corrosion estimates in the 14,700+ localities (shown in Fig. 6).
CSIRO WEB-BASED CORROSION INFORMATION SYSTEM
The rapid acceptance and increased accessibility of the Internet paved the way to a new method of delivering information. The
CSIRO corrosion information system is Web-enabled, as expected of most information systems today. The corrosion
information system is a component of a wider CSIRO initiative, which is the Build Information Exchange (BIEX) Internet
portal (see Fig. 7).
The resulting corrosion maps and model are delivered through the Internet in a cost-effective manner (shown in Figs 8 and 9).
It is also possible to value-add to these models and maps by coupling them with service life estimation and material selection
applications (shown in Figs 10 and 11).
Figure 4. Estimated TOW in Australia based on meteorological data.
Figure 5. Simulated salt deposition Australia-wide using meteorological and coastal data.
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Figure 6. Corrosion map for copper steel derived using geo-statistical modelling.
Figure 7. CSIRO Build Information Exchange Internet portal
(http://www.dbce.csiro.au/biex/main.cfm)
Figure 8. The corrosion mapping system incorporated with the Industrial Galvanisers Corporation Website.
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Figure 9. The corrosion mapping system embedded within the BIEX portal.
Figure 10. Dialog box allowing user to define the failure criteria.
Figure 11. Estimated service life for selected materials from user-defined failure criteria.
4
OTHER WEB–BASED CORROSION INFORMATION
There are several corrosion-oriented Internet portals. One of them is Corrosion Source, a site that is essentially a marketplace
where one could buy software tools, among other corrosion-related products and services (http://www.corrosionsource.com/).
Another example is an Java-based corrosion rate and service life calculator, jointly developed by the International Lead Zinc
Research Organization and Cominco, which allows the user to estimate the corrosion rate of zinc by nominating parameters on
rainfall, salt deposition, sulfur dioxide levels, relative humidity, temperature and sheltering condition (see Fig. 12 and 13). The
site is based on Java technology and is hosted at http://www.fortjava.com/zclp/index.html.
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Figure 12. Corrosion rate and service life calculator for zinc coating.
Figure 13. Zinc coating life predictor report.
The third example is the Corrosion Doctors Resource Web portal, which contains several predefined (i.e. static) low-resolution
maps for China, India, Japan, New Zealand (see Fig. 14), North America and South Africa. The site is hosted at
http://www.corrosion-doctors.org/.
5
5.1
CORROSION PREDICTION MODELS
Generalised tables and graphs
Atmospheric corrosion rates of metals can significantly vary depending on the specific environmental conditions. The most
commonly used method for corrosion life estimation of galvanised steels has been the use of generalised values for the
different types of predefined environmental conditions. This method uses a generalised value in the form of tables and graphs
to represent the corrosion rates in each typical environment. An example of this method is the environmental corrosion
classification system that was developed by the International Standard Organization (ISO 1992).
The method provides a simplified estimation of corrosion rates. Unfortunately, it is only able to provide rough and non-specific
estimates. Hence, this non-specific approach is no longer adequate to meet the demands of the marketplace. For instance,
present designers and specifiers of galvanised steel increasingly ask for information on performance certainty. The ‘coating
guarantee program’ of the Industrial Galvanisers Corporation (IGC) is a typical example. IGC manages its own coating
guarantee projects, and provides design professionals with real-time corrosion data. This is becoming more important as
demand intensifies for reliable, long-term durability of steel construction products, and designers become more accountable for
material durability performance.
5.2
Geographic mapping method
In this method, corrosion rates of materials in a geographic area are based on field measurements in a grid of sites (King 1993;
Shaw 1976). It recognises the difficulty of using general corrosion rate predictions and attempts to estimate product service life
directly from field data. Thus, it is the most reliable method for the estimation of product life. However, its usefulness is
limited to the areas where such mapping is available, and there are relatively few regions of the world that have been suitably
mapped for this purpose.
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Figure 14. New Zealand corrosion map at the Corrosion Doctors Website.
The geographic mapping approach is fairly straightforward. Once, the corrosion map is complete, using it is as simple as
selecting a point on the map. Its main difficulty is in the logistics involved in setting up a grided network of field sites,
maintaining them, and analysing and assembling the results. This process is both time consuming and expensive.
5.3
Regression models
Regression models are derived from mathematical functions that are empirically formulated based on the statistical analysis of
historical data with respect to the relevant factors. Numerous studies have indicated that corrosion rates are strongly affected
by certain factors, such as TOW, sulfur dioxide and chloride concentrations in the air. Hence, it is not surprising that most
regression formulae use some if not all these variables. The following are some examples of such corrosion rate formulae.
C = a ln (RH) + ß ln (T) + ? ln (Salt) (Trinidad & Cole 2000)
–1
(1)
C = a ln (RH) + ß ln (T) + ? (DistanceFromCoast) (Trinidad & Cole 2000)
(2)
] C = a (SO2) + ß (Chloride) + ? (Wetness) + d (Knotkova et al. 1995)
(3)
C = [a (SO2) + ß (Chloride) + ?] Temp (Kucera et al. 1982)
(4)
Performing regression analysis is easy when the form of the regression function is known. Once the input data is assembled,
the analyst only needs to feed this data to regression software. Regression function is a standard feature on any statistical
software or even part of everyday office software (e.g. spreadsheet).
One of the problems with this approach is that the applicability of the resulting regression equation is somehow limited by the
coverage, variability and completeness of the input data. For example, Eq. (1) is suited to South East Asian regions with no
extensive mountain ranges. Hence, this equation may work well in Thailand or in Mindanao Island (Philippines), but would
perform poorly in Sumatra (Indonesia) and Luzon (Philippines).
The main difficulty in using the regression method is determining the form of the regression function. Consider the example of
generating a regression model that estimates the corrosion rate (C) given relative humidity (RH), temperature (T) and coastal
distance (D). The first step the analyst needs to take in this situation is to assume the form of the regression equation. For
instance, an analyst must first decide that the function will be of the form:
C = α (R) + β (T) + γ (D)0.5 + κ
Only then can the analyst feed the raw data into the regression analysis module, which is typically part of most statistical
software products. The difficult lies in determining the form of the function and identifying the set of variables that are best
suited. It is possible that for the problem at hand, the best regression equation is:
C = R (D)β à which reduces to à log (C) = log (R) + β log (D)
5.4
Artificial neural networks
The difficulty of determining the form of the regression function is avoided by using the neural network approach. A neural
network only needs to be given raw data related to the problem. It sorts through this information and produces an
understanding of the factors by defining the function, ƒ, such that:
C = ƒ (R, T, D)
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Artificial neural networks are the result of academic investigations that involve using mathematical formulations to model
nervous system operations. The resulting techniques are being successfully applied in a variety of applications. A neural
network can be used to learn patterns and relationships in data. Traditionally a programmer or analyst specifically ‘codes’
every facet of the problem in order for the computer to ‘understand’ the situation. Neural networks do not require the explicit
coding of the problem.
Unfortunately, this approach also suffers from several drawbacks. First, it is a ‘black box’ approach. It is difficult for the
analyst to understand the formulation process since this is done within the neural network software. Second, it also suffers
from the deficiency of regression modelling such as limitation of the applicability to particular environment, and the usual data
availability problem. Third, selecting a suitable neural network software product can be daunting to corrosion professionals.
The Google Internet search engine has an indexed holding of around 50 commercially available neural network software
products (www.google.com).
An example of a neural network application in corrosion modelling is the ILZRO-Cominco Corrosion Rate Calculator, which
was based on a neural network-based model developed by Zhang (2001).
5.5
Holistic approach
To avoid having to measure corrosion itself, Cole et al. (1999) proposed the ‘holistic’ approach. It proposed to simulate the
corrosion process itself, from the salt transport mechanism, particle deposition, film or surface formation, and other processes.
At its purest form, the holistic approach is extremely difficult if not impossible to achieve.
First, it is unlikely to have a complete understanding of the corrosion process. It may be possible to simulate some part of the
process under some known set of conditions. However, it may not be possible to simulate the entire process under all sets of
possible conditions. Second, even if one can completely understand and explain the entire corrosion process, it is
computationally impossible to build such a simulation system.
A more pragmatic approach is to simulate some of the corrosion process components and use other methods in other
components. This is the approach taken in the construction of the CSIRO on-line corrosion map. Table 1 illustrates the
methods that were used to model components of the corrosion process.
6
CONCLUSIONS
In conclusion, it should be noted that among the available modelling approaches, the geographic mapping method produces the
most accurate estimates and it is easy to use. However, its usefulness is limited to the areas where such mapping is available,
and there are relatively few regions of the world that have been suitably mapped for this purpose. The generalised method,
although simple, is no longer adequate to meet the current demands of today’s marketplace.
Table 1. An example of table layout
Component
Method used
Time of wetness
Generalised tables
Salt production
Generalised tables
Salt transport and deposition
Process simulation
Corrosion rate of copper steel
Process simulation
Corrosion rate of other materials
Regression model
Corrosion map production
Geo-statistical tessellations
The use of regression and neural network models shared the same advantages and disadvantages. Both methods tend to
produce more accurate estimates than the generalised approach and have a slightly wider applicability than the geographic
mapping approach. Regression models seem a better alternative when a small number of variables are used to estimate
corrosion. On the other hand, the use of neural networks is more advantageous if larger numbers of variables are used in
estimating corrosion.
The holistic approach, although theoretically sound, cannot be implemented with currently available knowledge and
technology. The more pragmatic hybrid approach does not give accurate results when compared to the geographic mapping
approach. It may be more widely applicable than both regression and neural net modelling, but it does not guarantee to produce
a better estimate.
It is evident that recent developments in information technology, especially in the area of geographic information systems,
artificial intelligence, Internet and interoperable database systems, have improved the corrosion mapping and modelling
process.
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7
ACKNOWLEDGMENTS
The authors would like to acknowledge the support of the metal industry in Australia, especially the Industrial
Galvanisers Corporation (IGC). The authors are also grateful to individuals who extended their support, and would
like to specifically mention John Robinson (IGC's Group Development Manager), Neil Wallace (BHP Research),
Jesse de Leon and Cheryl McNamara (CSIRO BCE Infrastructure Systems Engineering Group), George King and
Wan-Yee Chan (CSIRO BCE Sustainable Materials Engineering Group).
8
REFERENCES
1.
Cole I.S., King, G.A., Trinidad, G.S., Chan, W.Y. & Paterson, D.A. 1999, ‘An Australia-wide map of corrosivity: a GIS
approach’, Proc. 8th Int. Conf. on Durability of Building Materials and Components, Vancouver, Canada, 30 May to 3
June 1999, eds M.A. Lacasse & D.J. Vanier, NRC Research Press, Ottawa, vol. 2, pp. 901–911.
2.
International Standards Organization 1992, Corrosion of Metals and Alloys: Corrosivity of Atmospheres Classification,
ISO 9223, ISO, Geneva.
3.
King, G.A. 1993, ‘Corrosivity mapping – a sensitive and cost effective means of characterizing a region’s levels of
atmospheric corrosion’, Proc. CORROSION 93, NACE International, Houston, Texas, paper number 638.
4.
Knotkova, D., Boschek, P. & Kreislova, K., 1995, ‘Results of ISO CORRAG Program: processing of one-year data in
respect to corrosivity classification’, in ASTM STP 1239, eds W.W. Kirk & H.H. Lawson, ASTM, Philadelphia, p. 38.
5.
Kucera, V., Haagenrud, S., Atteraas, L. & Gullman, J. 1982, ‘Corrosion of steel and zinc in Scandinavia with respect to
the classification of the corrosivity of atmospheres’, in ASTM STP 965, Degradation of Metals in the Atmosphere, eds
S.W. Dean & T.S. Lee, p. 264.
6.
Shaw, T.R., 1976, ‘Corrosion map of the British Isles’, in Proceedings Atmospheric Factors Affecting the Corrosion of
Engineering Metals, ed. S.K. Coburn, ASTM Special Technical Publication, ASTM, Philadelphia, pp. 204-215.
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Trinidad, G.S. & Cole, I.S. 2000, ‘The role of GIS in modelling the performance of building materials’, in GIS and the
Built Environment, ed. S.E. Haagenrud, B. Rystedt & C. Sjostrom, CIB Report Publication No. 256.
8.
Zhang, X.G. 2001, The Zinc Coating Life Predictor, http://www.fortjava.com/zclp/Methodology.htm.
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