An analysis architecture is built to make full use of natural environment corrosion of materials data. All environmental factors, such as average temperature, average relative humidity, rainfall hours, sunshine duration, chloride ion settlement, etc. in nine atmospheric test stations, including Beijing, Wuhan, Jiangjin, and Wanning, and seawater temperature, salinity, dissolved oxygen, pH values, etc. in four marine test stations, including Qingdao, Zhoushan, Xiamen, and Yulin, are modelled monthly using a statistical analysis technique. Normal distribution, logarithmic normal distribution, Weibull distribution, and uniform distribution of the samples are checked by the Shapiro-Wilk and Kolmogorov-Smirnov methods. Subsequently, the probability distribution function, the expected population value and variance, and the confidence interval are estimated. Corrosion data for twenty-two kinds of carbon steels, low alloy steels, and stainless steels, including A3, 3C, 16Mn, 10CrMoAl, 1Cr18Ni9Ti, in atmospheric and marine test stations are analyzed. The main environmental factors for atmospheric and marine corrosion of each type of steel are determined by grey relational analysis. Subsequently, a predictive model incorporating the main corrosion environmental factors, exposure time, and corrosion rate is built by the BP artificial neural network. After the evolution of corrosion pit depth is predicted by the artificial neural network, a fracture mechanics calculation is used to evaluate the residual life of a structure with corrosion pit defects.

Corrosion is very harmful to the useful lifetime of materials. It can bring enormous economic losses and severe dangers to society. Natural environment corrosion is one of the most common corrosion phenomena. It is very complex and has a vast set of factors responsible for its existence. Natural environment corrosion includes mainly atmospheric corrosion and marine corrosion.

Atmospheric corrosion is defined as the corrosion of materials exposed to the air and its pollutants. Electrochemical corrosion and chemical corrosion can occur in the atmospheric environment. However, electrochemical corrosion is more important than chemical corrosion in atmospheric corrosion (

A large amount of data concerning the natural environment corrosion of materials has accumulated over the last 16 years through research supported by the Major Program of the National Natural Science Foundation of China (

The framework for the module of statistical analysis modelling for environmental factors is shown in Figure

Framework for the module of statistical analysis modelling for environmental factors.

The atmospheric environment database involves seventeen kinds of atmospheric environmental factors: average temperature, average relative humidity, rainfall, rainfall hours, sunshine duration, SO_{2} settlement, NO_{2} settlement, H_{2}S settlement, HCl settlement, SO_{3} settlement, Cl^{-} settlement, NH_{3} settlement, dustfall of water-solubility, dustfall of water-insolubility, pH value of rainwater, and the SO_{4} and Cl^{-} content of rainwater at the nine atmospheric test stations, Beijing, Wuhan, Jiangjin, Wanning, Qingdao, Guangzhou, Shenyang, Qionghai, and Hailar. The marine environment database involves nine kinds of marine environmental factors: seawater temperature, salinity, dissolved oxygen, pH value, temperature of the marine atmosphere, humidity, rainfall, sunshine, and wind speed at the four marine test stations, Qingdao, Zhoushan, Xiamen, and Yulin.

In the statistical analysis module, the normal distribution and the logarithmic normal distribution for the sample are checked by the W method. The W test is a kind of small sample test (3 ≤ n ≤ 50) that can only check the normal distribution (

where n is the sample number, k = n/2 when n is an even number or k = (n-1)/2 when n is an odd number, µ is the mean of the sample, x_{i} is the value of the i^{th} sample, and a_{i} is the i^{th} constant. If W > W_{α}, the sample follows normal distribution, W_{α} is the quantile of the W test, and α is the level of significance. In order to check the logarithmic normal distribution, the sample needs a logarithm conversion before Eq. (1) is used.

The probability density functions of the normal and logarithmic normal distributions are shown in Eq. (2)

where σ is the standard deviation of the sample. For normal distribution, the estimate of expected value of population E(x) = µ and the estimate of variance of population D(x)= σ^{2}. For the logarithmic normal distribution, the estimate of expected value of population ^{2}/2) and the estimate of variance of population ^{2}) – 1) exp(2^{2}).

The K-S test is a type of universal test. It is used to check normal and nonnormal distributions (_{0}(x) is the probability distribution function of the theoretical distribution and F_{n}(x) is the cumulative frequency function of the current sample,

If D < D_{α}, the sample follows the current theoretical distribution, D_{α} is the critical value of the W test, and α is the level of significance.

The probability density functions of the Weibull and uniform distributions are shown in Eq. (4)

where λ > 0 is the scale parameter and k > 0 is the shape parameter for the Weibull distribution. The parameters are estimated by the probability weighted moment method (

where Г is the gamma function, the estimate of expected value of population ^{2}Г(1+2/^{2}. For uniform distribution, a and b are the interval parameters. The estimate of expected value of population ^{2}/12.

The confidence interval is a measure of the reliability of an interval estimate for population parameter (_{1}, θ_{2}] exists, if p{_{1} ≤ _{2}} = 1 – _{1}, θ_{2}] is the confidence interval of θ at the confidence level 1-α. For a small sample n < 30 following the normal distribution, the estimate of confidence interval for the expected value of population is ^{2} is known and unknown respectively. For a large sample, the estimate of confidence interval for the expected value of population is _{α/2} is the bilateral quantile of the standard normal distribution at the level of significance α, t_{α/2,n-1} is the bilateral quantile of the t-distribution at the degree of freedom n, χ^{2}_{α/2,n-1} is the bilateral quantile of the Chi-square distribution.

The framework for the module of the assessment of corrosion damage is shown in Figure

Framework for the module of the assessment of corrosion damage.

The atmospheric corrosion database involves the average corrosion rate over a period of 16 years for seventeen kinds of carbon steels and low alloy steels: A3, 3C, 20, 08Al, 16Mn, 16MnQ, D36, 15MnMoVN, 14MnMoNbB, 09MnNb, 09CuPTiRE, 10CrMoAl, 10CrCuSiV, 12CrMnCu, 09CuPCrNi, 09CuPCrNiA, 06CuPCrNiMo and five kinds of stainless steels: 2Cr13, 00Cr17AlTi, 1Cr18Ni9Ti, 00Cr19Ni10, 0000Cr18Mo2 in the atmospheric test stations. The marine corrosion database involves the average corrosion rate, the average pit depth, the maximum pit depth, and the maximum crevice corrosion depth over a period of 16 years for these steels in the marine test stations.

Many environmental factors can affect the corrosion processes. Determining the main corrosion environmental factors is very meaningful. It can simplify the corrosion damage modelling and improve the model’s reliability. In the corrosion assessment module, the grey relational analysis is used to obtain the main environmental factors of atmospheric and marine corrosion for each carbon steel, low alloy steel, and stainless steel. The grey system is a system in which some of its information is clear and some is not clear. The grey relation is the uncertainty of the association between things or the uncertainty of the association between system factors and the main behavioural factors (

where r_{i} is the grey relational grade, ξ_{0i} is the grey relational coefficient, δ is the resolution coefficient, _{0}^{'}(k) and X_{i}^{’}(k) are the reference sequence and the comparative sequence by standardization treatment

The artificial neural network (ANN) is an information processing model that is inspired by biological nervous systems. It is composed of a large number of highly interconnected neurons. It can learn correlative patterns between input and output information without specific models and can use that learned association to predict the appropriate output for input data not used in training (

where ^{th} node of ^{th} node of ^{th} node of

In order to check the validity of the ANN algorithm, the corrosion data for A3 steel in the marine splash zone is used as an example for analysis. The number of nodes in the hidden layer, the learning rate, and the momentum coefficient are set as 4, 0.3, and 0.9 respectively. It can be seen in Figure

Fracture mechanics is a method for predicting the failure of a structure containing cracks. It uses analytical solid mechanics methods to calculate the driving force on cracks and experimental solid mechanics methods to characterize the material resistance to fracture (

where _{I}_{y} is the yield strength, α is the equivalent crack length, d is the corrosion pit depth, and L is the plate thickness.

Suppose that the corrosion pit depth is d_{1} at current time t_{1}. When the corrosion pit depth is d_{2} at time t_{2}, _{I2}_{IC}_{I2}_{2} and _{IC}_{2} is the critical corrosion pit depth. The residual life of the structure is equal to t_{2}-t_{1}.

This paper introduces the overall architecture for the analysis of natural environment corrosion. Atmospheric and marine environmental data are analyzed by a statistical method. The W method’s reliability in checking the normal distribution for a small sample is higher than that of the K-S method; but the W method is a special method to check normal distribution. It does not check any other distributions. The K-S test is a kind of universal test. It can check any distribution as long as the distribution function is known. Although the expected values and variances of the population can be estimated by the distribution function, a more reliable estimate is given by the confidence interval at different confidence levels. For atmospheric and marine corrosion data, the main corrosion environmental factors are confirmed by grey relational analysis. These factors are the input variables for the BP artificial neural network. This can simplify the BP artificial neural network modelling and improve the reliability of the model. After the relationship between corrosion pit depth and time is predicted by the BP network, the residual life of the structure can be obtained by a fracture mechanics calculation on the critical corrosion pit depth.

This work was supported by the National Science and Technology Major Project (No. 2011ZX06004-009).