PSI - Issue 13

Vit Krivy et al. / Procedia Structural Integrity 13 (2018) 825–830 Vit Krivy, Monika Kubzova, Katerina Kreislova, Martin Krejsa /Structural Integrity Procedia 00 (2018) 000 – 000

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= 1,77 ∙ 0,52 ∙ (0,02∙ ) ∙ + 0,102 ∙ 0,62 ∙ (0,033∙ +0,04∙ ) (2) where function is in form = 0,15 ∙ ( − 10) in casual that the temperature of air is smaller than 10° C, otherwise it is = −0,054 ∙ ( − 10) ; P d is average annual deposition rate of SO 2 [mg/(m 2 ·d)]; S d is average annual deposition rate of Cl - [mg/(m 2 ·d)]; T is average annual temperature [°C] and RH is average annual relative humidity [%]. 2.2. Prediction of corrosion losses according program UN/ECE ICP The UN/ECE ICP project was implemented in years 1987-1995, Kreislova et al. (2017). According to this program, the corrosion loss r corr of weathering steel can be estimated using the analytical relationship: = 34 ∙ 0,33 ∙ 0,02∙ ∙ (3) where function in in form = 0,059 ∙ ( − 10) in casual that the temperature of air is smaller than 10° C, otherwise it is = −0,036 ∙ ( − 10) ; P d is average annual deposition rate SO 2 [mg/(m 2 ·d)]; T is average annual temperature [°C] and RH is average annual relative humidity [%]. This analytical relationship takes into account only three environmental parameters; the relation does not take into account the possible influence of chlorides on the corrosion behavior of the steel. 2.3. Prediction of corrosion losses according program Multi-Assess Under this program, corrosion samples were placed in 50 tested sites in Europe, see Multi-Assess (2017). The samples were exposed from 1970 to 2005. Analytical relations derived from this corrosion loss estimation program take into account much more environmental parameters than the above equations. The experimental test period already includes a significant decrease in the major corrosion stimulator in air. According to this program, the corrosion loss r corr of carbon steel (or weathering steel) can be estimated using the following equation: = 29,1 + {21,7 + 1,39 ∙ 0,6 ∙ 60 ∙ + 1,29 ∙ ∙ [ + ] + 0,593 ∙ 10 } (4) where function is in the form = 0,15 ∙ ( − 10) in casual that the temperature of air is smaller than 10°C, otherwise it is = −0,054 ∙ ( − 10) ; P d is average annual deposition rate of SO 2 [mg/(m 2 ·d)]; S d is average annual deposition rate of Cl - [mg/(m 2 ·d)]; RAIN is average annual precipitation [mm]; RH 60 is average annual relative humidity [%]; H + is hydrogen ion concentration in precipitation [mg/l] and PM 10 is average annual concentration of dusty deposits (max. diameter 10 µm) [µg.m -3 ]. Prediction models for estimating corrosion loss after one year of exposure of metal are influenced by the annual average values of environmental parameters. These values can be obtained by long-term measurements or from available databases of hydrometeorological institutes, e.g. from online databases of the Czech Hydrometeorological Institute. For the comparison of exposures and obtained values of corrosion loss, two locations in the Czech Republic, Kopisty and Ostrava-Poruba, were selected. Locality Kopisty can be classified as industrial environment with corrosivity category C3. Locality Ostrava-Poruba is urban environment with corrosivity category C2 to C3. The climatic parameters in the Kopisty test site have been monitored for a long time already since 1969. In the case of Ostrava-Poruba test site, environmental data were obtained from databases accessible online at the Czech Hydrometeorological Institute portal. In the online database, there are environmental parameters for this site since 1997. The creation of input histograms for prediction models is further explained on the example of statistical processing of average annual temperatures in Kopisty test site. 3. Statistical analysis of long-term monitored environmental parameters