# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 3 Corrosion Pitting Evaluation
### Degree of Pitting Corrosion  
It is important to find an index reflecting the corrosive environment. On this end, the pitting current \(I_{p}\) could be utilized since it only dependents on the corrosive environment. Thus, the pitting current has been explicitly expressed by rewriting Equation (4), as shown in Equation (6):  
\[I_{p}=\frac{a^{3}}{t^{3}B^{3}m_{asp}^{2}}. \tag{6}\]  
The code ISO 9223 [41] classified the corrosive environment into six categories, named C1, C2, C3, C4, C5 and CX. A series of experiments has been carried out on the corrosion of weathering steel at various places representing different corrosive environment categories in China [42]. Five key indices are adopted to reflect various corrosive environments, and the value of each index corresponding to five corrosive environment categories are statistically obtained based on 8 years of data (from 2006 to 2014), as listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Location (Category)**} & \multirow{2}{*}{\begin{tabular}{c} **Average** \\ **Corrosion Rate** \\ **(\(\upmu\)/a)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Average Pit** \\ **Depth (\(\upmu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximal Pit** \\ **Depth (\(\upmu\)m)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Average** \\ **Pitting** \\ **Current (C/s)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Maximal** \\ **Pitting** \\ **Current (C/s)** \\ \end{tabular} } \\ \hline Wuhan (C4) & 9 & 130 & 150 & \(4.93\times 10^{-10}\) & \(7.58\times 10^{-10}\) \\ Beijing (C3) & 8 & 140 & 180 & \(6.16\times 10^{-10}\) & \(1.31\times 10^{-9}\) \\ Jiangjin (C5) & 18 & 280 & 390 & \(4.93\times 10^{-9}\) & \(1.33\times 10^{-8}\) \\ Lhasa (C1) & 1 & 20 & 30 & \(1.80\times 10^{-12}\) & \(6.06\times 10^{-12}\) \\ Mohe (C2) & 3.5 & 20 & 40 & \(1.80\times 10^{-12}\) & \(1.44\times 10^{-11}\) \\ Xishuangabana (C2) & 9 & 70 & 110 & \(7.70\times 10^{-11}\) & \(5.48\times 10^{-9}\) \\ Shenyang (C2) & 8 & 100 & 180 & \(2.25\times 10^{-10}\) & \(3.51\times 10^{-9}\) \\ Qingdao (C4) & 18 & 240 & 360 & \(3.10\times 10^{-9}\) & \(1.05\times 10^{-8}\) \\ Guangzhou (C4) & 15.5 & 230 & 290 & \(2.73\times 10^{-9}\) & \(5.48\times 10^{-9}\) \\ Qionghai (C3) & 10 & 210 & 250 & \(2.08\times 10^{-9}\) & \(3.51\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pitting corrosion data of weathering steel collected for 8 years.  
The pitting current in Table 1 is plotted against the corrosivity category in Figure 3, along with a brief description of the geographic feature, i.e., the marine or non-marine. The following letters \"-M\" and \"-A\" stand for the maximal and average value of pitting current, respectively.  
It can be observed from Figure 3 that the corrosivity category was directly proportional to the pitting current. Meanwhile, the result also indicates that the average pitting current was remarkably higher in the marine location than in the non-marine location, i.e., 3.38-5.92 times higher. Besides, the ratio of the max value to average value was highly scattered in different places, ranging from 53.6%-700%. It is worth noting that, the most critical place Jiangjin (C5) was not in the marine location. However, this place is a historic industrial area with heavy pollution of sulfur dioxide (SO2), which accelerates the corrosion significantly. In sum, the rate of pitting corrosion was dependent on both the corrosivity category and chemical factor, while the latter could have a larger influence.