# Non-Destructive Testing Applications for Steel Bridges
## 4 New and Developing Methods
### Digital Image Processing Method  
The American Society for Testing and Materials (ASTM) [87] presents a guideline based on which an inspector shall be able to rate the corrosion for the inspected element. The key term for the measurement of the corrosion is the percentage of rusted surface, where a scale from 1 to 10 is incorporated (Table 1).  
The digital image processing method entails an intelligent system that can automatically detect areas with rust through the processing of the captured photos. Digital image processing is not only limited to steel bridges, but can also be incorporated in other areas, including underground pipelines, construction material, and pavement, due to its accuracy, consistency, and objectivity. [PERSON] and [PERSON] [88,89] presented two different rust defect recognition methods. Both methods start with the same step of converting the original image to a grayscale image. After the conversion to grayscale, where the grayscale is expressed using light intensities from 0 to 255 (0 is black, and 255 is white), one of two recognition methods are applied: (1) Neuro-Fuzzy Recognition Approach (NFRA) [88,90]; (2) Simplified K-Means Approach (SKMA).  
The ability to process digitized images can be obstructed due to different environmental conditions, low-contrast digital images, non-uniform illumination, and noises on painting surfaces. Unfavorable environmental and lighting conditions can lead to false indications since the lack of distinction exists between the rust pixels and the background. The unsatisfactory results are mainly due to the grayscale image processing. [PERSON] and [PERSON] [88] demonstrated that the implementation of color images for identifying the rust is feasible and more effective. The color space can be expressed as a 3D cube (Figure 31), representing the most fundamental color space with three primary colors, red, green, and blue (RGB).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Corrosion Rating** & **Description** & **Area to Be Painted** \\ \hline
10 & No rust, less than 0.01\% rust & 0 \\
9 & Minute rust, less than 0.03\% rust & 0 \\
8 & Few isolated rust spots, less than 0.1\% rust & 0 \\
7 & Less than 0.3\% rust & 0 \\
6 & Extensive rust spots, less than 1\% rust & 8 \\
5 & Less than 3\% rust & 18 \\
4 & Less than 10\% rust & 40 \\
3 & Approximately 1/6 of surface rusted & 60 \\
2 & Approximately 1/3 of surface rusted & 100 \\
1 & Approximately 1/2 of surface rusted & 100 \\
0 & Approximately 100\% of surface rusted & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Corrosion performance rating based on ASTM [87].  
Figure 30: The methodology for the vision-based displacement measurement technique [86].  
[PERSON] et al. [89] utilized color space for statistical data acquisition and multivariate statistical analysis to detect rust on steel bridges. [PERSON] et al. [91] further developed color image processing by including image texture and color descriptions to fully describe the roughness and common color changes on metal surfaces. [PERSON] et al. [92] used Fourier transform and texture features to model a Fourier Transform-based Defect Detection Approach (FT-DEDA) that is applicable to various environmental conditions. This method can process images of steel bridge coatings with various background colors, including brown and red colors that were not applicable for other similar methods. Another rust detection method known as RUDERM has been developed to address the limitation of the FT-DEDA method to distinguish rust from other types of defects. RUDERM incorporates the collected rust color spectrum in the RGB color space to develop a rust color range used to differentiate various types of defects. Figure 32 presents a comparison between the K-means method and RUDERM incorporated with K-means [92]. The comparison clearly shows that combining K-means and RUDERM methods results in a significantly higher accuracy than the use of K-means method alone in a shorter time.