## 4 Image Fusion Applications in NDT Inspection
### Learning-Based Approaches
#### 4.5.2 Fuzzy Sets
Lastly, the study does not contain any mathematical evaluation method, although it had sufficient discussion on the reasons for data acquisition issues. Table 7 demonstrates that the current studies on NDT image fusion have investigated aluminium materials and concrete structures so far. The scarcity of NDT combination attempts applied to composite materials also shows the potential for a number of different algorithms to be used in this area. Research on material properties is a different application field of NDT inspection technologies. Inspecting concrete characteristics and determining physical properties was proposed by the authors of [108]. Because of difficulties in understanding the porosity, water saturation rate, elastic modulus, compressive strength, depth of carbonisation and chloride content of concrete structures, a complementary fusion of non-destructive information might improve the reliability. For this purpose, a fuzzy set-based possibility theory was chosen to combine this heterogeneous information for controlled produced specimens with different compositions. The fusion results were compared with the destructive measurement and had several differences. It has been claimed that such a fusion technique can be helpful in understanding the conflicts of the NDT methods. This can make it possible to have a critical evaluation of the results and provides for the creation of a confidence index for inspected structures. The proposed method has been considered encouraging for the water saturation and porosity rates. However, it is problematic in estimating the elasticity value. With a similar purpose, the authors of [109] proposed stress measurement of an aluminium alloy material using fuzzy combinator-based fusion. It has been claimed that a fused result might have 50 percent less error than individual inspection outcomes. Even though the combination of NDT methods can give more deterministic clues regarding the mechanical properties of inspected structures, it is clear that these contemporary approaches are highly limited. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sources** & **Material** & **Defect Types** & **Aim** & **Reference** \\ \hline Tomography & Aluminum & Foreign body or holes & Shape characterisation & [106] \\ (3 techniques) & & & & \\ \hline Ultrasonic and eddy currents & Aluminum (AA5083-H111) & Weld imperfections & \begin{tabular}{c} To improve detection of \\ friction stir welding \\ imperfections \\ \end{tabular} & [107] \\ \hline Laboratory-based measurements & Concrete & Foreign body or voids & \begin{tabular}{c} To determine porosity, \\ content and mechanical \\ properties \\ \end{tabular} & [108] \\ \hline Pulsed eddy current and ultrasonic methods & Aluminum alloy (AL-2024) & Stress & \
\begin{tabular}{c} To determine stress \\ measurement \\ \end{tabular} & [109] \\ \hline Ground penetrating radar & & & & \\ and square pulse & Concrete & Foreign body or voids &
\begin{tabular}{c} To identify material \\ content and characteristics \\ \end{tabular} & [110] \\ thermography & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A summary of fuzzy set-based fusion approaches for NDT methods.