# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 4 Infrared Method  
The infrared (IR) method is widely used in the civil engineering field in order to locate the weak spots or deterioration of structures, which are caused by cracks or corrosion [2]. The working principle of the IR method utilizes the measurement of the temperature changes on the surface of structures caused by compression or tension. The method uses specialized scanning cameras to capture the emitted heat at any temperature and convert the data into thermal images for further analysis. Concrete structures with defects, namely debonding render and mosaic or delaminating concrete, emits infra-red radiation, which can be seen by the infra-red cameras as the concrete surface will heat up faster under solar irradiation, and the generated hot spots will be visible in the thermal record. The uniformity of the captured IR images of surfaces with even color and texture indicates that the structure has no or insignificant defects [4]. This method has been found effective for the rapid assessment of large structures, namely, high-rise apartment blocks. The advances in the development of high-end IR cameras can potentially enhance the reliability, accuracy, and cost-effectiveness of the technique [24]. Recent advancements of this technology have focused on the development of the automated data processing and analysis methods and their deployment onto various platforms (e.g., UAV). [PERSON] et al. [29] proposed a new methodology for the enhancement of the usability and efficiency of IR thermography for subsurface detection of damages on concrete structures. The aim of this investigation was to address a challenge with interpretation and detection of delamination from IR images as setting up the temperature too high or too low might affect the identification of some defects. A finite element (FE) model was designed and simulated using COMSOL software in order to obtain a threshold for IR thermography data processing. Two thresholds were obtained for this investigation, namely sound and delaminated areas from the IR image and FE model, respectively. In this study, various sensitive parameters and factors to validate FE models, namely the size of delamination (area, thickness and volume), ambient temperature and solar irradiance conditions (different season), and the depth of delamination from the surface. The validation of the FE model demonstrated the potential of FEM analysis in the detectability within a temperature difference range of approximately \(\pm\)0.3-0.4 \({}^{\circ}\)C band between delaminated and sound areas. The undertaken FEM simulation provides the possibility to estimate a detectable delamination depth by IRT prior to field inspection, which in turn will save time and improve the efficiency of the measurement and detection procedure. The infrared measurement technique has also been combined with other measurement methods, which is a common practice in SHM. However, hybrid methods require highly skilled specialists in multiple disciplines in order to interpret and/or analyze obtained data. In recent years, artificial intelligence has become an important part of the interpretation of complex data. [PERSON] et al. [30] proposed the use of deep machine learning to automate the concrete crack detection process. In this study, a hybrid image scanning (HIS) system (see Figure 4) comprised of vision and infrared (IR) thermography was used. The system was controlled via a bespoke LabVIEW program, and the obtained data were processed using MATLAB. The working principle of the system is initiated via the control computer, which activates the continuous-wave (CW) laser to emit the point laser beam. The laser beam is then transformed into a line-shaped laser beam that is focused on a measured surface. In addition, the vision and IR cameras are used to acquire the surface condition and thermal waves responses. The scanned data is automatically saved in the control computer.