# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 8 Acoustic Emission Technique
### Concrete Structures  
Crack monitoring is crucial for the safety and maintenance of concrete structures and AE is attractive as an NDT technique as it can detect and locate in real-time damage in concrete structures. The application of AE in reinforced concrete (RC) structures in order to assess damage due to fatigue loading were presented by [PERSON] [81], concluding that the AE analyses (such as AE parameter, intensity analysis etc.) are limited, whereas the application of AE in structures with increasing fatigue magnitude needs further consideration. In order to overcome dispersion and attenuation issues of elastic wave based signals that can be a crucial problem in heterogenous materials, [PERSON] and [PERSON] [79] suggested a 3d parameter correction technique. Their proposal was assessed on the basis of mechanical observations from experimental testing of concrete beams. Artificial sources of 50 kHz frequency were generated on each of the 48 attached nodes by a signal generator and recorded by eight AE sensors attached to the beam. The relationship between the generated and recorded amplitude data were used for the proposal of correction AE parameters. In addition to RC concrete structures, the efficiency of AE for different types of concrete structures has been evaluated. [PERSON] et al. [82] used AE in order to study the crack development in lightweight cellular concrete and to assess the effect of fibre reinforcement on the fracture behaviour of these structures (see Figure 12a). Two types of sensors covering a wide range of frequency were attached at predefined locations of beam specimens, and the signal measurements were recorded. On the basis of tests results, the AE hits accumulation was correlated with the crack initiation and propagation characteristics. Appropriate AE sensor types are important in the fracture monitoring of concrete structures, and since concrete is a highly attenuating material, lower frequency sensors are most suitable. [PERSON] et al. [83] applied two AE sensors together with multichannel monitoring and operating in order to study cracking in crumb rubber concrete, showing that AE can allow for a  
Figure 11: Acoustic emission principle: (**a**) AE sensor and medium; (**b**) AE parameters [79]. good understanding of micro-cracking and fracture monitoring. Prefabricated structural elements were assessed by [84] and AE features provided information for concrete cracking, bar debonding and bar plastic deformation. Laboratory-based bending tests of grouted connections for offshore wind turbines were carried out by [PERSON] et al. [85]. Four AE resonant transducers were installed at the bottom and top of the connections for real-time monitoring through a data acquisition system. The signals were amplified using pre-amplifiers, and dB thresholds were set to eliminate background interference. The released AE energy and counts were correlated with load-deflection data in order to identify damage evolution and grout cracking; further to cracking, corrosion of RC structures is one of the major issues of structural failure that early detection could help limit the location and extent of repair and all associated costs. [PERSON] et al. [24] proposed AE as an efficient technique in order to detect corrosion in real-time. Raw data obtained from structural monitoring system is nearly useless without reinterpretation, and hence neural network and machine learning techniques can be applied for better understanding. A correlation between AE parameters and the structural performance and design strength requirement through a deep neural network was proposed by [PERSON] and [PERSON] [86]. The method was implemented on two bridges, demonstrating its potential for real-world applications. AE technique was used in order to characterise the source of the mechanism of events with different energy dissipation levels. This classification was based on experimental findings during the fracture of fibre reinforced concrete and was carried out via neural network analysis [87].