## 2 Unburied Pipe Tests
### Results and Discussion  
The averaged AE parameters taken into account for this analysis, namely Hits, Cumulative Counts, and Cumulative Amplitude, are reported in Figure 4 for all the tested conditions. The measurements exhibited an acceptable repeatability, with standard deviation generally remaining below 15% of the mean value.  
Figure 4: AE parameters: (**a**) total Hits; (**b**) Cumulative Counts; (**c**) Cumulative Amplitude. _NL_, no leak; _Lk-1_, first leak (1 m); _Lk-2_, second leak (3 m); _Lk-3_, third leak (8 m); _Lk-123_, all leaks.  
Some AE events can be observed for the non-leaking state. Since for this condition the flow in the test bed is null, such events are due to external disturbances.  
Data concerning the conditions _Lk-2_ and _Lk-3_ (i.e., the most distant leaks) exhibit low signal levels, reasonably due to significant attenuation of the high frequencies in the plastic pipe. Consequently, the corresponding acquisitions appear characterized by burst signals rather than continuous ones.  
As for the leak detection performance, all the leaking conditions are clearly revealed by an increment in all the monitored AE parameters. As expected, the increment is notable when the nearest leak is active (conditions _Lk-1_ and _Lk-123_), whereas it rapidly decreases as the leak distance grows. The trend of the Cumulative Counts is also consistent with the variation of the leaking flow rate. Indeed, more Counts are detected when all the leaks are active. Conversely, the condition _Lk-123_ shows less Hits than _Lk-1_. This anomaly is reasonably ascribable to the higher average signal level characterizing the former condition (26 db instead of 24 db), which affects the identification of AE events in the continuous signals (since the timing parameters are kept unaltered). The same behavior characterizes the Cumulative Amplitude as well, since it partially depends on the number of Hits.  
The percentage variation of the parameters in the leaking conditions, with respect to the non-leaking state, was computed to better compare their sensitivity to leaks (Figure 5). The values related to the Cumulative Counts are the highest for all the leaking conditions, and in particular for the most distant leak (_Lk-3_). Hence, the Cumulative Counts seem potentially more suitable to assess the presence of active leaks.  
The distribution of the AF over the RA values is also computed for all the tested conditions. Figure 6 reports the comparison between conditions _NL_ and _Lk-3_, shown as an example. Apparently, the leaking condition exhibits a larger percentage of _tensile-type_ AE events; i.e., it is characterized by  
Figure 5: Percentage variation of AE parameters: (**a**) total Hits; (**b**) Cumulative Counts; (**c**) Cumulative Amplitude.  
higher values of the AF to RA ratio (as opposed to _shear-type_ AE events, which feature lower ratios). This behavior is consistent with the result obtained in [17], and permits the detection of the leak.  
Hence, these preliminary tests showed that both the monitoring of Cumulative Counts and the analysis of the AF vs. RA pattern may be suitable for detecting leaks by means of AE measurements. Nonetheless, further data and analyses are required to confirm the effectiveness and the convenience of these tools for leak detection purpose, as well as to achieve a satisfactory degree of confidence in the detection, in particular, for distant leaks.