## 4 Conclusions  
After completing this work with two different technologies, the following conclusions may be drawn:  
* Acoustic emission by means of a non-contact optical microphone proved to be a useful tool for identifying damage at an early stage. Damage initiation was identified with  
Figure 21: Final condition of the structure after damage tolerance testing. Figure 20: Different locations strain along the whole tests during damage tolerance test. the presence of high-peak-frequency acoustic events after several cycles in accelerated and non-accelerated tests. * For damage evolution monitoring, the cumulative acoustic events curve showed its progression with three different distinguishable regions. The first related to damage initiation, followed by damage progression, and finally component rupture characterized by sudden increase in events. In this latter case, a sharp change in the acoustic event features was observed. * Among these regions, those ascribed to more severe damage (in particular phase 3, component rupture) presented high peak frequency values, indicating that this feature is relevant when assessing damage. Indeed, it is indicative of cracking phenomena. Based on this, several cracking phenomena occurred during phase 1 (damage initiation), while during phase 2 (damage progression), no high-peak-frequency events were found until component rupture commencement. * A classification method based on the Isolation Forest machine learning algorithm was developed for acoustic events classification. Time domain features were found to be the most relevant in this classification while frequency domain features were expected to play a more important role to have a clear physical explanation. It was proven that the main data variability source stemmed from different amplitude and energy level events, and this was properly captured by the machine learning algorithm. However, distinguishing cracking events from other source noises should involve frequency domain features. This may be hidden in a lower variability principal component and requires further data analysis. Further research will be carried out in future by applying machine learning algorithms to frequency domain features and removing the higher variability component ascribed to signal amplitude and duration. * A DIC-based system was employed for measurement of the damage. Strain evolution and in particular its re-distribution were evaluated, providing a clear indication of damage evolution. * These indications were identified at a much later stage than acoustic emission (AE). This may be due to the fact that a cracking event does not generate sufficient strain redistribution to be captured by DIC, and more ruptures have to occur to create identifiable damage. This is why DIC's strain redistribution shows clear evidence during phase 3 (rupture) of the test but not during phase 1. On the other hand, the DIC system does not require any classification to distinguish between noise and real events as the real magnitude (strain) is measured. * These findings, particularly those related to damage initiation and progression monitoring through acoustic emission (AE), open possibilities for evaluating and monitoring damage in complex structures where conventional techniques are challenging to apply. Conceptualization, A.G.d.I.Y. ; methodology, A.G.d.I.Y. and O.Z. ; software, I.E. and A.G.d.I.Y. ; formal analysis, A.G.d.I.Y. ; investigation, J.V. ; resources, O.Z. and R.S. ; data curation, I.E., X.Z., R.S. and M.K. ; writing--original draft preparation, A.G.d.I.Y. ; writing--review and editing, J.V., X.Z., O.Z. and M.K. ; project administration, A.G.d.I.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by European Commission under grant number 831798 within the H2020-EU.3.4-SOCIETAL CHALLENGES program part of the Clean Sky 2. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidential non-disclosure agreement.