Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Spatial-temporal variability of the benthic communities in Ravenna harbour using Autonomous Reef Monitoring Structures (ARMS) with focus on Non-Indigenous Species (NIS)

A Non-Indigenous Species (NIS) is defined as an organism, introduced outside its natural past or present range of distribution by humans, that successfully survives, reproduces, and establish in the new environment. Harbors and tourist marinas are considered NIS hotspots, as they are departure and arrival points for numerous vessels and because of the presence of free artificial substrates, which facilitate colonization by NIS. To early detect the arrival of new NIS, monitoring benthic communities in ports is essential. Autonomous Reef Monitoring Structures (ARMS) are standardized passive collectors that are used to assess marine benthic communities. Here we use an integrative approach based on multiple 3-month ARMS deployment (from April 2021 to October 2022) to characterize the benthic communities (with a focus on NIS) of two sites: a commercial port (Harbor) and a touristic Marina (Marina) of Ravenna. The colonizing sessile communities were assessed using percentage coverage of the taxa trough image analyses and vagile fauna (> 2 mm) was identified morphologically using a stereomicroscope and light microscope. Overall, 97 taxa were identified and 19 of them were NIS. All NIS were already observed in port environments in the Mediterranean Sea, but for the first time the presence of the polychaete Schistomeringos cf. japonica (Annenkova, 1937) was observed; however molecular analysis is needed to confirm its identity. Harbor and Marina host significantly different benthic communities, with significantly different abundance depending on the sampling period. While the differences between sites are related to their different environmental characteristic and their anthropogenic pressures, differences among times seems related to the different life cycle of the main abundant species. This thesis evidenced that ARMS, together with integrative taxonomic approaches, represent useful tools to early detect NIS and could be used for a long-term monitoring of their presence.