Mode-I fracture parameters of quasi-brittle materials: direct evaluation using DIC and relationship with Mode-II interfacial parameters

The objective of this thesis is the investigation of the Mode-I fracture mechanics parameters of quasi-brittle materials to shed light onto the influence of the width and size of the specimen on the fracture response of notched beams. To further the knowledge on the fracture process, 3D digital image correlation (DIC) was employed. A new method is proposed to determine experimentally the critical value of the crack opening, which is then used to determine the size of the fracture process zone (FPZ). In addition, the Mode-I fracture mechanics parameters are compared with the Mode-II interfacial properties of composites materials that feature as matrices the quasi-brittle materials studied in Mode-I conditions. To investigate the Mode II fracture parameters, single-lap direct shear tests are performed. Notched concrete beams with six cross-sections has been tested using a three-point bending (TPB) test set-up (Mode-I fracture mechanics). Two depths and three widths of the beam are considered. In addition to concrete beams, alkali-activated mortar beams (AAMs) that differ by the type and size of the aggregates have been tested using the same TPB set-up. Two dimensions of AAMs are considered. The load-deflection response obtained from DIC is compared with the load-deflection response obtained from the readings of two linear variable displacement transformers (LVDT). Load responses, peak loads, strain profiles along the ligament from DIC, fracture energy and failure modes of TPB tests are discussed. The Mode-II problem is investigated by testing steel reinforced grout (SRG) composites bonded to masonry and concrete elements under single-lap direct shear tests. Two types of anchorage systems are proposed for SRG reinforced masonry and concrete element to study their effectiveness. An indirect method is proposed to find the interfacial properties, compare them with the Mode-I fracture properties of the matrix and to model the effect of the anchorage.

Safe and Collaborative Navigation & Interaction With Mobile Manipulators in Domestic Appliance Test Labs

Safe collaboration between a robot and human operator forms a critical requirement for deploying a robotic system into a manufacturing and testing environment. In this dissertation, the safety requirement for is developed and implemented for the navigation system of the mobile manipulators. A methodology for human-robot co-existence through a 3d scene analysis is also investigated. The proposed approach exploits the advance in computing capability by relying on graphic processing units (GPU’s) for volumetric predictive human-robot contact checking. Apart from guaranteeing safety of operators, human-robot collaboration is also fundamental when cooperative activities are required, as in appliance test automation floor. To achieve this, a generalized hierarchical task controller scheme for collision avoidance is developed. This allows the robotic arm to safely approach and inspect the interior of the appliance without collision during the testing procedure. The unpredictable presence of the operators also forms dynamic obstacle that changes very fast, thereby requiring a quick reaction from the robot side. In this aspect, a GPU-accelarated distance field is computed to speed up reaction time to avoid collision between human operator and the robot. An automated appliance testing also involves robotized laundry loading and unloading during life cycle testing. This task involves Laundry detection, grasp pose estimation and manipulation in a container, inside the drum and during recovery grasping. A wrinkle and blob detection algorithms for grasp pose estimation are developed and grasp poses are calculated along the wrinkle and blobs to efficiently perform grasping task. By ranking the estimated laundry grasp poses according to a predefined cost function, the robotic arm attempt to grasp poses that are more comfortable from the robot kinematic side as well as collision free on the appliance side. This is achieved through appliance detection and full-model registration and collision free trajectory execution using online collision avoidance.

A conceptualisation of a governance model for biobanks in the digital society

Biobanks are key infrastructures in data-driven biomedical research. The counterpoint of this optimistic vision is the reality of biobank governance, which must address various ethical, legal and social issues, especially in terms of open consent, privacy and secondary uses which, if not sufficiently resolved, may undermine participants’ and society’s trust in biobanking. The effect of the digital paradigm on biomedical research has only accentuated these issues by adding new pressure for the data protection of biobank participants against the risks of covert discrimination, abuse of power against individuals and groups, and critical commercial uses. Moreover, the traditional research-ethics framework has been unable to keep pace with the transformative developments of the digital era, and has proven inadequate in protecting biobank participants and providing guidance for ethical practices. To this must be added the challenge of an increased tendency towards exploitation and the commercialisation of personal data in the field of biomedical research, which may undermine the altruistic and solidaristic values associated with biobank participation and risk losing alignment with societal interests in biobanking. My research critically analyses, from a bioethical perspective, the challenges and the goals of biobank governance in data-driven biomedical research in order to understand the conditions for the implementation of a governance model that can foster biomedical research and innovation, while ensuring adequate protection for biobank participants and an alignment of biobank procedures and policies with society’s interests and expectations. The main outcome is a conceptualisation of a socially-oriented and participatory model of biobanks by proposing a new ethical framework that relies on the principles of transparency, data protection and participation to tackle the key challenges of biobanks in the digital age and that is well-suited to foster these goals.

Design and characterization of automated agricultural vehicle systems

Nowadays, technological advancements have brought industry and research towards the automation of various processes. Automation brings a reduction in costs and an improvement in product quality. For this reason, companies are pushing research to investigate new technologies. The agriculture industry has always looked towards automating various processes, from product processing to storage. In the last years, the automation of harvest and cultivation phases also has become attractive, pushed by the advancement of autonomous driving. Nevertheless, ADAS systems are not enough. Merging different technologies will be the solution to obtain total automation of agriculture processes. For example, sensors that estimate products' physical and chemical properties can be used to evaluate the maturation level of fruit. Therefore, the fusion of these technologies has a key role in industrial process automation. In this dissertation, ADAS systems and sensors for precision agriculture will be both treated. Several measurement procedures for characterizing commercial 3D LiDARs will be proposed and tested to cope with the growing need for comparison tools. Axial errors and transversal errors have been investigated. Moreover, a measurement method and setup for evaluating the fog effect on 3D LiDARs will be proposed. Each presented measurement procedure has been tested. The obtained results highlight the versatility and the goodness of the proposed approaches. Regarding the precision agriculture sensors, a measurement approach for the Moisture Content and density estimation of crop directly on the field is presented. The approach regards the employment of a Near Infrared spectrometer jointly with Partial Least Square statistical analysis. The approach and the model will be described together with a first laboratory prototype used to evaluate the NIRS approach. Finally, a prototype for on the field analysis is realized and tested. The test results are promising, evidencing that the proposed approach is suitable for Moisture Content and density estimation.

Characterization of pyrolysis and oxidation processes of biomaterials, biomasses and biofuels

The urgent need for alternative solutions mitigating the impacts of human activities on the environment has strongly opened new challenges and opportunities in view of the energy transition. Indeed, the automotive industry is going through a revolutionary moment in its quest to reduce its carbon footprint, with biofuels being one of the viable alternatives. The use of different classes of biofuels as fuel additives/standalone components has attracted the attention of many researchers. Despite their beneficial effects, biofuel’s combustion can also result in the production of undesirable pollutants, requiring complete characterization of the phenomena occurring during their production and consumption. Industrial scale-up of biomass conversion is challenging owing to the complexity of its chemistry and transport phenomena involved in the process. In this view, the role of solid-phase and gas-phase chemistry is paramount. Thus, this study is devoted to detailed analysis of physical-chemical phenomena characterizing biomass pyrolysis and biofuel oxidation. The pyrolysis mechanism has been represented by 20 reactions whereas, the gas-phase kinetic models; manually upgraded model (KiBo_MU) and automated model (KiBo_AG), comprises 141 species and 453 reactions, and 631 species and 28329 reactions, respectively. The accuracy of the kinetic models was tested against experimental data and the models captured experimental trends very well. While the development and validation of detailed kinetic mechanisms is the main deliverable of this project, the realized procedure integrating schematic classifications with methodologies for the identification of common decomposition pathways and intermediates represents an additional source of novelty. Besides, the fundamentally oriented nature of the adopted method allows the identification of most relevant reactions and species under the operating conditions different industrial applications, paving the way for reduced kinetic mechanisms. Ultimately, the resulting detailed mechanisms can be used to integrate with more complex fluid dynamics model to accurately reproduce the behavior of real systems and reactors.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

A biophysical model for valence-based navigational learning: the role of D-Serine

Recent experiments have revealed the fundamental importance of neuromodulatory action on activity-dependent synaptic plasticity underlying behavioral learning and spatial memory formation. Neuromodulators affect synaptic plasticity through the modification of the dynamics of receptors on the synaptic membrane. However, chemical substances other than neuromodulators, such as receptors co-agonists, can influence the receptors' dynamics and thus participate in determining plasticity. Here we focus on D-serine, which has been observed to affect the activity thresholds of synaptic plasticity by co-activating NMDA receptors. We use a computational model for spatial value learning with plasticity between two place cell layers. The D-serine release is CB1R mediated and the model reproduces the impairment of spatial memory due to the astrocytic CB1R knockout for a mouse navigating in the Morris water maze. The addition of path-constraining obstacles shows how performance impairment depends on the environment's topology. The model can explain the experimental evidence and produce useful testable predictions to increase our understanding of the complex mechanisms underlying learning.