Longitudinal bar buckling behavior

Reinforced concrete columns might fail because of buckling of the longitudinal reinforcing bar when exposed to earthquake motions. Depending on the hoop stiffness and the length-over-diameter ratio, the instability can be local (in between two subsequent hoops) or global (the buckling length comprises several hoop spacings). To get insight into the topic, an extensive literary research of 19 existing models has been carried out including different approaches and assumptions which yield different results. Finite element fiberanalysis was carried out to study the local buckling behavior with varying length-over-diameter and initial imperfection-over-diameter ratios. The comparison of the analytical results with some experimental results shows good agreement before the post buckling behavior undergoes large deformation. Furthermore, different global buckling analysis cases were run considering the influence of different parameters; for certain hoop stiffnesses and length-over-diameter ratios local buckling was encountered. A parametric study yields an adimensional critical stress in function of a stiffness ratio characterized by the reinforcement configuration. Colonne in cemento armato possono collassare per via dell’instabilità dell’armatura longitudinale se sottoposte all’azione di un sisma. In funzione della rigidezza dei ferri trasversali e del rapporto lunghezza d’inflessione-diametro, l’instabilità può essere locale (fra due staffe adiacenti) o globale (la lunghezza d’instabilità comprende alcune staffe). Per introdurre alla materia, è proposta un’esauriente ricerca bibliografica di 19 modelli esistenti che include approcci e ipotesi differenti che portano a risultati distinti. Tramite un’analisi a fibre e elementi finiti si è studiata l’instabilità locale con vari rapporti lunghezza d’inflessione-diametro e imperfezione iniziale-diametro. Il confronto dei risultati analitici con quelli sperimentali mostra una buona coincidenza fino al raggiungimento di grandi spostamenti. Inoltre, il caso d’instabilità globale è stato simulato valutando l’influenza di vari parametri; per certe configurazioni di rigidezza delle staffe e lunghezza d’inflessione-diametro si hanno ottenuto casi di instabilità locale. Uno studio parametrico ha permesso di ottenere un carico critico adimensionale in funzione del rapporto di rigidezza dato dalle caratteristiche dell’armatura.

Design and implementation of a testbed for data distribution management

Data Distribution Management (DDM) is a core part of High Level Architecture standard, as its goal is to optimize the resources used by simulation environments to exchange data. It has to filter and match the set of information generated during a simulation, so that each federate, that is a simulation entity, only receives the information it needs. It is important that this is done quickly and to the best in order to get better performances and avoiding the transmission of irrelevant data, otherwise network resources may saturate quickly. The main topic of this thesis is the implementation of a super partes DDM testbed. It evaluates the goodness of DDM approaches, of all kinds. In fact it supports both region and grid based approaches, and it may support other different methods still unknown too. It uses three factors to rank them: execution time, memory and distance from the optimal solution. A prearranged set of instances is already available, but we also allow the creation of instances with user-provided parameters. This is how this thesis is structured. We start introducing what DDM and HLA are and what do they do in details. Then in the first chapter we describe the state of the art, providing an overview of the most well known resolution approaches and the pseudocode of the most interesting ones. The third chapter describes how the testbed we implemented is structured. In the fourth chapter we expose and compare the results we got from the execution of four approaches we have implemented. The result of the work described in this thesis can be downloaded on sourceforge using the following link: https://sourceforge.net/projects/ddmtestbed/. It is licensed under the GNU General Public License version 3.0 (GPLv3).

Teos-based consolidants for stone conservation: evaluation of the effects and testing by x-ray imaging

Natural stones have been widely used in the construction field since antiquity. Building materials undergo decay processes due to mechanical,chemical, physical and biological causes that can act together. Therefore an interdisciplinary approach is required in order to understand the interaction between the stone and the surrounding environment. Utilization of buildings, inadequate restoration activities and in general anthropogenic weathering factors may contribute to this degradation process. For this reasons, in the last few decades new technologies and techniques have been developed and introduced in the restoration field. Consolidants are largely used in restoration and conservation of cultural heritage in order to improve the internal cohesion and to reduce the weathering rate of building materials. It is important to define the penetration depth of a consolidant for determining its efficacy. Impregnation mainly depends on the microstructure of the stone (i.e. porosity) and on the properties of the product itself. Throughout this study, tetraethoxysilane (TEOS) applied on globigerina limestone samples has been chosen as object of investigation. After hydrolysis and condensation, TEOS deposits silica gel inside the pores, improving the cohesion of the grains. X-ray computed tomography has been used to characterize the internal structure of the limestone samples,treated and untreated with a TEOS-based consolidant. The aim of this work is to investigate the penetration depth and the distribution of the TEOS inside the porosity, using both traditional approaches and advanced X-ray tomographic techniques, the latter allowing the internal visualization in three dimensions of the materials. Fluid transport properties and porosity have been studied both at macroscopic scale, by means of capillary uptake tests and radiography, and at microscopic scale,investigated with X-ray Tomographic Microscopy (XTM). This allows identifying changes in the porosity, by comparison of the images before and after the treatment, and locating the consolidant inside the stone. Tests were initially run at University of Bologna, where characterization of the stone was carried out. Then the research continued in Switzerland: X-ray tomography and radiography were performed at Empa, Swiss Federal Laboratories for Materials Science and Technology, while XTM measurements with synchrotron radiation were run at Paul Scherrer Institute in Villigen.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.