Synthesis and characterization of artificial aggregates and their potential applications in road construction

L’obiettivo del progetto è stato quello di realizzare ed analizzare aggregati artificiali creati attraverso geopolimerizzazione e macro-incapsulazione di paraffina in aggregati leggeri espansi, discutendo i loro possibili impieghi nelle pavimentazioni stradali. Dopo un'accurata calibrazione delle miscele geopolimeriche, sono stati realizzati degli aggregati artificiali, in seguito caratterizzati in accordo con la norma UNI EN 10343, con l'intento di sostituire materiali stradali vergini. Contemporaneamente, sono stati prodotti aggregati leggeri impregnati di paraffina (PCM), in grado di cambiare fase una volta raggiunti all'incirca i 3 °C, e successivamente rivestiti da due strati di resina poliestere e polvere di granito, denominati PLA: sfruttandone le proprietà termiche, si è valutato il loro possibile utilizzo come soluzione anti-icing. L’ultima fase della ricerca è stata incentrata nella realizzazione di aggregati geopolimerici espansi e molto porosi che potessero contenere una elevata quantità di PCM, sostituendo l'argilla espansa utilizzata nella produzione degli PLA.

Analisi termiche sull'impiego di materiali a cambiamento di fase (PCM) nelle pavimentazioni stradali

Il presente lavoro di tesi mira a studiare l’utilizzo di aggregati artificiali (PLA) costituiti da aggregati leggeri (LWA) impregnati di materiali a cambiamento di fase (Phase-Change Materials, PCM) nei conglomerati bituminosi. L’obiettivo della tesi è quello di dimostrare che l’utilizzo di questi materiali nelle sovrastrutture stradali, grazie alla proprietà di cambiare fase (da solida a liquida e viceversa) in funzione della temperatura, induce una liberazione di calore. La conseguenza immediata dell’utilizzo di questi materiali è la ridotta necessità di manutenzione invernale, abbattendo i costi di ripristino della pavimentazione. Inoltre l’utilizzo di PLA non deve pregiudicare l’aspetto prestazionale e la vita utile dell’infrastruttura.

Lightweight Internet Traffic Classification - A Subject Based Solution with Word Embeddings

Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application-level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word-embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $ 3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.

Study of recycled Rap and Ra as alternative aggregates for pavements' sub-base and base

Nowadays, an important world’s population growth forecast establish that an increase of 2 billion people is expected by 2050. (UN,2019). This increment of people worldwide involves more humans, as well as growth of the demand for the construction of new residential, institutional, industrial, and infrastructural areas, prompting to a higher consumption of natural resources as required for construction materials. In addition, an effect of this population growth is the production and accumulation of waste causing a serious environmental and economic issue around the world. As an alternative to just producing more waste at the final stage of a building, house, road, among other concrete-based structures, adequate techniques must be applied for recycling and reusing these potential materials. The main priority of the thesis is to foment and evaluate the sustainable construction work leading to environmental-friendly actions that promote the reuse and recycling of construction waste, focusing on the use of construction recycled construction materials as an alternative for sub-base and base of road structure application. This thesis is committed to the analysis of the several laboratory tests carried out for achieving the physical-mechanical properties of the studied materials (recycled concrete aggregates + reclaimed asphalt pavement (RCA+RAP) and stabilized crushed sleepers). All these tests have been carried out in the Laboratory of Roads from the University of Bologna and in the experimental site in CAR srl., at Imola. The results are reported in tables, graphs, and are discussed. The mechanical properties values obtained from the laboratory tests are analysed and compared with standard values declared in the Italian and European normative for roads construction and to the results obtained from in-situ tests in the experimentation field (CAR srl in Imola) with the same materials. This to analyse the performance of them under natural conditions.

An Embedded In-Cabin Lightweight High-Performance 3D Gaze Estimation System

Gaze estimation has gained interest in recent years for being an important cue to obtain information about the internal cognitive state of humans. Regardless of whether it is the 3D gaze vector or the point of gaze (PoG), gaze estimation has been applied in various fields, such as: human robot interaction, augmented reality, medicine, aviation and automotive. In the latter field, as part of Advanced Driver-Assistance Systems (ADAS), it allows the development of cutting-edge systems capable of mitigating road accidents by monitoring driver distraction. Gaze estimation can be also used to enhance the driving experience, for instance, autonomous driving. It also can improve comfort with augmented reality components capable of being commanded by the driver's eyes. Although, several high-performance real-time inference works already exist, just a few are capable of working with only a RGB camera on computationally constrained devices, such as a microcontroller. This work aims to develop a low-cost, efficient and high-performance embedded system capable of estimating the driver's gaze using deep learning and a RGB camera. The proposed system has achieved near-SOTA performances with about 90% less memory footprint. The capabilities to generalize in unseen environments have been evaluated through a live demonstration, where high performance and near real-time inference were obtained using a webcam and a Raspberry Pi4.