Application of the Equivalent Static Analysis method to the design of a steel frame structure with added viscous dampers

All the structures designed by engineers are vulnerable to natural disasters including floods and earthquakes. The energy released during strong ground motions should be dissipated by structural elements. Before 1990’s, this energy was expected to be dissipated through the beams and columns which at the same time were a part of gravity-load-resisting system. However, the main disadvantage of this idea was that gravity-resisting-frame was not repairable. Hence, during 1990’s, the idea of designing passive energy dissipation systems, including dampers, emerged. At the beginning, main problem was lack of guidelines for passive energy dissipation systems. Although till 2000 many guidelines and procedures where published, yet most of them were based on complicated analysis which was not so convenient for engineers and practitioners. In order to solve this problem recently some alternative design methods are proposed including 1. Lopez Garcia (2001) simple procedure for optimal damper configuration in MDOF structures 2. Christopoulos and Filiatrault (2006) trial and error procedure 3. Silvestri et al. (2010) Five-Step Method. 4. Palermo et al. (2015) Direct Five-Step Method. 5. Palermo et al. (2016) Simplified Equivalent Static Analysis (ESA). In this study, effectiveness and differences between last three alternative methods have been evaluated.

Approccio network-based alla discriminant analysis mediante HPC per la ricerca di signature ottimali in dati ad alta dimensionalità

Il presente lavoro di tesi si inserisce nell’ambito della classificazione di dati ad alta dimensionalità, sviluppando un algoritmo basato sul metodo della Discriminant Analysis. Esso classifica i campioni attraverso le variabili prese a coppie formando un network a partire da quelle che hanno una performance sufficientemente elevata. Successivamente, l’algoritmo si avvale di proprietà topologiche dei network (in particolare la ricerca di subnetwork e misure di centralità di singoli nodi) per ottenere varie signature (sottoinsiemi delle variabili iniziali) con performance ottimali di classificazione e caratterizzate da una bassa dimensionalità (dell’ordine di 101, inferiore di almeno un fattore 103 rispetto alle variabili di partenza nei problemi trattati). Per fare ciò, l’algoritmo comprende una parte di definizione del network e un’altra di selezione e riduzione della signature, calcolando ad ogni passaggio la nuova capacità di classificazione operando test di cross-validazione (k-fold o leave- one-out). Considerato l’alto numero di variabili coinvolte nei problemi trattati – dell’ordine di 104 – l’algoritmo è stato necessariamente implementato su High-Performance Computer, con lo sviluppo in parallelo delle parti più onerose del codice C++, nella fattispecie il calcolo vero e proprio del di- scriminante e il sorting finale dei risultati. L’applicazione qui studiata è a dati high-throughput in ambito genetico, riguardanti l’espressione genica a livello cellulare, settore in cui i database frequentemente sono costituiti da un numero elevato di variabili (104 −105) a fronte di un basso numero di campioni (101 −102). In campo medico-clinico, la determinazione di signature a bassa dimensionalità per la discriminazione e classificazione di campioni (e.g. sano/malato, responder/not-responder, ecc.) è un problema di fondamentale importanza, ad esempio per la messa a punto di strategie terapeutiche personalizzate per specifici sottogruppi di pazienti attraverso la realizzazione di kit diagnostici per l’analisi di profili di espressione applicabili su larga scala. L’analisi effettuata in questa tesi su vari tipi di dati reali mostra che il metodo proposto, anche in confronto ad altri metodi esistenti basati o me- no sull’approccio a network, fornisce performance ottime, tenendo conto del fatto che il metodo produce signature con elevate performance di classifica- zione e contemporaneamente mantenendo molto ridotto il numero di variabili utilizzate per questo scopo.

Traffic Signal Optimization of Urban Arterial Network by Vehicle-to-Infrastructure

Urbanization has occasionally been linked to negative consequences. Traffic light system in urban arterial networks plays an essential role to the operation of transport systems. The availability of new Intelligent Transportation System innovations paved the way for connecting vehicles and road infrastructure. GLOSA, or the Green Light Optimal Speed Advisory, is a recent integration of vehicle-to-everything (v2x) technology. This thesis emphasized GLOSA system's potential as a tool for addressing traffic signal optimization. GLOSA serves as an advisory to drivers, informing them of the speed they must maintain to reduce waiting time. The considered study area in this thesis is the Via Aurelio Saffi – Via Emilia Ponente corridor in the Metropolitan City of Bologna which has several signalized intersections. Several simulation runs were performed in SUMOPy software on each peak-hour period (morning and afternoon) using recent actual traffic count data. GLOSA devices were placed on a 300m GLOSA distance. Considering the morning peak-hour, GLOSA outperformed the actuated traffic signal control, which is the baseline scenario, in terms of average waiting time, average speed, average fuel consumption per vehicle and average CO2 emissions. A remarkable 97% reduction on both fuel consumption and CO2 emissions were obtained. The average speed of vehicles running through the simulation was increased as well by 7% and a time saved of 25%. Same results were obtained for the afternoon peak hour with a decrease of 98% on both fuel consumption and CO2 emissions, 20% decrease on average waiting time, and an increase of 2% in average speed. In addition to previously mentioned benefits of GLOSA, a 15% and 13% decrease in time loss were obtained during morning and afternoon peak-hour, respectively. Towards the goal of sustainability, GLOSA shows a promising result of significantly lowering fuel consumption and CO2 emissions per vehicle.