Die vorliegende Arbeit befasst sich mit datengetriebenen Modellierungsansätzen in der Gebäudetechnik zum Zweck des Entwurfs modellprädiktiver Regler. Dabei widmet sich der erste Teil (Kapitel 2 und 3) im Wesentlichen den theoretischen Grundlagen der Systemidentifikation wohingegen im zweiten Teil (Kapitel 4 bis 6) praktische Anwendungen der Identifikation sowie die Implementierung modellprädiktiver Regler im Gebäudebereich im Vordergrund stehen. Die Grundlage der Arbeit stellt die Systemidentifikation mittels der Prädiktionsfehlerminimierung (Prediction Error Minimization – PEM) dar, deren Basis stochastische Modelle dynamischer Systeme bilden. Daher wird ausgehend von den unterschiedlichen Darstellungsformen stochastischer linearer zeitinvarianter Systeme (LTI-Systeme) die Problematik der optimalen Prädiktion thematisiert, wobei ein allgemeiner Ausdruck zur Berechnung der Mehrschrittprädiktion von LTI-Systemen hergeleitet wird. Anschließend werden Implementierungsaspekte von Mehr- bzw. Multischritt-PEM-Verfahren diskutiert und der Schätzer im Frequenzbereich analysiert, wobei sich verglichen mit der 1-Schritt-PEM eine erwartungsgemäß andere Wichtung des geschätzten Modells im Frequenzbereich ergibt. Die aus diesen Untersuchungen gewonnen Identifizierbarkeitsbedingungen entsprechen im offenen Regelkreis den bekannten Bedingungen der 1-Schritt-PEM, wohingegen sich im geschlossenen Regelkreis eine restriktivere Bedingung ergibt. Die weiterhin durchgeführte Genauigkeitsanalyse führt letztlich zu einem Ausdruck für die Abschätzung der Schätzfehlerkovarianzmatrix der Modellparameter, welche wiederum für die Varianz- bzw. Konfidenzschätzung anderer Systemmerkmale genutzt werden kann. In der Gebäudetechnik spielen nichtlineare Modellansätze eine wichtige Rolle. Für diese ist die explizite Berechnung der optimalen Prädiktion, und damit eine Identifikation mittels PEM, in der Regel problematisch bzw. nicht möglich. In der Arbeit werden daher zwei nichtlineare Modellstrukturen vorgeschlagen, für welche entsprechende Ausdrücke angegeben werden können, sodass eine direkte Anwendung der PEM ermöglicht wird. Dabei hat sich im Anwendungsteil der Arbeit herausgestellt, dass einer dieser Ansätze sinnvoll in der Gebäudetechnik genutzt werden kann. Aufgrund der Ähnlichkeit zum linearen Fall können zudem die Ideen, die typischerweise für die Parametrierung stochastischer LTI Systeme genutzt werden, auf den nichtlinearen Fall übertragen werden. Im Anwendungsteil der Arbeit wird dann die Nutzung der PEM im Gebäudebereich anhand praktisch aufgenommener Messdaten einiger Demonstratoren veranschaulicht. Es werden Modell- und Identifikationsansätze für die Schätzung von thermischen Raummodellen sowie einer Gastherme und einer Wärmepumpe vorgeschlagen, wobei plausible Identifikationsergebnisse erzielt werden konnten. Weiterhin wird im Rahmen der Identifikation von thermischen Raummodellen auch auf die wichtige Problematik nicht messbarer Störungen (z. B. Raumbelegung sowie Wärmeströme aus Nachbarräumen) eingegangen und mögliche Lösungsansätze aufgezeigt. Aufgrund der umfangreichen Sensorausstattung einiger Demonstratoren konnte zudem ein direkter Vergleich eines LTI-Modells mit dem obig genannten nichtlinearen Modellansatz durchgeführt werden, wobei eine vergleichbare Prädiktionsgüte beider Modellierungsansätze festgestellt wurde. Im letzten Teil wird dann der Entwurf modellprädiktiver Regler anhand zweier typischer Beispiele aus der Gebäudetechnik veranschaulicht. Für die Demonstration des Vorgehens werden Regelstreckenmodelle mittels der Modelica Buildings Bibliothek entworfen und dann für die Generierung synthetischer Daten verwendet, welche zur Identifikation von Modellen für den MPC Entwurf verwendet werden. Die identifizierten Modelle sind in beiden Fällen in der Lage das dynamische Verhalten des Modelica Prozessmodells hinreichend gut vorherzusagen, was die Sinnhaftigkeit des gewählten Modellierungs- bzw. Identifikationsansatzes untermauert. Aufbauend wird dann für beide Beispiele auf die Formulierung einer prädiktiven Regelungsstrategie eingegangen, wobei spezielle Modelleigenschaften ausgenutzt werden. Für beide Beispiele konnten simulativ Vorteile gegenüber einer konventionellen Regelungsstrategie nachgewiesen werden.

Chaos Engineering (CE) intentionally disrupts distributed systems by introducing faults into the system to better understand and improve their resilience. By studying these intentional disruptions, CE provides insights that help enhance system performance and the overall user experience. However, two main challenges exist: reducing the negative impact or ”blast radius” of these CE experiments without diluting the value of the CE experiment and identifying a standardized set of metrics to monitor during such CE experiments. This research addresses these challenges by monitoring application and system-level metrics known as the Golden Signals, and a steady-state metric called the Apdex score during a CE experiment. Using Pearson and Spearman correlation analyses alongside Granger Causality tests, a strong connection between the Golden Signals and Apdex score is identified. The study also introduces a new health-check system design that uses the Apdex score to automatically stop a CE experiment if a preset threshold is violated. Furthermore, the design also introduces a method for early termination of the CE experiment based on forecasted Apdex scores. This method not only limits potential system damage but also reveals key system weaknesses, striking a balance between risk and discovery. / Chaos Engineering (CE) stör medvetet distribuerade system genom att införa fel i systemet för att bättre förstå och förbättra deras motståndskraft. Genom att studera dessa medvetna störningar ger CE insikter som hjälper till att förbättra systemprestanda och den övergripande användarupplevelsen. Två huvudutmaningar finns dock: att minska den negativa effekten eller ”blast radius” av dessa CE-experiment utan att försämra värdet av CE-experimentet och att identifiera en standardiserad uppsättning av mätvärden att övervaka under sådana CE-experiment. Denna forskning tar itu med dessa utmaningar genom att övervaka applikations- och systemnivåmätvärden kända som Golden Signals, och en jämviktsmetrik kallad Apdex-poängen under ett CE-experiment. Genom att använda Pearson och Spearmans korrelationsanalyser tillsammans med Granger orsakssambandstester identifieras en stark koppling mellan Golden Signals och Apdex-poängen. Studien introducerar också en ny hälsocheck-systemdesign som använder Apdex-poängen för att automatiskt stoppa ett CE-experiment om ett förinställt tröskelvärde överskrids. Vidare introducerar designen också en metod för tidig avslutning av CE-experiment baserat på förutsagda Apdex-poäng.. Denna metod begränsar inte bara potentiell systemskada utan avslöjar också nyckelsystemsvagheter och skapar en balans mellan risk och upptäckt.

No description available.

<p>Optimization under uncertainty is the field of optimization where the data or the optimization model itself has uncertainties associated with it. Such problems are more commonly referred to as stochastic optimization problems. These problems capture the broad idea of making optimal decisions under uncertainty. An important class of these stochastic optimization problems is chance-constrained optimization problems, where the decision maker seeks to choose the best decision such that the probability of violating a set of uncertainty constraints is within a predefined probabilistic threshold risk level. Such stochastic optimization problems have found a lot of interest in the service industry as the service providers need to satisfy a minimum service level agreement (SLA) with their customers. Satisfying SLA in the presence of uncertainty in the form of probabilistic failure of infrastructure poses many interesting and challenging questions. In this thesis, we answer a few of these questions.</p> <p>We first explore the problem of quantifying uncertainties that adversely impact the service provider's infrastructure, thereby hurting the service level agreements. In particular we address the probability quantification problem, where given an uncertainty set, the goal is to quantify the probability of an event, on which the optimal value of an optimization problem exceeds a predefined threshold value. The novel techniques we propose, use and develop ideas from diverse literatures such as mixed integer nonlinear program, chance-constrained programming, approximate sampling and counting techniques, and large deviation bounds. Our approach yields the first polynomial time approximation scheme for the specific probability quantification problem we consider. </p> <p>Our next work is inspired by the ideas of risk averse decision making. Here, we focus on studying the problem of minimizing risk functions. As a special case we also explore the problem of minimizing the Value at Risk (VaR), which is a well know non-convex problem. For more than a decade, the well-known, best convex approximation to this problem has been obtained by minimizing the Conditional Value at Risk (CVaR). We proposed a new two-stage model which formulates these risk functions, which eventually leads to a bilinear optimization problem, a special case of which is the VaR minimization problem. We come up with enhancements to this bilinear formulation and use convexification techniques to obtain tighter lower and upper convex approximations to the problem. We also find that the approximation obtained by CVaR minimization is a special case of our method. The overestimates we construct help us to develop tighter convex inner approximations for the chance constraint optimization problems.</p>

The current global scenario is such where impact on the environment is becoming a rising concern. Global automotive manufacturers have focused more towards hybrid and electric vehicles as both more aware customers and governmental legislation have begun demanding higher emission standards. One of the many ways that Volvo Car Group approaches this trend is by mild hybridization which is by assisting the combustion engine by a small electric motor and a battery pack. A smart energy management strategy is needed in order to get the most out of the benefits that hybrid electric vehicles offer. The main objective of this strategy is to utilize the electrical energy on-board in such a manner that the overall efficiency of the hybrid powertrain becomes as high as possible. The current implementation is such that the decision for using the on-board battery is non-predictive. This results in a sub-optimal utilization of the hybrid powertrain. In this thesis, a predictive energy optimization tool is developed to maximize the utility of hybridization and the practical implementation of this tool is investigated. The optimization considers both the capacity as well as the thermal loadconstraints of the battery. The developed optimization tool uses information about the route ahead together with convex optimization to produce optimal reference trajectories of the battery states. These trajectories are used in a real-time controller to determine the battery use by controlling the adjoint states in the Equivalent Consumption Minimization Strategy equation. This optimization tool is validated and compared with the baseline controller in a simulation environment based on Simulink. When perfect information about the road ahead is known, the average reduction in fuel consumption is 0.99% relative the baseline controller. Several issues occurring in the real implementation are explored, such as the limited computational speed and the length of the route ahead that can be predicted. For this reason the information input to the optimization tool is segmented and the resulting performance is investigated. For a 30 second segmentation of the future route information, the average saving in fuel consumption is 0.13% relative to the baseline controller. It is shown that the main factor limiting the amount of savings in fuel consumption is the introduction of the thermal load constraints on the battery. / Det nuvarande globala scenariot är sådant där miljöpåverkan håller på att bli en växande angelägenhet. Globala fordonstillverkare har fokuserat mer på hybrid- och elfordon, eftersom både mer medvetna kunder och statlig lagstiftning har börjat kräva högre emissionskrav. Ett av de många sätt som Volvo Car Group närmar sig denna trend är genom mild hybridisering genom att bistå förbränningsmotorn med en liten elmotor och ett batteripaket. En smart strategi för energihantering behövs för att få ut det mesta av de fördelar som hybrida elfordon erbjuder. Huvudsyftet med denna strategi är att utnyttja den elektriska energin ombord på ett sådant sätt att den totala effektiviteten hos hybriddrivlinan blir så hög som möjligt.Den nuvarande implementeringen är sådan att beslutet att använda det fordonsbaserade batteriet är inte-förutsägbart. Detta resulterar i en suboptimal användning av hybriddrivlinan. I denna avhandling är ett prediktivt Energioptimeringsverktyg utvecklat för att maximera nyttan av hybridisering och det praktiska implementerandet av detta verktyg undersöks. Optimeringen beaktar både kapaciteten och de termiska belastningsbegränsningarna hos batteriet. Det utvecklade optimeringsverktyg använder information om vägen framåt tillsammans medkonvex optimering för att producera optimala referenstrajektorier av batteritillståndet. Dessa trajektorier används i en realtidsstyrenhet för att bestämma batterianvändningen genom att kontrollera adjungerade tillstånden strategiekvationen för den ekvivalenta förbrukningsminimiseringen. Optimeringsverktyget verifieras och jämförs med den ursprungliga styrenheten i en simuleringsmiljö baserad på Simulink. När perfekt information om vägen framåt är känd, är den genomsnittliga minskningen av bränsleförbrukningen 0,99 % relativt den ursprungliga styrenheten. Flera frågor som uppstår i den verkliga implementeringen undersöks, såsom den begränsade beräkningshastigheten och längden på den väg framåt som kan förutses. Av denna anledning är segmenteras informationen till optimeringsverktyget och den resulterande prestandan undersöks. För en 30 sekunders segmentering av framtida väginformation är den genomsnittliga besparingen i bränsleförbrukningen 0,13 % i förhållande till den ursprungligastyrenheten. Resultaten visar att den viktigaste faktorn som begränsar bränsleförbrukningsbesparingen är införandet av de termiska belastningsbegränsningarna på batteriet.

Esta tesis se centra en la construcción y usos de los modelos metabólicos a escala genómica para obtener biocombustibles de manera eficiente, como etanol e hidrógeno. Como organismo objetivo, se ha elegido a la cianobacteria Synechocystis sp. PCC6803. Este organismo ha sido estudiado como una potencial plataforma de producción alimentada por fotones, dada su capacidad de crecer solamente a partir de dióxido de carbono y fotones. Esta tesis versa acerca de los métodos para modelar, analizar, estimar y predecir el comportamiento del metabolismo de las células. La principal meta es extraer conocimiento de los diferentes aspectos biológicos de un organismo con el fin de utilizarlo para un objetivo industrial pertinente. Esta tesis ha sido estructurada en capítulos organizados de acuerdo con las sucesivas tareas que terminan con la construcción de una célula in silico que se comporta, idealmente, como la que está basada en el carbono. Este proceso suele comenzar con los archivos de anotación del genoma y termina con un modelo metabólico a escala genómica capaz de integrar datos -ómicos. El primer objetivo de la presente tesis es la reconstrucción de un modelo del metabolismo de esta cianobacteria que tenga en cuenta todas las reacciones presentes en la misma. Esta reconstrucción tenía que ser lo suficientemente flexible como para permitir el crecimiento en las distintas condiciones ambientales bajo las cuales este organismo crece en la naturaleza, así como permitir la integración de diferentes niveles de información biológica. Una vez que se cumplió este requisito, se pudieron simular variaciones ambientales y estudiar sus efectos desde una perspectiva de sistema. Se han estudiado hasta cinco diferentes condiciones de crecimiento en este modelo metabólico y sus diferencias han sido evaluadas. La siguiente tarea fue definir estrategias de producción para sopesar la viabilidad de este organismo como una plataforma de producción. Se simularon perturbaciones genéticas para e / This thesis is focused on the construction and uses of genome-scale metabolic models to efficiently obtain biofuels, such as ethanol and hydrogen. As a target organism, cyanobacterium Synechocystis sp. PCC6803 was chosen. This organism has been studied as a potential photon-fuelled production platform, for its ability to grow only from carbon dioxide, water and photons. This dissertation verses about methods to model, analyse, estimate and predict the metabolic behaviour of cells. Principal goal is to extract knowledge from the different biological aspects of an organism in order to use it for an industrial relevant objective. This dissertation has been structured in chapters accordingly organized as the successive tasks that end up building an in silico cell that behaves as the carbon-based one. This process usually starts with the genome annotation files and ends up with a genome-scale metabolic model able to integrate ¿omics data. First objective of present thesis is to reconstruct a model of this cyanobacteria¿s metabolism that accounts for all the reactions present in it. This reconstruction had to be flexible enough as to allow growth under the different environmental conditions under which this organism grows in nature as well as to allow the integration of different levels of biological information. Once this requisite was met, environmental variations could be simulated and their effect studied under a system-wide perspective. Up to five different growth conditions were simulated on this metabolic model and differences were evaluated. Following assignment was to define production strategies to weigh this organism¿s viability as a production platform. Genetic perturbations were simulated to design strains with an enhanced production of three industrially-relevant metabolites: succinate, ethanol and hydrogen. Resulting sets of genetic modifications for the overproduction of those metabolites are, thus, proposed. Moreover, functional reactions couplings were studied and weighted to their metabolite production importance. Finally, genome-scale metabolic models allow establishing integrative approaches to include different types of data that help to find regulatory hotspots that can be targets of genetic modification. Such regulatory hubs were identified upon light/dark shifts and general metabolism operational principles inferred. All along this process, blind spots in Synechocystis sp. PCC6803 metabolism, and more importantly, blind spots in our understanding of it, are revealed. Overall, the work presented in this thesis unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean production platform. / Esta tesis es centra en la construcció i els usos del models metabòlics a escala genòmica per a obtenir eficientment biocombustibles, com etanol i hidrogen. Com a organisme diana, s¿elegí el cianobacteri Synechocystis sp. PCC6803. Aquest organisme ha segut estudiat com una plataforma de producció nodrida per fotons, per la seva habilitat per créixer a partir únicament de diòxid de carboni, aigua i fotons. Aquesta tesi versa sobre mètodes per a modelitzar, analitzar, estimar i predir el comportament metabòlic de cèl¿lules. La principal meta és extreure coneixement del diferents aspectes biològics d¿un organisme de manera que s¿usen per a un objectiu industrial rellevant. La tesi ha segut estructurada en capítols organitzats d¿acord a les successives tasques que acaben construint una cèl¿lula in silico que es comporta, idealment, com la que està basada en carboni. Aquest procés generalment comença amb els arxius de l¿anotació del genoma i acaba amb un model metabòlic a escala genòmica capaç d¿integrar dades ¿òmiques. El primer objectiu de la present tesi és la reconstrucció d¿un model del metabolisme d¿aquest cianobacteri que tinga en compte totes les reaccions que hi estan presents. Esta reconstrucció havia de ser prou flexible com per permetre la simulació del creixement en les diferents condicions ambientals en les quals aquest cianobacteri creix en la natura, així com permetre la integració de diferents nivells d¿informació biològica. Una vegada que aquest requisit fou assolit, es pogueren simular variacions ambientals i estudiar els seus efectes amb una perspectiva de sistema. S¿han simulat fins a cinc condicions de creixement en este model metabòlic i les seves diferències han segut avaluades. La següent tasca fou definir estratègies de producció per a valorar la viabilitat d¿aquest organisme com a plataforma de producció. Es simularen pertorbacions genètiques per al disseny de soques amb producció millorada de metabòlits de rellevància industrial: succinat, etanol i hidrogen. Així, es proposen conjunts de modificacions genètiques per a la sobreproducció d¿aquests metabòlits. També s'han estudiat reaccions acoblades funcionalment i s¿ha ponderat la seva importància en la producció de metabòlits. Finalment, els models metabòlics a escala genòmica permeten establir criteris per integrar diferents tipus de dades que ens ajuden a trobar punts importants de regulació. Eixos centres reguladors, que poden ser objecte de modificacions genètiques, han segut investigats baix canvis dràstics d¿il¿luminació i s¿han inferit principis operacionals del metabolisme. Al llarg d'aquest procés, s¿han revelat punts cecs al metabolisme de Synechocystis sp. PCC6803 i, el més important, punts cecs en la nostra comprensió d'aquest metabolisme. En general, el treball presentat en aquesta tesi dona a conèixer les capacitats industrials del cianobacteri Synechocystis sp. PCC6803 per a produir metabòlits d'interès, tot sent una plataforma de producció neta i sostenible. / Montagud Aquino, A. (2012). Modelling and analysis of biological systems to obtain biofuels [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17319

Reverse logistics is an important process that is often misunderstood. Reverse logistics can cause considerable cost, but provide numerous opportunities. Many organisations do not understand the correct processes and procedures to follow and how to manage reverse logistics efficiently. The focus of this study was on best practices in reverse logistics. A best practice framework was developed to help organisations overcome problems and manage their reverse logistics more efficiently. This study adopted a mixed method research approach with both qualitative and quantitative elements. A comprehensive literature study was conducted to develop a conceptual best practice framework in reverse logistics and a survey was conducted to seek inputs from industry in South Africa to refine the framework into a workable instrument in practice. The study concluded that the best practices identified in literature have proven to be important in practice, and applying such practices will enable organisations to manage their reverse logistics more efficiently. / Business Management / M. Com. (Logistics)

Bibliographical refernces appear at the end of each chapter / This study reviews solid waste management practices in Polokwane City. The study area covered some of the residential areas in Polokwane City; namely; Ivy Park, Fauna Park, Welgelegen, Westernburg and the City Centre. This article describes two main methods that were used to collect data; that is Qualitative and Quantitative method. Field survey was also made to validate data obtained from the participants that were interviewed during qualitative data process. After framing the problem, the objectives of Solid Waste Management Practices in Polokwane City were briefly outlined as follows: • Assess solid waste management practices in Polokwane City. • Make comparison on how households and the municipality take responsibility in storing, collecting, transporting, treating and disposing solid waste. • Investigate what problems the City encounters in managing solid waste. • Make relevant recommendations aimed at improving solid waste management practices within the City. The service management was administered by the municipality and private sector. From the five study residential areas, the Municipality manages waste in the City Centre while the private sector manages waste in the other residential areas. However, the City continues to play an administrative role over the contracted service provider. Statistical results were presented in figures and tables. The results showed the storage habits, frequency of collection, mode of transport and methods of disposal for solid waste in Polokwane City. The only method of disposal in the city was found to be landfilling. Activities that took place at the landfill site, such as reclaiming were outlined together with the economic values that these activities add to the City. The study also revealed that as population increases, the amount of solid waste generated also increased. / Environmental Sciences / M. Sc. (Environmental Management)

Nous adaptons une heuristique de recherche à voisinage variable pour traiter le problème du voyageur de commerce avec fenêtres de temps (TSPTW) lorsque l'objectif est la minimisation du temps d'arrivée au dépôt de destination. Nous utilisons des méthodes efficientes pour la vérification de la réalisabilité et de la rentabilité d'un mouvement. Nous explorons les voisinages dans des ordres permettant de réduire l'espace de recherche. La méthode résultante est compétitive avec l'état de l'art. Nous améliorons les meilleures solutions connues pour deux classes d'instances et nous fournissons les résultats de plusieurs instances du TSPTW pour la première fois. / We adapt a general variable neighborhood search heuristic to solve the traveling salesman problem with time windows (TSPTW) where the objective is to minimize the completion time. We use efficient methods to check the feasibility and the profitability of a movement. We use a specific order to reduce the search space while exploring the neighborhoods. The resulting method is competitive with the state-of-the-art. We improve the best known solutions for two classes of instances and provide the results of multiple instances of TSPTW for the first time.

L'analyse et la reconstruction automatique de scène urbaine 3D est un problème fondamental dans le domaine de la vision par ordinateur et du traitement numérique de la géométrie. Cette thèse présente des méthodologies pour résoudre le problème complexe de la reconstruction d'éléments urbains en 3D à partir de données aériennes Lidar ou bien de maillages générés par imagerie Multi-View Stereo (MVS). Nos approches génèrent une représentation précise et compacte sous la forme d'un maillage 3D comportant une sémantique de l'espace urbain. Deux étapes sont nécessaires ; une identification des différents éléments de la scène urbaine, et une modélisation des éléments sous la forme d'un maillage 3D. Le Chapitre 2 présente deux méthodes de classifications des éléments urbains en classes d'intérêts permettant d'obtenir une compréhension approfondie de la scène urbaine, et d'élaborer différentes stratégies de reconstruction suivant le type d'éléments urbains. Cette idée, consistant à insérer à la fois une information sémantique et géométrique dans les scènes urbaines, est présentée en détails et validée à travers des expériences. Le Chapitre 3 présente une approche pour détecter la 'Végétation' incluses dans des données Lidar reposant sur les processus ponctuels marqués, combinée avec une nouvelle méthode d'optimisation. Le Chapitre 4 décrit à la fois une approche de maillage 3D pour les 'Bâtiments' à partir de données Lidar et de données MVS. Des expériences sur des structures urbaines larges et complexes montrent les bonnes performances de nos systèmes. / Analysis and 3D reconstruction of urban scenes from physical measurements is a fundamental problem in computer vision and geometry processing. Within the last decades, an important demand arises for automatic methods generating urban scenes representations. This thesis investigates the design of pipelines for solving the complex problem of reconstructing 3D urban elements from either aerial Lidar data or Multi-View Stereo (MVS) meshes. Our approaches generate accurate and compact mesh representations enriched with urban-related semantic labeling.In urban scene reconstruction, two important steps are necessary: an identification of the different elements of the scenes, and a representation of these elements with 3D meshes. Chapter 2 presents two classification methods which yield to a segmentation of the scene into semantic classes of interests. The beneath is twofold. First, this brings awareness of the scene for better understanding. Second, deferent reconstruction strategies are adopted for each type of urban elements. Our idea of inserting both semantical and structural information within urban scenes is discussed and validated through experiments. In Chapter 3, a top-down approach to detect 'Vegetation' elements from Lidar data is proposed using Marked Point Processes and a novel optimization method. In Chapter 4, bottom-up approaches are presented reconstructing 'Building' elements from Lidar data and from MVS meshes. Experiments on complex urban structures illustrate the robustness and scalability of our systems.