Las desviaciones de la ejecución real sobre los estimados planeados y a los compromisos adquiridos son muy comunes en la ejecución de los proyectos en ingeniería. Este hecho genera dudas sobre la eficacia de los procesos de planificación e incentiva los estudios alrededor del control de los mismos. El presente trabajo desarrolla un modelo de control de proyectos entendiendo la ejecución de los mismos como un sistema dinámico. Para ello se enfoca en el control de recursos renovables (como la mano de obra o los equipos) y describe el impacto de los mismos sobre los indicadores de ejecución del proyecto. El impacto de las políticas de control propuestas muestra una reducción de la duración global de ejecución del conjunto de proyectos. El análisis se desarrolló utilizando redes de actividad modeladas en dinámica de sistemas.

Abstract: The aim of this investigation is to assess the behavior of the low-cost sensors under different levels of temperature and relative humidity, which are the main parameters affecting their performance. Several set of experiments were carried out in both, laboratory and field conditions using four low-cost sensors from the company Alphasense. As a short summary of the experimental plan, the low-cost sensors were firstly tested under six relative humidity and four temperature levels against traditional reference instruments. This with the aim of developing a correction algorithm for these two parameters. Then the implementation of a low-cost dryer was evaluated which would reduce the influence of the mentioned parameters on the low-cost sensor behavior. All of these stated tests were performed in controlled conditions in the laboratory. In the end, a long-term experiment in the field was done. This experiment had a duration of one month, and its aim was to appraise the algorithm calculated from the laboratory when comparing their results against the concentrations measured by a monitoring station in Marienplatz, Stuttgart. The relative humidity levels achieved during the laboratory experiments were from 10% until 85%, increasing in steps of 15%, and the temperature levels were 10°C, 25°C, 35°C and 45°C. All low-cost sensors showed a significant improvement when the developed correction algorithm was applied, which is reflected in the selected evaluation parameters (MAPE, MAE, linearity and offset). The relative humidity levels used for the assessment of the low-cost dryer were 40%, 55%, 70% and 85%, finding similar results obtained from the previous experiments. However, with the advantage that in this case, after the drying process, the relative humidity and temperature levels were stable, which leads to avoid applying a complex correction algorithm. The concentrations measured by the low-cost sensors with the applied correction showed improved results, with absolute errors of 38.18 ppb, 7.64 ppb and 4.27 ppb for CO, NO and NO2, respectively. Further investigation is also suggested, regarding the use of different set of sensor for checking the reproducibility as well as dynamic changes in the influencing parameters.

Abstract: Power systems have been dominated by synchronous generators for more than a century. The power system stability has been classically categorized with respect to the dynamics and interactions of the synchronous generator. The classic categories are rotor-angle, frequency, and voltage stability. However, due to the recent high penetration of inverterbased generators such as solar panels and wind turbines, instabilities have arisen outside the spectrum of the classical power system definitions. Therefore, in 2020, the slowinteraction converter-driven stability has been included in the power system stability classification by the IEEE Power System Dynamic Performance Committee. This instability has been observed in real-life cases showing undamped voltage oscillations, which have led to the partial disconnection of power systems. This work focuses on this recently classified instability whose investigation is in the early stages regarding its cause, detection and prevention. Therefore, this work proposes a methodology for the detection and prediction of this instability. This methodology is based on time-domain simulations, the computation of eigenvalues of the power system and the participation factors of oscillatory modes. The proposed tool has been effective in not only detecting but also anticipating the onset of converter-driven instability in operational scenarios with long-term voltage instability. The elements causing the instability are identified using the participation factors, which facilitate the tuning of inverter-based generators controls. Thereby, it is possible to mitigate the converter-driven instability before its manifestation and thus avoid undesired disconnection of generation units, or in the worst case, a potential system collapse. Resumen: Los sistemas de energía han estado dominados por generadores síncronos durante más de un siglo. La estabilidad del sistema de potencia se ha categorizado clásicamente con respecto a la dinámica y las interacciones del generador síncrono. Las categorías clásicas son el ángulo del rotor, la frecuencia y la estabilidad de voltaje. Sin embargo, debido a la reciente alta penetración de generadores basados en inversores, como paneles solares y turbinas eólicas, han surgido inestabilidades fuera del espectro de las definiciones clásicas de sistemas de energía. Por lo tanto, en 2020, la estabilidad impulsada por el convertidor de interacción lenta ha sido incluida en la clasificación de estabilidad del sistema de energía por el Comité de Desempeño Dinámico del Sistema de Energía de IEEE. Esta inestabilidad se ha observado en casos de la vida real que muestran oscilaciones de tensión no amortiguadas, que han provocado la desconexión parcial de los sistemas eléctricos. Este trabajo se centra en esta inestabilidad recientemente clasificada cuya investigación se encuentra en etapas tempranas en cuanto a su causa, detección y prevención. Por tanto, este trabajo propone una metodología para la detección y predicción de esta inestabilidad. Esta metodología se basa en simulaciones en el dominio del tiempo, el cálculo de los valores propios del sistema eléctrico y los factores de participación de los modos oscilatorios. La herramienta propuesta ha sido eficaz no solo para detectar sino también para anticipar el inicio de la inestabilidad impulsada por el convertidor en escenarios operativos con inestabilidad de voltaje a largo plazo. Los elementos que causan la inestabilidad se identifican utilizando los factores de participación, que facilitan el ajuste de los controles de los generadores basados en inversores. De esta manera, es posible mitigar la inestabilidad impulsada por el convertidor antes de su manifestación y así evitar la desconexión no deseada de las unidades de generación o, en el peor de los casos, un colapso potencial del sistema.

Abstract: Claystone degradation under the effect of freezing and thaw cycles represents a key aspect in the production of erodible material in mountain ranges. Erosion processes have numerous potential consequences such as dam filling, increment in slope instability, increase of badlands, among others. Due to the possible effects related to erosion, numerous research processes have been carried out following primarily experimental approaches based on both soil physical characteristics and mechanical properties. However, these have been based on a macroscopic scale. This document presents the development of a research process based on the implementation of numerical models aimed at representing the processes involved in freezing and thawing phenomena on porous media and their effect on potential changes in soil microfabric. A calibration and validation process of the proposed numerical models was carried out based on experimental measurements of the thermal behavior of soil samples subjected to freezing and thawing cycles. Using the numerical relationships found, a methodology for determining the soil water retention curve (SWRC) was established (as a first approximation) based on a quick and relatively simple freezing and thawing test under undrained conditions. Simultaneously, and following the main objective of the research, the microfabric state during cyclic freezing and thawing was established following the relationship between SWRC and pore size distribution (PSD), as well as the possible hysteresis between freezing and thawing processes. The significant effect of these thermodynamic processes on the state of the terrain, was identified particularly in regard to increased erodibility. One possible cause for particle detachment necessary for erosion processes to occur lies in the development of macroporosity along with the existence of gaps between pore families in the PSD. This behavior was observed through the developed numerical processes. The combined effect of freezing and thawing cycles leads to an increase in soil erodibility. Finally, though a constitutive model-based approach, the cryogenic suction paths associated with freezing and thawing processes under the modification of the soil’s microstructure were established. A final perspective to soil's mechanical degradation is presented in terms of the hydraulic work associated with the cryogenic suction paths developed within the soil mass. Both the progressive evolution of the microstructure (through PSD) and the values of hydraulic work associated with each cycle show an attenuation in the incremental change as the cycles continue. Due to the numerical nature of the approximations and the trends found, further cyclical laboratory tests should be carried out. However, these represent a novel approach to the degradation phenomenon as well as the effects associated with the soil water retention curve.

Abstract: This thesis proposes an update to the NS-047 technical standard of the aqueduct and sewerage company of Bogota for storm drain design, introducing the comprehensive and modern UPC methodology. The research addresses the limitations of the current standard, emphasizing the importance of critical parameters such as flow depth and velocities for pedestrian and vehicle safety during storms. By considering a wide range of hydrologic and hydraulic factors, the UPC method provides a realistic representation of stormwater behavior and enables better system understanding and optimization. The adoption of UPC is expected to enhance stormwater management, promoting safety and sustainability in urban flood control. The application of the UPC methodology to a real-life case study, the CASU road project in Bogotá, demonstrates its effectiveness in optimizing storm drain design. The analysis identified areas for improvement, resulting in a revised approach with strategically located storm drains that effectively controlled runoff while reducing the number of structures. This optimization not only saves costs but also minimizes risks for road users. The comparison between EAAB and UPC methodologies highlights the superiority of the UPC method in terms of its comprehensive approach, graphical outputs, and ability to incorporate multiple roadways. To further improve storm drains designs, experimentation is recommended to determine capture coefficients for different structures, especially in low points critical for drainage efficiency and flood prevention. Obtaining accurate capture coefficients through real-world experiments will enhance the overall stormwater management performance and inform design decisions. By complementing the EAAB standard with the UPC methodology and conducting experimentation for capture coefficients, this research aims to provide a clearer, practical, and more effective storm drain design approach for infrastructure projects in Bogotá and other areas in Colombia.

An accurate understanding of the environment is key for a robot in order to execute tasks safe and efficiently. In the field of perception, after the introduction of deep learning, computer vision tasks have made big leaps, surpassing even the human inference capability. As a trade-off, big amounts of annotated data were required to train such algorithms. On its own, the collection of annotated dataset is a highly time consuming activity prone to human errors, setting a limit to the maximum achievable performance. In this sense, annotations (quality) and samples (quantity) bound the optimization of perception algorithms. One extra challenge encountered when training object detectors for robotics applications is that the sensor setup can be multi-modal, and vary significantly between robots. This work explores how to generate and use synthetic RGB-D training data from a near photo-realistic game engine to train modality-specific person detectors, and perform ablation studies on a challenging, real-world dataset recorded using a reference RGB-D sensor in different intralogistics environments. A virtual RGB-D camera was implemented, leveraging the underlying deferred rendering architecture. Multiple environments were tailored, exploring various data augmentation techniques and enabling the comparison between different types of synthetic data. Detection layers of a pre-trained object detector network have been trained from scratch for the RGB and depth modality, with the latter being transformed by applying a Jet-colormap. Compared to a pre-trained network, a domain gap of 5 mPA points was still present for RGB images. Meanwhile with synthetic (15k) and real (1.5k) depth images, it was already possible to train robust human detectors. Comparing simulation features against data preparation, filtering annotations had a major impact on performance than adopting an explicit time-of-flight sensor model.

Tackling climate change effects worldwide brings the interest of many donor countries with expertise to (co)develop solutions into beneficiary countries through international cooperation. The solutions proposed can be either too solution-driven, understood as a donor country selling its knowledge / products, or on the other hand, too locally problem-driven, which is desirable in theory, but facing practical limitations such as time or scale constraints when closing to outside influences. A methodology that tries to join both (apparently opposing) sides, with their advantages and disadvantages of problem and solution driven hasn’t been explored yet.For exploring how this can be done, in this research 2 case studies are analyzed: the Bangladesh Delta Plan 2100 (BDP2100), a nation-wide long term holistic plan on the Bangladeshi Delta, and a group of Coastal Water Management initiatives, a set of continuous efforts to adopt livelihoods to the dynamic situation in the coastal regions of Bangladesh. These cases are analyzed through interviews and desk research to determine to what extent are they problem and solution driven compared to proposed frameworks and pointing how can they be improved. From these analyses, a combined framework is proposed where problem and solution driven approaches are joined taking the best from each perspective. The result is meant to be a first guiding tool for decision-makers in donor and beneficiary countries that face the dilemma between taking local and foreign elements for developing solutions. Resumen Hacer frente a los efectos del cambio climático en todo el mundo atrae el interés de muchos países donantes con experiencia para (co) desarrollar soluciones en los países beneficiarios, a través de la cooperación internacional. Las soluciones propuestas pueden ser demasiado orientadas a la solución, entendidas como un país donante que vende sus conocimientos / productos o, por otro lado, demasiado localmente orientadas a los problemas, lo que es deseable en teoría, pero enfrentan limitaciones prácticas como limitaciones de tiempo o escala cuando se cierran a influencias externas. Aún no se ha explorado una metodología que intente unir a ambas partes (aparentemente opuestas), con las ventajas y desventajas de soluciones basadas en desarrollo netamente de problemas y desarrollo netamente de soluciones. Para explorar cómo se puede combinar esto, en esta investigación se analizan 2 casos de estudio: el Bangladesh Delta Plan 2100 (BDP2100), un plan holístico a largo plazo a nivel nacional en el delta de Bangladesh, y un grupo de iniciativas de gestión costera del agua, un conjunto de esfuerzos continuos para adoptar medios de vida a la situación dinámica en las regiones costeras de Bangladesh. Estos casos se analizan a través de entrevistas e investigaciones teóricas para determinar en qué medida están orientados a los problemas y soluciones en comparación con los marcos propuestos, y señalan cómo pueden mejorarse. A partir de estos análisis, se propone un marco combinado donde se unen los enfoques impulsados por problemas y soluciones, tomando lo mejor de cada perspectiva. El resultado pretende ser una primera herramienta de guía para los tomadores de decisiones en los países donantes y beneficiarios que enfrentan el dilema entre tomar elementos locales y extranjeros para desarrollar soluciones.

Abstract: The forthcoming industrial environments will require a high level of automation to be flexible. In this sense, Industry 4.0 encourages the integration of new autonomous systems digitally interconnected that can interact with humans. This thesis is developed at CIM 4.0 in the frame of the FIXIT project aiming to produce an autonomous system integrated by a UAV and a UGV. As a result, a combined system that complies with the Industry 4.0 requirements is created and mainly serves an operator by performing inspection tasks. This work is focused on redesigning the UAV airframe. Normally these structures are manufactured with some conventional techniques like molding which limits building complex structures and presents high costs in most cases. In this sense, the scope is to create a personalized structure at a lower price than the standard airframes by implementing one of the industry 4.0 pillars: additive manufacturing. The topology optimization method is integrated into the design process to create a mass-customized structure with optimum structural properties. The role of Additive manufacturing in this work is crucial to obtain an innovative, customized system with an optimum cost considering the prices on the market. The thesis is performed in different phases, starting from the definition of the UAV configuration and the propulsion system. Then a multi-material comparison and the analysis of different airframe designs are performed considering the cost and structural performance. Following this is the detailed design, in which other necessary features like the landing system, devices’ supports, and protection structures are designed. Finally, is performed the manufacturing phase in which a functional prototype is printed through FDM technology. The result is a lightweight customized airframe with good mechanical properties and optimum cost that is well integrated with the electronic components and landing system, enabling its autonomous performance characterized by repeatability and effectiveness. Esta tesis se desarrolla en el marco del proyecto FIXIT en el Centro de Manufactura Integrada (CIM) 4.0 con el objetivo de crear un sistema autónomo integrado por un Vehículo Aéreo No Tripulado (UAV) y un Vehículo Terrestre No Tripulado (UGV). Resultado: Como resultado, se crea un sistema combinado que cumple con los requisitos de la Industria 4.0 y que principalmente sirve a un operador para realizar tareas de inspección. En este sentido, el objetivo de este proyecto de tesis es crear una estructura personalizada a un precio inferior al de las estructuras estándar mediante la implementación de uno de los pilares de la Industria 4.0: la fabricación aditiva. El método de optimización de la topología se integra en el proceso de diseño para crear una estructura personalizada con propiedades estructurales óptimas. El papel de la fabricación aditiva en este trabajo es crucial para obtener un sistema innovador y personalizado con un costo óptimo, teniendo en cuenta los precios del mercado. La tesis se realiza en diferentes fases, comenzando desde la definición de la configuración del UAV y el sistema de propulsión. Luego se realiza una comparación de materiales múltiples y se analizan diferentes diseños de la estructura del UAV teniendo en cuenta el costo y el rendimiento estructural. A continuación, se lleva a cabo el diseño detallado, en el que se diseñan otras características necesarias como el sistema de aterrizaje, los soportes de dispositivos y las estructuras de protección. Finalmente, se realiza la fase de fabricación en la que se imprime un prototipo funcional mediante la tecnología de Fabricación por Deposición Fundida (FDM). El resultado es una estructura personalizada y ligera con buenas propiedades mecánicas y un costo óptimo que se integra bien con los componentes electrónicos y el sistema de aterrizaje, lo que permite su funcionamiento autónomo caracterizado por la repetibilidad y la efectividad.

Abstract: Complex networks emerge as a natural framework to describe real-life phenomena involving a group of entities and their interactions, i.e., a social net-work. Furthermore, other problems involve a collection of networks, such as multi-layer, temporal, or brain networks (connectomes). With the increasing availability of graph-shaped data and associated meta-data, i.e., node or edge attributes, machine learning (ML) techniques on networks have risen in popularity. However, there are still several challenging issues to be addressed concerning feature engineering, graph representation, visualization, and general graph mining applications. This thesis contributes to bridging the gap between ML and complex net-works. We investigate dynamics on networks such as discrete and continuous- time random walks, in two directions. First, we develop a multi-scale anomaly detection algorithm on attributed networks. We exploit the link between graph signal processing and the Markov stability framework used in community detection, for spotting anomalous nodes in multiple contexts of the network. Second, we introduce a generalization of assortativity on networks operating on scalar and categorical node attributes. These coefficients turn out to be useful descriptors to build network fingerprints, so that we can perform supervised graph classification, i.e., predicting the toxicity of molecules or classifying Reddit discussion threads. Besides, we develop an unsupervised approach to learn graph embeddings from a collection of networks. By learning a non-linear mapping from input graphs to a lower-dimensional feature space, we obtain useful graphs representations used in graph visualization, clustering, and classification, e.g., predicting people’s gender based on their structural connectome. Finally, we propose a principled approach to identify stable biomarkers for schizophrenia diagnosis in the human connectome. From an ML perspective, the problem is stated as an embedded feature selection scheme for graph classification. By doing so, we identify the affected-core in schizophrenia, highlighting increasing performance when structural and functional neural features are combined. Resumen: Las redes complejas emergen como marco general en la descripción de problemas reales que incluyen entidades y sus interacciones, ex., redes sociales. Además, otros problemas incluyen colecciones de redes tales como redes multi nivel, redes temporales o redes del cerebro. Con el incremento de la disponibilidad de datos en forma de grafos y metadatos asociados, ex., atributos en nodos o ejes, técnicas de aprendizaje automático en redes han incrementado en popularidad. Sin embargo, múltiples desafíos siguen aún sin ser resueltos, relacionados con representación de grafos, ingeniería de características en grafos, visualización y aplicaciones generales de minería de datos sobre grafos. Esta tesis doctoral contribuye a cerrar la brecha entre dos campos del conocimiento emergentes: aprendizaje automático y ciencia de redes. En esta tesis se investigan sistemas dinámicos sobre redes en la extracción de características estructurales en redes y se introduce una generalización de asortatividad en redes. Dicha asortatividad permite definir descriptores de grafos útiles en la construcción de huellas digitales de redes utilizadas en problemas de aprendizaje supervisado en clasificación de grafos, ex., predicción de toxicidad en moléculas o clasificación de redes sociales Reddit. Además, esta tesis propone un nuevo método no supervisado para crear embeedings de grafos. Gracias a la estimación de una función no lineal, se puede mapear grafos enteros en espacios euclideanos, preservando la similaridad entre grafos de origen. Finalmente, se propone un algoritmo para el diagnóstico de esquizofrenia en el cerebro humano usando connectomes, destacando las regiones del cerebro afectadas por la enfermedad mostrando resultados de alta confiabilidad utilizando distintas representaciones neuronales.

Abstract: Infrastructure plays an essential role in the economy and has a positive impact on social stability. In this regard, bridges are a fundamental component of infrastructure; however, most of the bridges currently under operation were constructed a long time ago and have even passed their useful life. Certainly, Flaig & Lark (2000) noted that most of the current bridges in the UK were built between 1,950 and 1,970. Furthermore, Steve Kroft (2014) indicated that almost 70,000 bridges (one out of every nine bridges) in America are structurally deficient. Consequently, it is necessary to develop fast and efficient inspection techniques that meet current requirements, and that could potentially replace the highly- subjective visual observation methods. This research presents a critical comparison among different Machine-Learning algorithms that are currently used in image classification problems and applied them into the automated recognition of cracks in reinforced concrete images so that they can be used as an alternative for the existing inspection methods. Moreover, ensembles with different combinations of the evaluated models were tested as an incentive to improve the performance of individual models. In total, four algorithms that based their operation in mathematical formulations and 10 different architectures of Convolutional Neural Networks were included. The results showed that the Random Forest classifier outperformed the other mathematical classifiers and performed similarly to the best of the neural networks. In addition, it was found that the ensembled models achieved little or no improvement concerning the best of the classifiers included in the stack. Resumen: La infraestructura desempeña un papel esencial en la economía de los países y tiene un efecto positivo en la estabilidad social de las personas. A este respecto, los puentes son un componente fundamental de la infraestructura; sin embargo, la mayoría de los puentes actualmente en funcionamiento se construyeron hace mucho tiempo e incluso han pasado su vida útil. Ciertamente, Flaig y Lark (2000) observaron que la mayoría de los puentes actuales del Reino Unido se construyeron entre 1.950 y 1.970. Además, Steve Kroft (2014) indicó que casi 70.000 puentes (uno de cada nueve puentes) en USA son estructuralmente deficientes. Por consiguiente, es necesario desarrollar técnicas de inspección rápidas y eficientes que cumplan los requisitos actuales y que puedan sustituir a los métodos de inspección visual altamente subjetivos. Esta investigación presenta una comparación crítica entre los diferentes algoritmos de aprendizaje a máquina que se utilizan actualmente en los problemas de clasificación de imágenes y los aplica al reconocimiento automatizado de grietas en las imágenes de concreto reforzado para que puedan utilizarse como alternativa a los métodos de inspección existentes. Además, se ensayaron conjuntos con diferentes combinaciones de los algoritmos de aprendizaje evaluados como incentivo para mejorar el rendimiento de los modelos individuales. En total, se incluyeron cuatro algoritmos que basaban su funcionamiento en formulaciones matemáticas y 10 arquitecturas diferentes de Redes Neuronales Convolucionales. Los resultados mostraron que el clasificador Random Forest superó a los otros clasificadores matemáticos y tuvo un rendimiento similar a la mejor de las redes neuronales. Además, se encontró que los modelos ensamblados lograron poca o ninguna mejora en lo que respecta al mejor de los clasificadores incluidos en el conjunto.