Improving target localization accuracy of wireless visual sensor networks

This paper discusses the target localization problem of wireless visual sensor networks. Specifically, each node with a low-resolution camera extracts multiple feature points to represent the target at the sensor node level. A statistical method of merging the position information of different sensor nodes to select the most correlated feature point pair at the base station is presented. This method releases the influence of the accuracy of target extraction on the accuracy of target localization in universal coordinate system. Simulations show that, compared with other relative approach, our proposed method can generate more desirable target localization's accuracy, and it has a better trade-off between camera node usage and localization accuracy.

An intelligent portable sensor system in diagnosing stress

Nowadays the stress is a frequent problem in the society. The level of stress could be important in order to recognise health problems later. Electrocardiogram technics allows to supervise the heart condition and the detection of anomalies about the patient. Sometimes the data collection systems by sensors placed on the patient restrict his mobility. Therefore the elimination of wires is a good solution for this trouble. Then the Bluetooth protocol is chosen as way for transmitting and receive data between stations. There are three ECG sensors placed on the right hand, the left hand and the right leg. It is possible to measure the heart signal with this technique. Besides there is an extra sensor in order to measure the temperature of the patient. Depending of the value of these parameters is possible to recognise stress levels. All sensors are connected to a special box with a microcontroller which treat every signal. This module has a Bluetooth part that transmitts wireless the new digital signal to the receiver. This one will be a dongle connected to the computer by Serial Port. A program in the computer has been implemented in order to receive the Bluetooth Data sent from the box and saving the data in a file for subsequent activities. El objetivo principal de este proyecto es el estudio de parámetros como la temperatura corporal y las señales de electrocardiograma para el diagnóstico del estrés. Existen varios estudios que relacionan estos parámetros y sus niveles con posibles casos de estrés y ansiedad. Para este fin usamos unos sensores colocados en el brazo derecho, brazo izquierdo y pierna izquierda. Esto forma el Eindhoven Triangle, que es conocido por dar una señal de electrocardiograma. A su vez también tendremos un sensor de temperatura colocado en un dedo de la mano para medir los grados a los que está el cuerpo en ese momento y así poder detectar ciertas anomalías. Estos sensores están conectados a un modulo que trata las señales analógicas recogidas, las une, y digitaliza para que el modulo transmisor pueda enviar via Bluetooth los datos hacia un receptor colocado en un área cercana. En el módulo hay una electrónica que ayuda a resolver problemas importantes como ruido o interferencias. Este receptor está conectado a un ordenador en el cual he desarrollado una aplicación que implementa el protocolo HCI y cuya funcionalidad es recoger los datos recibidos. Este programa es capaz de crear y gestionar conexiones Bluetooth entre dispositivos. El programa está preparado para que si las conexiones se cortan, se traten en la medida de lo posible los datos recogidos. Los datos se interpretarán y guardarán en un fichero .bin para posteriores usos, como graficaciones y análisis de parámetros. El programa está enteramente hecho en lenguaje Java y tiene un mecanismo de eventos que se activa cada vez que hay datos en el receptor, los recoge y los procesa con el fin de darles un trato posteriormente. Se eligió el formato .bin para los ficheros debido a su pequeño tamaño, ya que aunque sean más laboriosos de usar es mucho más eficiente que un .txt, que en este caso podría ocupar varios megabytes.

A practical approach for outdoors distributed target localization in wireless sensor networks

Wireless sensor networks are posed as the new communication paradigm where the use of small, low-complexity, and low-power devices is preferred over costly centralized systems. The spectra of potential applications of sensor networks is very wide, ranging from monitoring, surveillance, and localization, among others. Localization is a key application in sensor networks and the use of simple, efficient, and distributed algorithms is of paramount practical importance. Combining convex optimization tools with consensus algorithms we propose a distributed localization algorithm for scenarios where received signal strength indicator readings are used. We approach the localization problem by formulating an alternative problem that uses distance estimates locally computed at each node. The formulated problem is solved by a relaxed version using semidefinite relaxation technique. Conditions under which the relaxed problem yields to the same solution as the original problem are given and a distributed consensusbased implementation of the algorithm is proposed based on an augmented Lagrangian approach and primaldual decomposition methods. Although suboptimal, the proposed approach is very suitable for its implementation in real sensor networks, i.e., it is scalable, robust against node failures and requires only local communication among neighboring nodes. Simulation results show that running an additional local search around the found solution can yield performance close to the maximum likelihood estimate.

Detecting unknown attacks in wireless sensor networks that contain mobile nodes

As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

Runtime variability for dynamic reconfiguration in wireless sensor network product lines

Runtime variability is a key technique for the success of Dynamic Software Product Lines (DSPLs), as certain application demand reconfiguration of system features and execution plans at runtime. In this emerging research work we address the problem of dynamic changes in feature models in sensor networks product families, where nodes of the network demand dynamic reconfiguration at post-deployment time.

Multi-agent location system in wireless networks

In this paper we propose a flexible Multi-Agent Architecture together with a methodology for indoor location which allows us to locate any mobile station (MS) such as a Laptop, Smartphone, Tablet or a robotic system in an indoor environment using wireless technology. Our technology is complementary to the GPS location finder as it allows us to locate a mobile system in a specific room on a specific floor using the Wi-Fi networks. The idea is that any MS will have an agent known at a Fuzzy Location Software Agent (FLSA) with a minimum capacity processing at its disposal which collects the power received at different Access Points distributed around the floor and establish its location on a plan of the floor of the building. In order to do so it will have to communicate with the Fuzzy Location Manager Software Agent (FLMSA). The FLMSAs are local agents that form part of the management infrastructure of the Wi-Fi network of the Organization. The FLMSA implements a location estimation methodology divided into three phases (measurement, calibration and estimation) for locating mobile stations (MS). Our solution is a fingerprint-based positioning system that overcomes the problem of the relative effect of doors and walls on signal strength and is independent of the network device manufacturer. In the measurement phase, our system collects received signal strength indicator (RSSI) measurements from multiple access points. In the calibration phase, our system uses these measurements in a normalization process to create a radio map, a database of RSS patterns. Unlike traditional radio map-based methods, our methodology normalizes RSS measurements collected at different locations on a floor. In the third phase, we use Fuzzy Controllers to locate an MS on the plan of the floor of a building. Experimental results demonstrate the accuracy of the proposed method. From these results it is clear that the system is highly likely to be able to locate an MS in a room or adjacent room.

A generalized method for flat plates impact detection and location

In the last years, many analyses from acoustic signal processing have been used for different applications. In most cases, these sensor systems are based on the determination of times of flight for signals from every transducer. This paper presents a flat plate generalization method for impact detection and location over linear links or bars-based structures. The use of three piezoelectric sensors allow to achieve the position and impact time while the use of additional sensors lets cover a larger area of detection and avoid wrong timing difference measurements. An experimental setup and some experimental results are briefly presented.

On the free-boundary for an elliptic inverse nonlocal problem arising in the nuclear fusion. A numerical approach

We consider a mathematical model related to the stationary regime of a plasma magnetically confined in a Stellarator device in the nuclear fusion. The mathematical problem may be reduced to an nonlinear elliptic inverse nonlocal two dimensional free{boundary problem. The nonlinear terms involving the unknown functions of the problem and its rearrangement. Our main goal is to determinate the existence and the estimate on the location and size of region where the solution is nonnegative almost everywhere (corresponding to the plasma region in the physical model)

A 3D multi-objective optimization planning algorithm for wireless sensor networks

The complexity of planning a wireless sensor network is dependent on the aspects of optimization and on the application requirements. Even though Murphy's Law is applied everywhere in reality, a good planning algorithm will assist the designers to be aware of the short plates of their design and to improve them before the problems being exposed at the real deployment. A 3D multi-objective planning algorithm is proposed in this paper to provide solutions on the locations of nodes and their properties. It employs a developed ray-tracing scheme for sensing signal and radio propagation modelling. Therefore it is sensitive to the obstacles and makes the models of sensing coverage and link quality more practical compared with other heuristics that use ideal unit-disk models. The proposed algorithm aims at reaching an overall optimization on hardware cost, coverage, link quality and lifetime. Thus each of those metrics are modelled and normalized to compose a desirability function. Evolutionary algorithm is designed to efficiently tackle this NP-hard multi-objective optimization problem. The proposed algorithm is applicable for both indoor and outdoor 3D scenarios. Different parameters that affect the performance are analyzed through extensive experiments; two state-of-the-art algorithms are rebuilt and tested with the same configuration as that of the proposed algorithm. The results indicate that the proposed algorithm converges efficiently within 600 iterations and performs better than the compared heuristics.

A novel topology control approach to maintain the node degree in dynamic wireless sensor networks

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power.

Design and technical evaluation of an enhanced location-awareness service enabler for spatial disorientation management of elderly with mild cognitive impairment

The progressive ageing of population has turned the mild cognitive impairment (MCI) into a prevalent disease suffered by elderly. Consequently, the spatial disorientation has become a significant problem for older people and their caregivers. The ambient-assisted living applications are offering location-based services for empowering elderly to go outside and encouraging a greater independence. Therefore, this paper describes the design and technical evaluation of a location-awareness service enabler aimed at supporting and managing probable wandering situations of a person with MCI. Through the presence capabilities of the IP multimedia subsystem (IMS) architecture, the service will alert patient's contacts if a hazardous situation is detected depending on his location. Furthermore, information about the older person's security areas has been included in the user profile managed by IMS. In doing so, the service enabler introduced contribute to “context-awareness” paradigm allowing the adaptation and personalization of services depending on user's context and specific conditions or preferences.

Environmental Wireless Sensor Network Deployment in Food Industry: from Theory to Practice

The difficulty behind Wireless Sensor Network deployments in industrial environments not only resides in the number of nodes or the communication protocols but also in the real location of the sensor nodes and the parameters to be monitored. Sensor soiling, high humidity and unreachable locations, among others, make real deployments a very difficult task to plan. Even though it is possible to find myriad approaches for floor planners and deployment tools in the state of the art, most of these problems are very difficult to model and foresee before actually deploying the network in the final scenario. This work shows two real deployments in food factories and how their problems are found and overcome.

A simplified spectral approachfor impedance-based damage identification of frp-strengthened rc beams

Hoy en día, el refuerzo y reparación de estructuras de hormigón armado mediante el pegado de bandas de polímeros reforzados con fibras (FRP) se emplea cada vez con más frecuencia a causa de sus numerosas ventajas. Sin embargo, las vigas reforzadas con esta técnica pueden experimentar un modo de fallo frágil a causa del despegue repentino de la banda de FRP a partir de una fisura intermedia. A pesar de su importancia, el número de trabajos que abordan el estudio de este mecanismo de fallo y su monitorización es muy limitado. Por ello, el desarrollo de metodologías capaces de monitorizar a largo plazo la adherencia de este refuerzo a las estructuras de hormigón e identificar cuándo se inicia el despegue de la banda constituyen un importante desafío a abordar. El principal objetivo de esta tesis es la implementación de una metodología fiable y efectiva, capaz de detectar el despegue de una banda de FRP en una viga de hormigón armado a partir de una fisura intermedia. Para alcanzar este objetivo se ha implementado un procedimiento de calibración numérica a partir de ensayos experimentales. Para ello, en primer lugar, se ha desarrollado un modelo numérico unidimensional simple y no costoso representativo del comportamiento de este tipo vigas de hormigón reforzadas con FRP, basado en un modelo de fisura discreta para el hormigón y el método de elementos espectrales. La formación progresiva de fisuras a flexion y el consiguiente despegue en la interface entre el hormigón y el FRP se formulan mediante la introducción de un nuevo elemento capaz de representar ambos fenómenos simultáneamente sin afectar al procedimiento numérico. Además, con el modelo propuesto, se puede obtener de una forma sencilla la respuesta dinámica en altas frecuencias de este tipo de estructuras, lo cual puede hacer muy útil su uso como herramienta de diagnosis y detección del despegue en su fase inicial mediante una monitorización de la variación de las características dinámicas locales de la estructura. Un método de evaluación no destructivo muy prometedor para la monitorización local de las estructuras es el método de la impedancia usando sensores-actuadores piezoeléctricos (PZT). La impedancia eléctrica de los sensores PZT se puede relacionar con la impedancia mecánica de las estructuras donde se encuentran adheridos Ya que la impedancia mecánica de una estructura se verá afectada por su deterioro, se pueden implementar indicadores de daño mediante una comparación del espectro de admitancia (inversa de la impedancia) a lo largo de distintas etapas durante el periodo de servicio de una estructura. Cualquier cambio en el espectro se podría interpretar como una variación en la integridad de la estructura. La impedancia eléctrica se mide a altas frecuencias con lo cual esta metodología debería ser muy sensible a la detección de estados de daño incipiente local, tal como se desea en la aplicación de este trabajo. Se ha implementado un elemento espectral PZT-FRP como extensión del modelo previamente desarrollado, con el objetivo de poder calcular numéricamente la impedancia eléctrica de sensores PZT adheridos a bandas de FRP sobre una viga de hormigón armado. El modelo, combinado con medidas experimentales captadas mediante sensores PZT, se implementa en el marco de una metodología de calibración de modelos para detectar cuantitativamente el despegue en la interfase entre una banda de FRP y una viga de hormigón. El procedimiento de optimización se resuelve empleando el método del enjambre cooperativo con un algoritmo bagging. Los resultados muestran una gran aproximación en la estimación del daño para el problema propuesto. Adicionalmente, se ha desarrollado también un método adaptativo para el mallado de elementos espectrales con el objetivo de localizar las zonas dañadas a partir de los resultados experimentales, el cual contribuye a aumentar la robustez y efectividad del método propuesto a la hora de identificar daños incipientes en su aparición inicial. Finalmente, se ha llevado a cabo un procedimiento de optimización multi-objetivo para detectar el despegue inicial en una viga de hormigón a escala real reforzada con FRP a partir de las impedancias captadas con una red de sensores PZT instrumentada a lo largo de la longitud de la viga. Cada sensor aporta los datos para definir cada una de las funciones objetivo que definen el procedimiento. Combinando el modelo previo de elementos espectrales con un algoritmo PSO multi-objetivo el procedimiento de detección de daño resultante proporciona resultados satisfactorios considerando la escala de la estructura y todas las incertidumbres características ligadas a este proceso. Los resultados obtenidos prueban la viabilidad y capacidad de los métodos antes mencionados y también su potencial en aplicaciones reales. Abstract Nowadays, the external bonding of fibre reinforced polymer (FRP) plates or sheets is increasingly used for the strengthening and retrofitting of reinforced concrete (RC) structures due to its numerous advantages. However, this kind of strengthening often leads to brittle failure modes being the most dominant failure mode the debonding induced by an intermediate crack (IC). In spite of its importance, the number of studies regarding the IC debonding mechanism and bond health monitoring is very limited. Methodologies able to monitor the long-term efficiency of bonding and successfully identify the initiation of FRP debonding constitute a challenge to be met. The main purpose of this thesisis the implementation of a reliable and effective methodology of damage identification able to detect intermediate crack debonding in FRP-strengthened RC beams. To achieve this goal, a model updating procedure based on numerical simulations and experimental tests has been implemented. For it, firstly, a simple and non-expensive one-dimensional model based on the discrete crack approach for concrete and the spectral element method has been developed. The progressive formation of flexural cracks and subsequent concrete-FRP interfacial debonding is formulated by the introduction of a new element able to represent both phenomena simultaneously without perturbing the numerical procedure. Furthermore, with the proposed model, high frequency dynamic response for these kinds of structures can also be obtained in a very simple and non-expensive way, which makes this procedure very useful as a tool for diagnoses and detection of debonding in its initial stage by monitoring the change in local dynamic characteristics. One very promising active non-destructive evaluation method for local monitoring is impedance-based structural health monitoring(SHM)using piezoelectric ceramic (PZT) sensor-actuators. The electrical impedance of the PZT can be directly related to the mechanical impedance of the host structural component where the PZT transducers are attached. Since the structural mechanical impedance will be affected by the presence of structural damage, comparisons of admittance (inverse of impedance) spectra at various times during the service period of the structure can be used as damage indicator. Any change in the spectra might be an indication of a change in the structural integrity. The electrical impedance is measured at high frequencies with which this methodology appears to be very sensitive to incipient damage in structural systems as desired for our application. Abonded-PZT-FRP spectral beam element approach based on an extension of the previous discrete crack approach is implemented in the calculation of the electrical impedance of the PZT transducer bonded to the FRP plates of a RC beam. This approach in conjunction with the experimental measurements of PZT actuator-sensors mounted on the structure is used to present an updating methodology to quantitatively detect interfacial debonding between a FRP strip and the host RC structure. The updating procedure is solved by using an ensemble particle swarm optimization approach with abagging algorithm, and the results demonstrate a big improvement for the performance and accuracy of the damage detection in the proposed problem. Additionally, an adaptive strategy of spectral element mesh has been also developed to detect damage location with experimental results, which shows the robustness and effectiveness of the proposed method to identify initial and incipient damages at its early stage. Lastly, multi-objective optimization has been carried out to detect debonding damage in a real scale FRP-strengthened RC beam by using impedance signatures. A net of PZT sensors is distributed along the beam to construct impedance-based multiple objectives under gradually induced damage scenario. By combining the spectral element model presented previously and an ensemble multi-objective PSO algorithm, the implemented damage detection process yields satisfactory predictions considering the scale and uncertainties of the structure. The obtained results prove the feasibility and capability of the aforementioned methods and also their potentials in real engineering applications.

Application of PZT and FBG impedance sensors for structural health monitoring of reinforced concrete beams strengthened with FRP composite materials

Esta Tesis tiene como objetivo principal el desarrollo de métodos de identificación del daño que sean robustos y fiables, enfocados a sistemas estructurales experimentales, fundamentalmente a las estructuras de hormigón armado reforzadas externamente con bandas fibras de polímeros reforzados (FRP). El modo de fallo de este tipo de sistema estructural es crítico, pues generalmente es debido a un despegue repentino y frágil de la banda del refuerzo FRP originado en grietas intermedias causadas por la flexión. La detección de este despegue en su fase inicial es fundamental para prevenir fallos futuros, que pueden ser catastróficos. Inicialmente, se lleva a cabo una revisión del método de la Impedancia Electro-Mecánica (EMI), de cara a exponer sus capacidades para la detección de daño. Una vez la tecnología apropiada es seleccionada, lo que incluye un analizador de impedancias así como novedosos sensores PZT para monitorización inteligente, se ha diseñado un procedimiento automático basado en los registros de impedancias de distintas estructuras de laboratorio. Basándonos en el hecho de que las mediciones de impedancias son posibles gracias a una colocación adecuada de una red de sensores PZT, la estimación de la presencia de daño se realiza analizando los resultados de distintos indicadores de daño obtenidos de la literatura. Para que este proceso sea automático y que no sean necesarios conocimientos previos sobre el método EMI para realizar un experimento, se ha diseñado e implementado un Interfaz Gráfico de Usuario, transformando la medición de impedancias en un proceso fácil e intuitivo. Se evalúa entonces el daño a través de los correspondientes índices de daño, intentando estimar no sólo su severidad, sino también su localización aproximada. El desarrollo de estos experimentos en cualquier estructura genera grandes cantidades de datos que han de ser procesados, y algunas veces los índices de daño no son suficientes para una evaluación completa de la integridad de una estructura. En la mayoría de los casos se pueden encontrar patrones de daño en los datos, pero no se tiene información a priori del estado de la estructura. En este punto, se ha hecho una importante investigación en técnicas de reconocimiento de patrones particularmente en aprendizaje no supervisado, encontrando aplicaciones interesantes en el campo de la medicina. De ahí surge una idea creativa e innovadora: detectar y seguir la evolución del daño en distintas estructuras como si se tratase de un cáncer propagándose por el cuerpo humano. En ese sentido, las lecturas de impedancias se emplean como información intrínseca de la salud de la propia estructura, de forma que se pueden aplicar las mismas técnicas que las empleadas en la investigación del cáncer. En este caso, se ha aplicado un algoritmo de clasificación jerárquica dado que ilustra además la clasificación de los datos de forma gráfica, incluyendo información cualitativa y cuantitativa sobre el daño. Se ha investigado la efectividad de este procedimiento a través de tres estructuras de laboratorio, como son una viga de aluminio, una unión atornillada de aluminio y un bloque de hormigón reforzado con FRP. La primera ayuda a mostrar la efectividad del método en sencillos escenarios de daño simple y múltiple, de forma que las conclusiones extraídas se aplican sobre los otros dos, diseñados para simular condiciones de despegue en distintas estructuras. Demostrada la efectividad del método de clasificación jerárquica de lecturas de impedancias, se aplica el procedimiento sobre las estructuras de hormigón armado reforzadas con bandas de FRP objeto de esta tesis, detectando y clasificando cada estado de daño. Finalmente, y como alternativa al anterior procedimiento, se propone un método para la monitorización continua de la interfase FRP-Hormigón, a través de una red de sensores FBG permanentemente instalados en dicha interfase. De esta forma, se obtienen medidas de deformación de la interfase en condiciones de carga continua, para ser implementadas en un modelo de optimización multiobjetivo, cuya solución se haya por medio de una expansión multiobjetivo del método Particle Swarm Optimization (PSO). La fiabilidad de este último método de detección se investiga a través de sendos ejemplos tanto numéricos como experimentales. ABSTRACT This thesis aims to develop robust and reliable damage identification methods focused on experimental structural systems, in particular Reinforced Concrete (RC) structures externally strengthened with Fiber Reinforced Polymers (FRP) strips. The failure mode of this type of structural system is critical, since it is usually due to sudden and brittle debonding of the FRP reinforcement originating from intermediate flexural cracks. Detection of the debonding in its initial stage is essential thus to prevent future failure, which might be catastrophic. Initially, a revision of the Electro-Mechanical Impedance (EMI) method is carried out, in order to expose its capabilities for local damage detection. Once the appropriate technology is selected, which includes impedance analyzer as well as novel PZT sensors for smart monitoring, an automated procedure has been design based on the impedance signatures of several lab-scale structures. On the basis that capturing impedance measurements is possible thanks to an adequately deployed PZT sensor network, the estimation of damage presence is done by analyzing the results of different damage indices obtained from the literature. In order to make this process automatic so that it is not necessary a priori knowledge of the EMI method to carry out an experimental test, a Graphical User Interface has been designed, turning the impedance measurements into an easy and intuitive procedure. Damage is then assessed through the analysis of the corresponding damage indices, trying to estimate not only the damage severity, but also its approximate location. The development of these tests on any kind of structure generates large amounts of data to be processed, and sometimes the information provided by damage indices is not enough to achieve a complete analysis of the structural health condition. In most of the cases, some damage patterns can be found in the data, but none a priori knowledge of the health condition is given for any structure. At this point, an important research on pattern recognition techniques has been carried out, particularly on unsupervised learning techniques, finding interesting applications in the medicine field. From this investigation, a creative and innovative idea arose: to detect and track the evolution of damage in different structures, as if it were a cancer propagating through a human body. In that sense, the impedance signatures are used to give intrinsic information of the health condition of the structure, so that the same clustering algorithms applied in the cancer research can be applied to the problem addressed in this dissertation. Hierarchical clustering is then applied since it also provides a graphical display of the clustered data, including quantitative and qualitative information about damage. The performance of this approach is firstly investigated using three lab-scale structures, such as a simple aluminium beam, a bolt-jointed aluminium beam and an FRP-strengthened concrete specimen. The first one shows the performance of the method on simple single and multiple damage scenarios, so that the first conclusions can be extracted and applied to the other two experimental tests, which are designed to simulate a debonding condition on different structures. Once the performance of the impedance-based hierarchical clustering method is proven to be successful, it is then applied to the structural system studied in this dissertation, the RC structures externally strengthened with FRP strips, where the debonding failure in the interface between the FRP and the concrete is successfully detected and classified, proving thus the feasibility of this method. Finally, as an alternative to the previous approach, a continuous monitoring procedure of the FRP-Concrete interface is proposed, based on an FBGsensors Network permanently deployed within that interface. In this way, strain measurements can be obtained under controlled loading conditions, and then they are used in order to implement a multi-objective model updating method solved by a multi-objective expansion of the Particle Swarm Optimization (PSO) method. The feasibility of this last proposal is investigated and successfully proven on both numerical and experimental RC beams strengthened with FRP.

How should the sustainability of the location of dry ports be measured? A proposed methodology using Bayesian networks and multi-criteria decision analysis

The global economic structure, with its decentralized production and the consequent increase in freight traffic all over the world, creates considerable problems and challenges for the freight transport sector. This situation has led shipping to become the most suitable and cheapest way to transport goods. Thus, ports are configured as nodes with critical importance in the logistics supply chain as a link between two transport systems, sea and land. Increase in activity at seaports is producing three undesirable effects: increasing road congestion, lack of open space in port installations and a significant environmental impact on seaports. These adverse effects can be mitigated by moving part of the activity inland. Implementation of dry ports is a possible solution and would also provide an opportunity to strengthen intermodal solutions as part of an integrated and more sustainable transport chain, acting as a link between road and railway networks. In this sense, implementation of dry ports allows the separation of the links of the transport chain, thus facilitating the shortest possible routes for the lowest capacity and most polluting means of transport. Thus, the decision of where to locate a dry port demands a thorough analysis of the whole logistics supply chain, with the objective of transferring the largest volume of goods possible from road to more energy efficient means of transport, like rail or short-sea shipping, that are less harmful to the environment. However, the decision of where to locate a dry port must also ensure the sustainability of the site. Thus, the main goal of this article is to research the variables influencing the sustainability of dry port location and how this sustainability can be evaluated. With this objective, in this paper we present a methodology for assessing the sustainability of locations by the use of Multi-Criteria Decision Analysis (MCDA) and Bayesian Networks (BNs). MCDA is used as a way to establish a scoring, whilst BNs were chosen to eliminate arbitrariness in setting the weightings using a technique that allows us to prioritize each variable according to the relationships established in the set of variables. In order to determine the relationships between all the variables involved in the decision, giving us the importance of each factor and variable, we built a K2 BN algorithm. To obtain the scores of each variable, we used a complete cartography analysed by ArcGIS. Recognising that setting the most appropriate location to place a dry port is a geographical multidisciplinary problem, with significant economic, social and environmental implications, we consider 41 variables (grouped into 17 factors) which respond to this need. As a case of study, the sustainability of all of the 10 existing dry ports in Spain has been evaluated. In this set of logistics platforms, we found that the most important variables for achieving sustainability are those related to environmental protection, so the sustainability of the locations requires a great respect for the natural environment and the urban environment in which they are framed.