Teses de doutoramento

This research aims at developing a comprehensive approach for assessing the energy use and efficiency in wastewater systems, considering the water-energy-greenhouse gas (W-E-G) emissions nexus supported by methods and tools, such as a tailored energy balance and a performance assessment system (PAS). This approach is aligned with continuous improvement principles and allows carrying out the diagnosis of energy efficiency in wastewater systems supporting the building of a portfolio of energy use improvement measures responding to strategic objectives and attending to the systems

A presente tese tem como objetivo melhorar a gestÆo dos sistemas de distribui‡Æo de  gua atrav‚s da utiliza‡Æo de m‚todos probabil¡sticos e estat¡sticos para analisar os dados de contadores inteligentes. Assim, ‚ proposta uma metodologia para avalia‡Æo da incerteza em medi‡äes de caudal dinƒmicas, baseada apenas nas pr¢prias medi‡äes, no m‚todo de bootstrap por blocos e no ajustamento de um modelo adequado aos dados em estudo. De seguida, ‚ desenvolvida uma metodologia de amostragem probabil¡stica estratificada dos clientes para identifica‡Æo de uma amostra representativa onde deverÆo ser instalados contadores inteligentes, sendo os estratos definidos pelos grupos obtidos numa an lise de clusters baseada em dados de fatura‡Æo.  ainda apresentada uma metodologia para a decomposi‡Æo÷ de s‚ries temporais de caudal em consumo autorizado, consumo nÆo autorizado, roturas e perdas, utilizando t‚cnicas de suaviza‡Æo de s‚ries temporais e an lise do espetro singular (singular sprectum analysis). Por £ltimo, sÆo testados alguns m‚todos robustos e de analise funcional para dete‡Æo de valores an¢malos em series temporais de caudal.

Os efeitos das altera‡äes clim ticas aumentaram a necessidade de investigar a capacidade da vegeta‡Æo em atenuar a for‡a das ondas, nas zonas costeiras. Apoiando-se em modela‡Æo f¡sica, anal¡tica e num‚rica da propaga‡Æo de ondas sobre vegeta‡Æo, este estudo teve como objetivos: i) analisar a diferen‡a na dissipa‡Æo de ondas sobre campos de vegeta‡Æo r¡gida e flex¡vel, ii) caracterizar o movimento de vegeta‡Æo flex¡vel, iii) estimar o coeficiente de arrasto de vegeta‡Æo r¡gida e vegeta‡Æo flex¡vel, e iv) analisar a influˆncia da flexibilidade (movimentoda vegeta‡Æo) e da for‡a de in‚rcia no coeficiente de arrasto.

Drinking water storage tanks are essential components of water supply systems to store water, to level off pressure in networks and to meet emergency storage. They are also frequent sources of deterioration of drinking water quality and safety owing to inadequate tank design and operation. Mostly, existing tanks design, dimensions and operation do not account for water mixing and renewal.

Nature-Based Solutions (NBS) are crucial to achieving the goals of the United Nations Agenda 2030 for sustainable development and other global agendas, such as the Paris Agreement on Climate Change. In the last decade, the NBS umbrella concept has become more relevant intending to contribute to urban resilience and to address the climate change challenges. NBS are living solutions inspired by nature that use or mimic natural processes intending to face several societal challenges, from the perspective of resource-efficient use and the promotion of economic and environmental benefits. Currently, cities are encouraged to understand and measure the NBS contribution to identify adequate strategies for enhancing resilience and prioritize investments accordingly. The objective of the present thesis is to promote and enhance the NBS implementation in cities, focused on solutions for stormwater management and control. Based on the analysis of their contribution to urban resilience, the potential to meet environmental, social, and economic challenges and to adapt across diverse urban scales and contexts is demonstrated. In this sense, a Resilience Assessment Framework (RAF) to assess the NBS contribution to urban resilience was developed. This framework aims to assess the NBS contribution to urban resilience. Moreover, a Guidance for the RAF application in cities with different resilience maturity was developed, which involved the RAF validation by seven cities (from a national and international context), that participated voluntarily in this phase. In this context, the developed RAF is comprehensive and multidimensional. It is driven by the definition of objectives, criteria, and metrics, according to the proposed structure for assessing the water supply and wastewater system service performance in the framework of the ISO 24500 standards of the International Organization for Standardization. For an oriented assessment of the criteria, qualitative and quantitative metrics were defined, considering data from different sources and complexity. Reference values were also identified and metrics' classifications were defined. In this classification, each answer is associated with a resilience development level, intending to assess the NBS contribution to urban resilience on a normalized scale. To support the RAF application to cities with different resilience maturity (in terms of resilience and available information), three analysis degrees (essential, complementary, and comprehensive) and a set of metrics were proposed, which are pre-defined in the guidance for the RAF application. To support the selection of the analysis degree more adequate to any city, a structure to characterize the city's profile was developed. This complementary profile also supports the interpretation of the RAF results, both at the city and at the level of specific NBS. To conclude, the application of the RAF essential analysis degree to the seven participating cities, with different challenges regarding urban resilience and NBS, and of the comprehensive analysis degree (complete RAF) to Porto´s urban area, through an assemble assessment at the city and NBS levels, were presented.

Over the past two decades, performance assessment (PA) has been one of the areas show-ing the greatest advancement in the water sector. PA aims to measure effectiveness and/or efficiency of an activity or process using performance metrics, supporting the decision-making process and the continuous improvement of water utilities. Despite the potential benefits, few projects, and initiatives on PA have been undertaken regarding storm water systems (SWS). The present thesis aims to develop a performance assessment framework (PAF), applicable to both conventional SWS and sustainable urban drainage systems (SUDS), objective driven and focused on systems’ functioning aspects, to promote the adoption of PA by water utilities, municipalities, and other institutional organisations. The developed PAF integrates eight objectives, 25 assessment criteria and 80 perfor-mance metrics. The PAF is applied to five real case studies served by conventional SWS or SUDS, to test its applicability and validate its components. The assessment of two case studies served by conventional SWS, as part of the internal validation phase, revealed problems related to flooding occurrences, illicit domestic connections, insufficient hy-draulic capacity, and lack of self-cleaning capacity in some pipes. The SUDS case study translated an overall acceptable performance, with the identification of the advantage of management trains in SUDS layout. The external validation phase was carried out in col-laboration with two Portuguese water utilities, leading to the consolidation of the PAF proposal. For each water utility, case studies were selected. The assessment identified areas with vulnerabilities to flooding occurrences and parts of the SW infrastructure need-ing urgent rehabilitation. Lack of funding, data availability and regulatory frameworks for SWS are some of the listed factors that hinder the PA implementation. The PAF pro-vides a reference basis in the sector, applicable to different types of SWS, covering dif-ferent performance dimensions. It facilitates and promotes the implementation of PA, to prepare for current and future challenges.

Infrastructure asset management (IAM) has been increasingly becoming a key topic in the mind-set shift of the water sector managers and policy makers, allowing to assist in the operational, tactical and strategic decision-making in urban water infrastructures. Nevertheless, IAM implementation by the water utilities is still far from needed and not widespread, due to several challenges and constraints, mainly associated with limited human, technological and financial resources. The current research aims at enhancing urban water infrastructure asset management through an integrated decision-making approach, that includes novel methodologies for condition assessment, service life prediction and cost modelling. This approach can be applied at three assessment levels – macro, meso and micro – allowing the identification of intervention priority assets and the study of different long-term rehabilitation solutions, when applied at the meso and macro level, and the diagnosis, prioritization and study of different intervention solutions at the micro assessment level.

Transportation infrastructures are vital to our society. These infrastructures naturally tend to deteriorate over time. A set of activities is then required to manage existing assets in accordance with sound management principles. These activities are usually integrated into systems that manage infrastructures according to technical, social and economic aspects. This research aims to examine the potential of applying machine learning techniques to current transportation infrastructure management systems. Machine learning is an application of artificial intelligence enabling systems to automatically learn and improve from experience (data), arriving at solutions not explicitly programmed. Powered by algorithms that can learn from data, machine learning leads to efficient and effective systems that improve over time. Over the last few years, the application of machine learning has transformed several industries and could do so for transportation infrastructure as well. To achieve the research objectives, a set of studies was performed. Each of these studies had a specific objective, the set of which can be expressed as follows: to define combined performance indicators for pavement condition assessment using machine learning; to develop machine learning models for pavement performance prediction; to formulate machine learning solutions to overcome pavement data issues, such as missing data; to integrate machine learning techniques into decision support systems for pavement maintenance management. Together these studies explored the development of machine learning applications for solving some standard pavement management problems, providing insights into the application of machine learning to transportation infrastructure management systems. The studies successfully demonstrated the potential of machine learning applications for pavement management systems. It showed that machine learning is applicable to various transportation asset management problems, such as condition assessment, performance prediction, and decision support, and that it is able to outperform some of the analytical techniques currently used. Overall, this research work finds that machine learning is a promising tool for transportation infrastructure management systems.

Wireless Sensor Networks (WSN) are being progressively used in several application areas, particularly to collect data and monitor physical processes. Moreover, sensor nodes used in environmental monitoring applications, such as the aquatic sensor networks, are often subject to harsh environmental conditions while monitoring complex phenomena. Non-functional requirements, like reliability, security or availability, are increasingly important and must be accounted for in the application development. For that purpose, there is a large body of knowledge on dependability techniques for distributed systems, which provides a good basis to understand how to satisfy these non-functional requirements of WSN-based monitoring applications. Given the data-centric nature of monitoring applications, it is of particular importance to ensure that data is reliable or, more generically, that it has the necessary quality. The problem of ensuring the desired quality of data for dependable monitoring using WSNs is studied herein. With a dependability-oriented perspective, it is reviewed the possible impairments to dependability and the prominent existing solutions to solve or mitigate these impairments. Despite the variety of components that may form a WSN-based monitoring system, it is given particular attention to understanding which faults can affect sensors, how they can affect the quality of the information, and how this quality can be improved and quantified. Open research issues for the specific case of aquatic monitoring applications are also discussed. One of the challenges in achieving a dependable system behavior is to overcome the external disturbances affecting sensor measurements and detect the failure patterns in sensor data. This is a particular problem in environmental monitoring, due to the difficulty in distinguishing a faulty behavior from the representation of a natural phenomenon. Existing solutions for failure detection assume that physical processes can be accurately modeled, or that there are large deviations that may be detected using coarse techniques, or more commonly that it is a high-density sensor network with value redundant sensors. This thesis aims at defining a new methodology for dependable data quality in environmental monitoring systems, aiming to detect faulty measurements and increase the sensors data quality. The framework of the methodology is overviewed through a generically applicable design, which can be employed to any environment sensor network dataset. The methodology is evaluated in various datasets of different WSNs, where it is used machine learning to model each sensor behavior, exploiting the existence of correlated data provided by neighbor sensors. It is intended to explore the data fusion strategies in order to effectively detect potential failures for each sensor and, simultaneously, distinguish truly abnormal measurements from deviations due to natural phenomena. This is accomplished with the successful application of the methodology to detect and correct outliers, offset and drifting failures in real monitoring networks datasets. In the future, the methodology can be applied to optimize the data quality control processes of new and already operating monitoring networks, and assist in the networks maintenance operations.

A state-of-the-art revision of immersed tunnels is done. The selected case-study for this thesis, an immersed tunnel for crossing Tagus River between Algés and Trafaria, is presented. Next, cases of earthquake-induced liquefaction and its associated mechanism are discussed. The most relevant constitutive models for the numerical simulation of the response of soils under cyclic loading are described, including the Manzari-Dafalias model. Subsequently, Tagus River sand is physically characterized and six monotonic drained triaxial tests are analysed, with the goal of characterizing the stress-strain behaviour of the sand and obtain its parameters. Five cyclic undrained torsional tests are also analysed with the goal of characterizing cyclic behaviour of the sand. Then, the calibration framework for the Manzari-Dafalias model is presented, combining results of laboratory tests with numerical sensitivity studies. A parameter sensitivity analysis is carried out to understand the relevance of some chosen model parameters, by using an OpenSees constitutive driver, both through numerical simulation of monotonic drained triaxial tests and of cyclic undrained torsional tests. Some model parameters are calibrated directly from triaxial testing. The remaining parameters are calibrated through numerical simulation and curve fitting of the model to the laboratory results. A new constitutive driver is implemented in MATLAB to clarify some of the Manzari-Dafalias model issues, namely in the liquefaction phase. Finally, multiple liquefaction mitigation measures, and their application in immersed tunnels, are described. Laboratory testing of a mitigation measure, specifically injection of a duromeric expansive polyurethane resin, commercially available, is accomplished. The physical characteristics of both the resin and of the sand-resin mixture are presented. A series of tests, namely high frequency ultrasonic pulse tests, uniaxial compression and tensile tests, and triaxial compression tests, are performed. The modulus of elasticity, the Poisson’s ratio, the uniaxial compressive and tensile strengths, the triaxial compressive strength and the shear strength parameters, are determined. Two additional injection tests are executed to check densification of the sand between injection columns and its relative density is determined. Finally, the main conclusions are summarized and guidelines for future developments are established.