Doctoral School

Public display of deposited theses

Submission of objections to a doctoral thesis within the period of public exhibition

In accordance with the Academic Regulations for Doctoral Studies, doctors may request access to a doctoral thesis in deposit for consultation and, if there are, to send to the Permanent Commission of the Doctoral School the observations and allegations that they consider opportune on the content.

DOCTORAL DEGREE IN APPLIED MATHEMATICS

  • ABUASAKER, WALAA: A Contribution to Group Decision Aiding by means of Multi-Perceptual Unbalanced Linguistic Assessments.
    Author: ABUASAKER, WALAA
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN APPLIED MATHEMATICS
    Department: School of Mathematics and Statistics (FME)
    Mode: Normal
    Deposit date: 28/07/2025
    Deposit END date: 08/09/2025
    Thesis director: SANCHEZ SOLER, MONICA | AGELL JANÉ, NÚRIA
    Thesis abstract: In decision-aiding environments under uncertainty, assessments are often expressed in linguistic terms whose meanings can vary significantly between individuals. In this thesis, this variation is modeled through the concept of a linguistic perceptual map, grounded in the lattice structure of hesitant fuzzy linguistic term sets. Each individual or group is associated with a unique linguistic perceptual map reflecting their interpretation of linguistic assessments. This doctoral research introduces a novel framework for projecting and aggregating individual assessments onto a common perceptual map, enabling the derivation of a central opinion and an associated measure of consensus. This thesis extensively studies the mathematical properties of the projection function. The thesis provides a theorem proving that the projection function is a monomorphism between lattices. In addition, it is also proven that this projection function preserves crucial order relations. Furthermore, this thesis progresses beyond existing research by introducing an interpretation function. This function facilitates the translation of the aggregated result from the common perceptual map back to each individual’s original linguistic perceptual map. The properties of the interpretation function are also subject to analysis, demonstrating its role in preserving previous order relations, despite not being a morphism. This doctoral research presents a multi-perceptual collective decision-aiding methodology built on the mathematical concepts introduced in this work. Finally, to illustrate the practicality of the proposed methodology, it is applied to three real-world case studies. The first analyzes data of ratings from the Amazon Books platform, the second considers data from the Too Good To Go platform to explore public interest in food waste reduction, and the third examines data of news coverage across European countries concerning the Israel-Gaza war.
  • PARDO ARAUJO, MARTA: Understanding the Impact of Climate and Human Mobility on the Spread of Invasive Mosquito Vectors and Diseases
    Author: PARDO ARAUJO, MARTA
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN APPLIED MATHEMATICS
    Department: School of Mathematics and Statistics (FME)
    Mode: Normal
    Deposit date: 28/07/2025
    Deposit END date: 08/09/2025
    Thesis director: BARTUMEUS FERRÉ, FREDERIC | ALONSO GIMENEZ, DAVID
    Thesis abstract: Global change, driven by climate shifts, habitat alteration, and human activity, has accelerated the spread of invasive species and pathogens into previously unaffected regions. Once established, these species can disrupt ecosystems, introduce diseases, and threaten public health and economies. In Europe, this has been evident through the COVID-19 pandemic and recent dengue outbreaks in Mediterranean countries. The latter driven by: the arrival and establishment of mosquito vectors, the subsequent regional spread of these vectors, and the arrival of infected individuals from dengue endemic areas. The combination of global transportation networks and increasingly favourable conditions for invasive species has made their tracking and prevention particularly challenging. In response, recent advances in technology and mathematical modelling have enhanced our ability to understand and address these challenges. This thesis investigates how climate and human mobility influence the spread of invasive mosquito vectors and associated diseases.Aedes mosquitoes are invasive vectors that are colonizing various regions in Europe. In this thesis we focus on: Aedes albopictus, Aedes aegypti, Aedes koreicus, and Aedes japonicus, with particular emphasis on Ae. albopictus, the main vector responsible for recent dengue outbreaks in Europe. As ectothermic organisms, mosquitoes are highly sensitive to environmental conditions; their development rates and the time needed to become infectious are strongly temperature-dependent. In this work, we examine how environmental factors, including temperature, rainfall, human population density, and land use, affect the population dynamics of these species and how these factors relate to their current spatial distribution. We calculated the mosquito reproduction number, which we used as an indicator of habitat suitability. Our results reveal that the four species possess unique thermal performance profiles. Ae. aegypti showed the highest optimal temperature, whereas \textit{Ae. japonicus} had the lowest. We further investigate the impact of human mobility on the spread of \textit{Ae. albopictus} in Spain using a metapopulation model enriched with high-resolution mosquito occurrence and human mobility data. Our results show that human movement is a key driver of mosquito spread, with both natural and human-mediated dispersal jointly promoting complex and multi-scale spreading. We observed a minor influence of climate on spread dynamics, highlighting dispersal as the primary factor shaping the spatial distribution of the species. Additionally, we identified a minimum temperature threshold of -12°C which hinders Ae. albopictus establishment. Finally, this thesis examines how human mobility affects disease spread within a connected system, considering two mobility types: commuting (short-term, round-trip movement) and migration (long-term or permanent relocation). By applying random matrix theory, we derived a threshold equivalent to the basic reproduction number and identified the main drivers of disease propagation. Our results show that the average commuting rate plays a crucial role in disease distribution across the system. This result aligns with our findings on mosquito invasion dynamics, where human mobility similarly governs the distribution of colonized patches.

DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY

  • GONZÁLEZ ESPINOSA, VANESSA: Diseño de materiales cementícios reforzados con fibras vegetales impregnadas con materiales de cambio de fase para mejorar el comportamiento térmico de las cubiertas de los edificios.
    Author: GONZÁLEZ ESPINOSA, VANESSA
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY
    Department: Department of Architectural Technology (TA)
    Mode: Normal
    Deposit date: 31/07/2025
    Deposit END date: 12/09/2025
    Thesis director: CLARAMUNT BLANES, JOSE | LACASTA PALACIO, ANA MARIA
    Thesis abstract: In the current context, sustainable construction prioritises innovative materials that combine energy efficiency, mechanical strength and safety against extreme conditions, such as fire, in order to address the challenges of climate change and human needs. Fibre-reinforced cementitious composites and phase change materials (PCM) are emerging as a promising solution, particularly in raised roof pavements, where thermal regulation is essential. The main objective of this thesis is to develop a cement board reinforced with non-woven vegetable fibres and PCM for raised roof pavements, determining the optimal dosage of cement, fibres and PCM that maximises mechanical resistance and thermal regulation capacity, as well as analysing its response to fire. The research seeks to advance the integration of PCM and plant fibres into cementitious matrices, proposing improvements for their practical application in construction with an environmental focus.The methodology, which is highly experimental in nature, was structured in several stages. First, the state of the art on cementitious composites and PCM was reviewed, identifying trends and challenges. Subsequently, an experimental campaign was designed that included: selection of materials (commercial cements, non-woven fibres and pure and microencapsulated PCMs), formulation of mixtures with different proportions of fibres and PCMs compared to a control without PCMs, evaluation of mechanical properties (flexural strength), thermal properties (conductivity, thermal storage and retardation) and fire behaviour through standardised tests, and statistical analysis to determine the impact of each component.The results show that the incorporation of PCM RT28 through direct impregnation into non-woven fibres in cementitious composites improves thermal properties, achieving a delay in temperature changes comparable to that of microencapsulated PCM mixed into the cementitious matrix. However, the composite made with PCM RT28 has superior mechanical strength, with a modulus of rupture (MOR) approximately three times greater than that of the microencapsulated composite, although both show a decrease in strength compared to samples without PCM. The non-woven fibres, by effectively impregnating the PCM, reinforce the cohesion of the composite and preserve hardening by deformation, partially mitigating the loss of mechanical strength.Although both the vegetal fibres and the PCM used are organic in nature and therefore combustible, the cementitious composites exhibited good fire performance, with low-intensity flames and a high self-extinguishing capacity once the heat source was removed. Overall, the samples with PCM incorporated through fibre impregnation showed better fire behaviour than those formulated with microencapsulated PCM: although ignition occurred slightly earlier, the total heat released (THR), as measured in the cone calorimeter tests, was substantially lower.This combination, which has been little explored, balances thermal efficiency and structural functionality, with direct applications in sustainable buildings. The research provides a detailed analysis of the interaction between plant fibres, PCM and the cement matrix, proposing optimal dosages and strategies to mitigate fire-related risks. The results lay the foundations for future research and practical applications, promoting the development of more efficient and sustainable building materials.
  • ROODNESHIN, MARYAM: Strategies for urban progress toward higher renewable energy: a PV-centric approach with a clustering methodology. Case study in Barcelona
    Author: ROODNESHIN, MARYAM
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN ARCHITECTURAL, BUILDING CONSTRUCTION AND URBANISM TECHNOLOGY
    Department: Department of Architectural Technology (TA)
    Mode: Normal
    Deposit date: 28/07/2025
    Deposit END date: 08/09/2025
    Thesis director: MUROS ALCOJOR, ADRIAN | MASSECK, TORSTEN ANDREAS
    Thesis abstract: This dissertation presents a comprehensive study on the integration ofintegrating photovoltaic (PV) systems within urban environments, focusing on Barcelona as a case study. It explores innovative approaches to strivingachieving for nearly 100%higher renewable energy adoption by leveraging a PV-centric methodology enhanced through clustering strategies. The research emphasizes emphasises the interplay between architectural design, construction techniques, and urban planning to optimize optimise energy efficiency and utilizationutilisation, addressing critical environmental challenges such as CO2 emissions and air pollution.Through the integration of computer simulations, literature reviews, and empirical analyses, the study evaluates Barcelona’s solar potential and the impact of morphological indicators—such as density, building height, and orientation—on energy performance. The findings provide actionable insights for urban planners and architects, proposing advanced solar grid designs that minimize minimise reliance on fossil fuels while promoting sustainable urban development.A GIS-based framework integrates urban spatial data to assess solar potential and financial viability, incorporating parameters such as building density, height, and orientation A GIS-based framework is employed to assess the solar potential and financial viability of PV systems across five representative Barcelona neighbourhoods, incorporating a detailed analysis of a specific area within each neighbourhood. By modelling district-specific characteristics and optimizing optimising urban layouts, the study identifies key factors influencing energy efficiency, including urban form and energy capture/dissipation dynamics. A clustering strategy organizes organises PV arrays based on proximity and solar energy uniformity using algorithms like K-means and 3D proximity, striking a balance between energy production, cost, and environmental impact.Optimization Optimisation scenarios evaluate PV system parameters—such as module dimensions, tilt, orientation, and spacing—assuming multi-crystalline silicon (mc-Si) technology with 20% efficiency. Key constraints, including roof occupation limits, maximum ground coverage ratios, and shading reduction, are addressed to enhance energy yield, efficiency, and cost-effectiveness. The evolutionary algorithm within Grasshopper’s Galapagos component refines system designs by analysing metrics such as Levelizedised Cost of Energy (LCOE), energy yield, and CO2 emissions.The research introduces the concept of “solar neighbourhoods,” emphasizing emphasising the integration of reduced energy consumption and maximized maximised energy production through strategic urban design. Findings The findings highlight the critical role of neighbourhood geometry, orientation, and compactness in optimising PV panel placement and improving environmental performance. By reducing ground coverage ratios, enhancing energy production, and ensuring financial viability, these strategies contribute to the development of resilient and sustainable urban landscapes.This study bridges the gap between theoretical research and practical applications, offering a roadmap for urban planners and policymakers to integrate PV systems and renewable energy solutions into city development. Recommendations include optimizing optimising urban layouts, tailoring building geometries, and enhancing solar exposure to address energy and environmental challenges. By aligning financial, environmental, and operational considerations, the research provides a holistic framework for fostering sustainable urban development and resilience, charting a path toward greener, energy-efficient global cities.

DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE

  • BAGHERZADE GHAZVINI, MINA: A Data-driven Intelligent Decision Support Framework for Process Operation Management. An Application to Gas Turbine Process.
    Author: BAGHERZADE GHAZVINI, MINA
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN ARTIFICIAL INTELLIGENCE
    Department: Department of Computer Science (CS)
    Mode: Normal
    Deposit date: 25/07/2025
    Deposit END date: 05/09/2025
    Thesis director: SANCHEZ MARRE, MIQUEL | ANGULO BAHON, CECILIO
    Thesis abstract: This thesis presents a data-driven framework for enhancing the management and maintenance of industrial processes, exemplified through a case study involving gas turbines. The research focuses on several key areas: preprocessing operational data, identifying operational modes, analysing transitions between these modes, and detecting patterns for predictive maintenance. The research proposal begins with detailed data preprocessing to ensure the quality and usability of data. It then introduces methods for automatically recognising distinct operational modes using an ensemble of clustering techniques The research also explores the modelling of transitions between these operational states, capturing the dynamic nature of industrial processes. Furthermore, the Cluster-based Matrix Profile method is proposed to detect significant operational patterns that indicate potential issues or efficiencies, essential for developing predictive maintenance strategies. Overall, the framework developed in this thesis offers a systematic approach to improve decisionmaking, reliability, and efficiency in managing industrial processes. Although initially applied to gas turbines, this framework holds the potential for broader applications across various industrial systems.

DOCTORAL DEGREE IN CIVIL ENGINEERING

  • GAHIMA, STEPHAN: Data-Driven Patient-Specific Models Supporting Decision Making with Application to Atherosclerotic Plaque Analysis
    Author: GAHIMA, STEPHAN
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN CIVIL ENGINEERING
    Department: Barcelona School of Civil Engineering (ETSECCPB)
    Mode: Normal
    Deposit date: 30/07/2025
    Deposit END date: 10/09/2025
    Thesis director: DIEZ MEJIA, PEDRO | GARCIA GONZALEZ, ALBERTO
    Thesis abstract: This thesis advances finite element-based tools to improve atherosclerosis analysis by addressing current computational limitations. Atherosclerosis, the leading cause of ischaemic heart attacks, imposes a significant social and economic burden (estimated at around $1 trillion worldwide by 2030). In this disease, patients develop plaques from lipid accumulation in the arteries and these plaques can be prone to rupture or stable. Differentiating between these types is essential for effective clinical risk management.Fast, accurate, and robust computational methods can streamline the clinical pipeline and ultimately may help to assess atherosclerotic analysis. We use Finite Element Method (FEM) to simulate stress in atherosclerotic sections, as peak stress plays a key role in assessingrupture risk. Our methods are based on unfitted FEM, which simplify mesh generation, save computational and pre-processing time. Importantly, these methods can work directly on voxelized data such as medical images. The proposed unfitted approaches achieve accuracy within 5 % of that attained by classical fitted approaches and commercial software, for both linear and non-linear cases. Moreover, these methods incorporate a flexible and realistic boundary conditions that account for the influence of surrounding tissues. We also developed an Adaptive Model Reduction (AMR) technique based on a linear hypothesis, serving as a preliminary step toward creating fast surrogate models for near-real-time simulations.Validation experiments demonstrate that AMR decrease computational resource usage by over 70 % while maintaining an accuracy within a 9 % error margin compared to high-fidelity models. Finally, preliminary results indicate that using Topological Data Analysis (TDA) to build interpretable Machine Learning (ML) models can effectively assesses plaque rupture risk. Early experiments yield a classification accuracy of approximately 75 %, a performance comparable to established radiomics approaches.Overall, this doctoral work demonstrates that combining advanced FEMs with interpretable ML may provides nuanced insights into atherosclerotic plaque assessment. Future research should address potential limitations such as data variability and scalability to enable broader implementation of these computational techniques in clinical practice.

DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING

  • COLLAO LAZO, JORGE ALEJANDRO: Application of BIM visual programming algorithms for infrastructure projects
    Author: COLLAO LAZO, JORGE ALEJANDRO
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING
    Department: Department of Civil and Environmental Engineering (DECA)
    Mode: Normal
    Deposit date: 29/07/2025
    Deposit END date: 09/09/2025
    Thesis director: LOZANO GALANT, JOSE ANTONIO | TURMO CODERQUE, JOSE
    Thesis abstract: The BIM digitization has generated a growing automation of traditional AECO project development processes. However, this automation has mainly benefited building projects, which has generated a critical gap in infrastructure projects, such as (1) bridges, (2) roads, and (3) tunnels. This gap is explained by infrastructure projects' lack of standardization compared with building projects. Recently, the BIM industry has incorporated conventional computer programming as a tool capable of partially reducing this lack of automation, developing code-line algorithms. However, both the work interface and the high technical skills required to produce these scripts have been unfriendly to AECO industry professionals. To resolve this gap, BIM software development companies generated an alternative algorithm creation technique called Visual Programming (VP). This technique creates their scripts through visual expressions, represented as process charts instead of the code lines of conventional programming, optimizing the working interface of different human resources from AECO projects. However, the development of VP algorithms for infrastructure projects is still limited.To fill these gaps, the objective of this thesis will be to study the potential automation of infrastructure project processes, developing an integrated platform between VP algorithms and BIM models.At a general level, the platform proposed by this thesis has the following 2 elements: (1) VP algorithms; These custom scripts have the specific function of processing information from external databases and deliver specific data as outputs, and (2) BIM models; These information models will digitally collect the outputs of the VP algorithms and associate them with specific BIM families of the infrastructure projects under study. This thesis focuses on studying transversal problems of Civil Engineering using VP-BIM tools through the development of the following applications:Firstly, this thesis develops a calculation module to estimate (1) greenhouse (GHG) emissions; these pollutants contribute to global warming and climate change by trapping heat in the Earth's atmosphere [1–3] and (2) carbon footprint for vehicle traffic on specific roads in any country with the appropriate traffic and vehicle fleet data. The main novelty of this module is the automated integration of the GHG emission factors recommended by the European Environmental Agency (EEA) TIER 1 level with specific fleet data through a customized VP script. This proposed module was applied on a road in Barcelona (Spain), and the results were compared with a similar study made by the Barcelona transport agency. In addition, specific calculation modules were developed to measure the impact of emission reduction strategies.The second application developed by this thesis aims to use visual programming as an educational tool. A parametric programming workflow is employed to replicate the structural behavior of a beam within a BIM Model. This approach enables students to visualize and analyze structural performance, fostering a deeper connection between classroom theory and hands-on experimentation. This system aims to improve the visualization and understanding of structural data for AEC industry professionals by generating dynamic 3D and 2D models. The educational impact of this tool was assessed through a survey conducted in a structural analysis course at the University of La Serena (Chile), demonstrating its effectiveness in enhancing student comprehension and engagement with modern engineering practices.
  • DEL CARLO, FEDERICA: He.R.A. Heritage at Risk Assessment: Vulnerability Estimation of Masonry Churches for Multi-Layer Single Risk Analysis
    Author: DEL CARLO, FEDERICA
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING
    Department: Department of Civil and Environmental Engineering (DECA)
    Mode: Change of supervisor
    Deposit date: 25/07/2025
    Deposit END date: 05/09/2025
    Thesis director: ROCA FABREGAT, PEDRO | CAPRILI, SILVIA
    Thesis abstract: This research was developed in response to the increasing need for coherent and integrated methodologies to assess the complex and multifaceted risks threatening cultural heritage. Three critical issues were identified at the outset. The inconsistency in terminology and the absence of standardised definitions across risk analysis frameworks, hindering both comparability between studies and the development of actionable strategies. The scarcity of methodologies capable of addressing a large number of heritage architectures, resulting in time and cost-intensive risk assessments that are tailored to specific architectural contexts and difficult to apply elsewhere. The prevailing preference for methodologies that focus on single hazardous events often overlooks the potential for cascading or compound events, despite current risk management guidelines advocating for the integration of multi-event information in planning and preparedness strategies. To address these gaps, the research proposes a quantitative methodology for regional-scale physical vulnerability estimation, specifically targeting seismic and landslide vulnerabilities in historical churches. The adopted methodology is based on a multilayer single-hazard framework, aiming to harmonise and standardise risk analysis procedures. Physical vulnerability is expressed through an index-based approach that combines event intensity indicators with churches physical resistance indicators. While seismic and landslide phenomena are analysed independently, both are evaluated within a shared conceptual approach, which enables direct comparison and prioritisation across hazardous events. Three main phases can be identified. First, the risk context is defined by identifying the objectives, study area characteristics, and hazardous event types. A consistent and scalable data collection process is designed, including the creation of a survey form tailored to masonry churches. The survey form draws on literature-based indicators and existing vulnerability analysis methodologies to support systematic and standardised data gathering for a large building sample. Second, event-specific physical vulnerability indices are developed and validated. The seismic vulnerability index draws on a regression-based model informed by nonlinear static analyses performed on archetypes derived from a typological classification. Generalised Linear Models (GLMs) are employed to relate the probability of failure to churches main geometric attributes. The resulting seismic physical vulnerability index represents the expected tendency of vulnerability over a properly selected range of intensities. The landslide vulnerability index is adapted from an existing method and recalibrated using resistance indicators specifically selected for historical churches. Both indices range into a 0–1 scale and allow for comparability within prioritisation and disaster risk reduction frameworks. Finally, the methodology is extended to estimate scenario-based risk for earthquake-triggered landslides, combining the landslide vulnerability index with susceptibility data and uniform exposure. This scenario-based approach enables the estimation of risk without requiring information on the frequency of the triggering events. The full methodology is applied to a case study in northern Tuscany, a region characterised by high seismicity and widespread slope instability, encompassing 71 surveyed masonry churches. Results reinforce the value of an integrated yet adaptable vulnerability analysis strategy.
  • ELNAGGAR, OMAR AHMED MAHMOUD: Framework for Optimizing the Construction Process in Saudi Arabia: The Integration of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies
    Author: ELNAGGAR, OMAR AHMED MAHMOUD
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING
    Department: Department of Civil and Environmental Engineering (DECA)
    Mode: Normal
    Deposit date: 25/07/2025
    Deposit END date: 05/09/2025
    Thesis director: TURMO CODERQUE, JOSE | ATENCIO CASTILLO, EDISON PATRICIO
    Thesis abstract: The construction industry in Saudi Arabia is experiencing rapid growth driven by the ambitious Vision 2030, but challenges related to inefficiencies, cost overruns, and delays persist. This research presents a comprehensive framework designed to optimize the construction process in Saudi Arabia by integrating Lean Construction, Building Information Modeling (BIM), and Emerging Technologies. Through an extensive review of 64 academic papers, this research identifies key tools and methodologies within Lean, BIM, and Emerging Technologies, that have been successfully applied in construction management. However, it also highlights the absence of a comprehensive framework that effectively integrates these domains in a structured manner.The research was developed through the Design Science Research Method (DSRM) and proposes a novel framework that categorizes these elements across the four phases of construction: Plan, Design, Construct, and Operate. This integration seeks to align functionality, optimize processes, and enhance lifecycle efficiency, while addressing the limitations of current approaches. The framework initially evaluated through expert consensus using the Delphi Method and subsequently tested in a real-world case study of a confidential mega-scale project in Saudi Arabia during the Design phase. Using pre-defined Key Performance Indicators (KPIs) such as cost efficiency, time efficiency, productivity, waste reduction, quality and safety, stakeholder satisfaction, and process automation, the study provides insights into the practical impact of the framework in improving project outcomes.The findings demonstrate the potential of the integrated framework to reduce waste, improve collaboration, and optimize overall project performance. This research contributes to the advancement of construction project management in Saudi Arabia by proposing a transformative approach that integrates Lean, BIM, and Emerging Technologies, offering practical recommendations for stakeholders seeking to enhance their construction processes. This framework aims to guide the Saudi construction industry in overcoming existing challenges and achieving more efficient, cost-effective, and high-quality project outcomes.Expert evaluations and practical applications validate the effectiveness and relevance of the framework, highlighting its potential to revolutionize construction management in Saudi Arabia and offering a robust model for other regions encountering similar challenges.
  • FARRE CHECA, JOSEP: Analysis of Cantilever Construction of Concrete Cable-Stayed Bridges with Time-Dependent Phenomena
    Author: FARRE CHECA, JOSEP
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN CONSTRUCTION ENGINEERING
    Department: Department of Civil and Environmental Engineering (DECA)
    Mode: Normal
    Deposit date: 28/07/2025
    Deposit END date: 08/09/2025
    Thesis director: TURMO CODERQUE, JOSE | LOZANO GALANT, JOSE ANTONIO
    Thesis abstract: This work presents a study on the construction analysis of cable-stayed bridges built by the cantilever method, with a particular focus on the effects of time-dependent phenomena such as creep and shrinkage. In the design of these structures, construction analysis is essential, as the highest stresses and deflections typically occur during the erection process, rather than in service. Therefore, accurately simulating the construction sequence is necessary to ensure structural safety and reliability. However, many existing analysis tools have limitations, such as difficulties in isolating and evaluating individual construction stages, high computational time and storage requirements, and the dependence on global iterative procedures to reach the final target state. These challenges make the construction simulation process both time-consuming and restrictive.To address these limitations, this research proposes and develops efficient computational methods for simulating the construction process of cable-stayed bridges. A first method, referred to as the Direct Algorithm, is presented for use in cases where time-dependent effects are neglected. This algorithm allows for the independent simulation of each construction stage without relying on the superposition principle or on information from previous or subsequent stages.When time-dependent effects such as creep and shrinkage must be considered, the Forward-Direct Algorithm is proposed. This method enables the simulation of the construction process while accounting for long-term concrete behavior, and does so without requiring iterative procedures to adjust the tensioning forces.One of the main contributions of this work is the definition of new criteria for the Objective Service Stage, understood as the target geometry and stress state of the bridge at a specific time. These criteria consider both the cantilever construction process and the influence of time-dependent phenomena. The aim is to establish a tensioning strategy that requires only a single prestressing operation for each stay cable, thereby avoiding costly and complex tuning operations either at the end of construction or during the service life of the structure.The proposed algorithms and criteria are validated through a case study based on the geometry of a real bridge design, analyzed under different construction scenarios. The results confirm the accuracy and reliability of the Direct Algorithm and the Forward-Direct Algorithm in predicting structural behavior during both construction and service. Comparisons with traditional analysis methods from the literature highlight the advantages of the proposed approaches in terms of both precision and computational efficiency. This research offers practical tools and strategies for improving the design and construction of cable-stayed bridges, particularly those built with cast-in-place concrete and erected using the cantilever method, where time-dependent effects play a significant role.

DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS

  • CHAVEZ AGUIRRE, JEAN PIERS NICOLAS: Post-Earthquake Functional Recovery and Resilience of Seismically Isolated Hospitals and Large-Scale Building Portfolios
    Author: CHAVEZ AGUIRRE, JEAN PIERS NICOLAS
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN EARTHQUAKE ENGINEERING AND STRUCTURAL DYNAMICS
    Department: Department of Civil and Environmental Engineering (DECA)
    Mode: Normal
    Deposit date: 25/07/2025
    Deposit END date: 05/09/2025
    Thesis director: LOPEZ ALMANSA, FRANCISCO | MURCIA DELSO, JUAN
    Thesis abstract: This dissertation presents a comprehensive contribution to assessing and optimizing the functional resilience of both individual hospital buildings and urban-scale building portfolios. The research addresses critical challenges in post-earthquake recovery by combining probabilistic methodologies, optimization algorithms, and machine learning techniques to evaluate and enhance the performance of hospital facilities, as well as assess large building portfolios with unprecedented computational efficiency. The first core contribution of this work lies in the development of a probabilistic framework for assessing the post-earthquake functionality of seismically isolated hospital buildings. Recognizing that traditional assessment methods and resilience metrics overlook complex interdependencies among non-structural components, medical equipment, and utilities, this study employs Bayesian Networks (BNs) to model such dependencies explicitly. The proposed framework quantifies the probabilistic damage of over 60 equipment and component types based on ground motion parameters, and propagates their impact through critical hospital departments. The framework allows for the direct computation of functionality indices using damage outputs.Building upon the proposed functionality loss model, the research also introduces a multiobjective optimization framework for hospital recovery planning using the NSGA-II genetic algorithm. The framework addresses the challenge of optimal labor allocation by minimizing both repair time and cost. It enables decision-makers to define the number of workers per repair group based on the importance of each component, which is classified according to its contribution to total functionality loss. The solution space is explored through a high-dimensional search of work effort allocations, and Pareto-optimal repair plans are obtained. The results indicate that excessive labor deployment yields diminishing returns in repair time while significantly increasing cost, with differences in repair time under design basis earthquake and maximum considered earthquake remaining below 13%.To address the scalability of resilience assessments at the urban scale, the dissertation proposes the Urban Cluster Earthquake Resilience (UCER) framework, which leverages unsupervised machine learning algorithms for regional seismic risk and resilience evaluation. Applied to a dataset of 23420 reinforced concrete and masonry buildings in a synthetic urban environment in Italy, the framework employs t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction, HDBSCAN for density-based clustering, and K-Medoids for robust cluster representation. This process reduces the analysis complexity from thousands of buildings to 398 representative clusters. The results and findings of this dissertation highlight the benefits of integrated, data-driven approaches for assessing seismic resilience. By combining probabilistic interdependence modeling, artificial intelligence, and urban clustering, this research bridges the gap between detailed building-level assessments and large-scale regional resilience planning. Ultimately, the findings support the development of more resilient healthcare infrastructures and cities by enabling decision-makers to rely on scientifically grounded tools to prepare, respond, and recover effectively from seismic disasters.

DOCTORAL DEGREE IN ELECTRICAL ENGINEERING

  • BADRI BARBERÁN, JOSÉ ANTONIO: Development of a methodology for optimizing transformers and devices with magnetic core
    Author: BADRI BARBERÁN, JOSÉ ANTONIO
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN ELECTRICAL ENGINEERING
    Department: Department of Electrical Engineering (DEE)
    Mode: Normal
    Deposit date: 25/07/2025
    Deposit END date: 05/09/2025
    Thesis director: RIBA RUIZ, JORDI ROGER | GARCIA ESPINOSA, ANTONIO
    Thesis abstract: This thesis contributes to the field of design and optimization of magnetic core devices, specifically transformers for power electronics. A calculation software has been developed for the design of transformers, based on an analytical-parametric geometric, electromagnetic, thermal model of the main components of a transformer: the core and windings. Experimental studies have been carried out to analyze the magnetic losses in transformer cores, evaluating the contribution of the different constructive parts as well as the impact of voltage harmonics on the magnetic losses of the core. In addition, hysteresis and other loss models have been developed to improve the accuracy of core loss estimation. For the coils, electromagnetic (skin and proximity) and thermal effects have been modelled. Through experimental studies and simulations, the interaction between the temperature and frequency-dependent effects on winding resistance have been analyzed. These studies have demonstrated that winding losses can be, in fact, analytically and accurately calculated. Once a detailed design has been obtained, the core and winding losses are entered into a thermal model to predict the operating temperature of the transformer. After concluding the analytical-parametric model, a real-world industrial applications-oriented optimization system has been implemented. This system allows both single-objective and multi-objective optimization on any desired transformer parameter, always ensuring compliance with the technical and operational needed requirements through constraints. The optimization system has been validated using real cost-optimized prototypes and the results obtained have been compared with experimental tests, demonstrating the accuracy and reliability of the calculation system developed, and allowing its applicability in actual industrial manufacturing processes.

DOCTORAL DEGREE IN ELECTRONIC ENGINEERING

  • FORNT MAS, JORDI: Designing Deep Learning Accelerators in the limits of Energy Efficiency
    Author: FORNT MAS, JORDI
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN ELECTRONIC ENGINEERING
    Department: Department of Electronic Engineering (EEL)
    Mode: Normal
    Deposit date: 25/07/2025
    Deposit END date: 05/09/2025
    Thesis director: MOLL ECHETO, FRANCESC DE BORJA | ALTET SANAHUJES, JOSEP
    Thesis abstract: Deep Neural Network (DNN) models form the backbone of today’s Artificial Intelligence (AI) systems. Their large size and high computational cost have resulted in specialized hardware accelerators being essential for executing these models across many applications. However, the energy efficiency of state-of-the-art accelerator systems falls short of the demands of current AI, especially considering that, while DNN models keep getting larger and more complex, Moore’s Law is coming to a halt. This thesis aims at investigating new ways of optimizing the energy efficiency of AI accelerators by considering and leveraging different degrees of freedom involved in the computation of DNN workloads. Namely, several energy efficiency optimization techniques are explored involving accelerator dataflow, functional circuit approximations, low-bit quantization, mixed-precision, and undervolting; with the goal of pushing the limits of energy-efficient AI acceleration.

DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING

  • YAZDANI CHERATI, DAVOOD: Hydromechanical Simulation of Argillaceous Rocks for Radioactive Waste Disposal Applications
    Author: YAZDANI CHERATI, DAVOOD
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN GEOTECHNICAL ENGINEERING
    Department: Department of Civil and Environmental Engineering (DECA)
    Mode: Normal
    Deposit date: 28/07/2025
    Deposit END date: 08/09/2025
    Thesis director: VAUNAT, JEAN | GENS SOLE, ANTONIO
    Thesis abstract: Argillaceous claystones are primarily composed of clay particles of sedimentary origin andcontain a substantial amount of chemically precipitated cement, often calcium carbonate, whichacts as a bonding agent. Due to their favorable properties—such as low permeability, minimalmolecular diffusion, self-sealing capabilities, and high retention capacity for radionuclides—theyare considered suitable host geomaterials for the deep geological disposal of radioactive waste.However, fractures within these geomaterials, induced by excavations or post-disposal processes,can create preferential pathways for radionuclide migration, potentially influencing theperformance of the disposal system. Therefore, these problems should be numerically evaluated.However, due to their complex behavior, modeling argillaceous rocks presents a significantchallenge. Under shearing, these geomaterials exhibit anisotropy, creep, and quasi-brittle failurecharacterized by significant post-peak softening and strain localization. This study aims toinvestigate the hydromechanical response of Callovo-Oxfordian (COx) argillaceous claystones tolaboratory tests, field excavations, and post-disposal processes by employing the argillite modelsimplemented in the CODE-BRIGHT program. The argillite models are adopted since they caneffectively reproduce the key characteristics of argillaceous materials. Additionally, throughoutthis thesis, several other constitutive models are applied to simulate the behavior of materialsinteracting with the COx, including soft and rigid supports, and swelling materials. The outcomesof this thesis provide significant insight into the hydromechanical behavior of argillaceous rocks,thereby contributing to a more accurate evaluation of the disposal process.

DOCTORAL DEGREE IN MATERIALS SCIENCE AND ENGINEERING

  • GARCÍA DE ALBÉNIZ LÓPEZ DE ABERÁSTURI, NEREA: Engineering zirconia surfaces with cell instructive and antibacterial properties
    Author: GARCÍA DE ALBÉNIZ LÓPEZ DE ABERÁSTURI, NEREA
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN MATERIALS SCIENCE AND ENGINEERING
    Department: Department of Materials Science and Engineering (CEM)
    Mode: Article-based thesis
    Deposit date: 29/07/2025
    Deposit END date: 09/09/2025
    Thesis director: JIMENEZ PIQUÉ, EMILIO | MAS MORUNO, CARLOS
    Thesis abstract: Tetragonal zirconia polycrystals stabilized with 3 mol% yttria (3Y-TZP) has gained growing interest as an alternative to titanium for dental implants, owing to its excellent biocompatibility, high mechanical strength and corrosion resistance and superior aesthetics. Despite these advantages, the clinical performance of zirconia implants still depends on their ability to promote osseointegration while simultaneously minimizing the risk of bacterial colonization, a competitive process known as "race for the surface". Surface properties of dental implants, such as topography, chemistry, and wettability, critically influence the biological response at the tissue-implant interface. In particular, micro- and nanotopographies directly impact cell-material interaction and can modulate several cellular functions including adhesion, migration, proliferation, and differentiation. Similarly, these topographical features affect bacterial response, either promoting bacterial adhesion or, conversely, reducing colonization through antifouling or bactericidal effects. For this reason, surface modifications have become a widely explored strategy to enhance the biological performance of implants. Nevertheless, the major challenge lies in designing surfaces that simultaneously support osseointegration while also preventing bacterial adhesion.This PhD Thesis addressed this challenge by investigating different surface modification approaches to improve the biological performance of zirconia. The aim was to create topographies that simultaneously improve cell behavior while exhibiting antibacterial properties. In concrete, we developed and characterized a series of micro and nanostructured zirconia surfaces and evaluated their biological performance both in terms of human mesenchymal stem cell (hMSC) response and bacterial adhesion of different strains. Prior to experimental work, a comprehensive bibliographic review on topographical modification strategies for 3Y-TZP was conducted (Chapter I), highlighting existing knowledge gaps and guiding the selection of surface treatments. Following this, different surface modification techniques were employed, including hydrofluoric acid (HF) etching for generating nanotopography (Chapter II) and laser patterning via nanosecond (ns-) and femtosecond (fs-) laser to create defined microstructures (Chapter III). These techniques were also combined to evaluate a potential synergistic effect of hierarchically rough micro- and nanotopographies on the biological response (Annex I). Our findings demonstrate both chemical etching and laser patterning techniques successfully enhanced the biological performance of zirconia by improving the hMSCS behavior and reducing bacterial adhesion. However, their combination did not result in a synergistic improvement. Among all the surfaces, the 3 μm linear pattern (L3) created through fs-laser patterning offered the best balance by simultaneously enhancing hMSC adhesion, migration, and osteogenic differentiation, while significantly reducing the adhesion of Staphylococcus aureus and Pseudomonas aeruginosa bacteria. It also led to the most favorable biological outcome under competitive co-culture conditions. Furthermore, biofunctionalization of this topography using a multifunctional peptide containing both antimicrobial and cell-adhesive sequences showed promising synergistic biological effects (Annex II). Importantly, these improvements were achieved without compromising the mechanical integrity of the L3-patterned surface (Annex III). In conclusion, this PhD Thesis demonstrated that topographical modification of zirconia offers promising strategies for developing zirconia implant with improved biological performance, enhancing both osseointegration and antibacterial properties. Future directions should focus on integrating biochemical cues, in vivo validations, and complete assessment of the mechanical integrity.

DOCTORAL DEGREE IN PHOTONICS

  • KARANIKOLAOU, TERESA DIMITRA: Heating and decoherence due to light scattering in atomic media
    Author: KARANIKOLAOU, TERESA DIMITRA
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN PHOTONICS
    Department: Institute of Photonic Sciences (ICFO)
    Mode: Normal
    Deposit date: 25/07/2025
    Deposit END date: 05/09/2025
    Thesis director: CHANG, DARRICK
    Thesis abstract: Cold atom platforms have become central to quantum technologies such as information processing, simulation, and metrology. Their versatility and high degree of control make them especially powerful. A key breakthrough in the field was the ability to trap individual atoms using light. Far-off-resonant optical dipole traps—like optical tweezers and lattices—enable precise positioning of atoms in diverse geometries, from simple 2D arrays to complex 3D structures. Another major advantage of cold atoms is their ability to mediate interactions between photons, which do not naturally interact in free space. Cold atomic ensembles act as a nonlinear medium, enabling strong interactions even at the two-photon level. Using collective atom-photon coupling and Rydberg-state excitations, they allow for photon-photon gates and the creation of non-classical states of light. These two features—strong optical nonlinearities and precise atomic positioning—make cold atoms a leading platform for quantum networks, quantum simulations, and studies of light-matter interaction.A phenomenon common to both platforms is photon scattering, which can either be of an intended or unintended nature. Up until recently, simple theories of scattering were sufficient for the community. However, the advance of atomic platforms now requires more nuanced and sophisticated theories to understand scattering and their consequences on applications. This constitutes the main theme of the thesis. In the application of atom trapping, in many practical situations atoms may experience state-dependent potentials. The potential mismatch can lead to excess heating and reduced elastic scattering of light, as compared to well-known limits like an atom in “magic-wavelength” traps or a trapped ion. In the first part of the thesis, we develop a model to analyze these effects, which can have important consequences in quantum optics or in atom imaging.In the second part of the thesis, we investigate how Rydberg spin waves decohere in the presence of light scattering, within the context of Rydberg Electromagnetically Induced Transparency (EIT). Within Rydberg EIT, an initial photon is stored as a coherent, extended superposition across atoms. This initial photon can strongly modify the propagation of subsequent photons, leading to large nonlinearities, but the scattering of subsequent photons can reveal information about where the first photon was stored, leading to decoherence of the initial superposition state. This in turn can lead to decreased utility or ability to retrieve the first photon. Here, we elucidate the nature of decoherence, and in particular for the first time we take fully into account the three-dimensional nature of the ensemble and its multiple scattering of light. We find regimes in which multiple scattering might offer additional protection from decoherence, as compared to previous simplified theories. Overall, this thesis makes new advances in understanding the nature of microscopic atom-light interactions and scattering, and connects this fundamental physics to key consequences in real-life applications.

DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS

  • FELGUEIRAS LUIS, DIANA ZAIDA: Magnetohydrodynamics enhanced radio blackout mitigation system for spacecraft during planetary entries
    Author: FELGUEIRAS LUIS, DIANA ZAIDA
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
    Department: Department of Signal Theory and Communications (TSC)
    Mode: Normal
    Deposit date: 28/07/2025
    Deposit END date: 08/09/2025
    Thesis director: CAMPS CARMONA, ADRIANO JOSE | CHAZOT, OLIVIER
    Thesis abstract: Spacecraft entering planetary atmospheres are enveloped by a plasma layer with high levels of ionization, caused by the extreme temperatures in the shock layer. The charged particles in the plasma interact with electromagnetic waves emitted by onboard antennas, resulting in communication and tracking difficulties during flight and causing radio communication blackouts that can last several minutes. The magnetic field alleviation technique is a proposed solution to reduce the plasma layer effects on radio signal propagation. This technique involves superimposing a magnetic field onto the plasma flow, transforming it into an anisotropic medium and altering its refractive index. This enables the creation of an extraordinary wave capable of propagating at plasma frequencies higher than the radio signal frequency.The Horizon 2020 Magnetohydrodynamic Enhanced Entry System for Space Transportation (MEESST) project aims to design and test a high-temperature superconducting (HTS) magnet to mitigate radio communication blackout and reduce surface heat flux. This research focuses on the experimental study of the radio communication blackout phenomenon and its mitigation using an applied magnetic field, in the VKI plasma wind tunnel. A tailored communication system, designed to accommodate the ionization levels encountered in ground testing, is developed and fully characterized in the UPC anechoic chamber.The results are divided into three main parts, each addressing a specific research question. The first part is dedicated to studying signal propagation in a plasma flow. Both air and CO2 plasma flows are characterized using emission spectroscopy. Radial temperature profiles are derived from the intensity of an oxygen atomic line, while electron number density profiles are determined from the Stark broadening of the Hb line. Radio signal magnitude profiles are measured in various configurations (side-to-side and stagnation) across a frequency range of 33 GHz to 40 GHz, under a wide range of testing conditions. Attenuation and Faraday rotation caused by the plasma are estimated, and the results show a strong correlation with the measured plasma frequencies.The second part examines the effects of an applied magnetic field on the plasma flow and radio signal propagation. The probe houses the HTS magnet, which can generate a maximum magnetic flux density of approximately 0.45 T at the front of the antenna. Characterization of the magnetized air plasma flow reveals an increase in radiance, stagnation heat flux, and plasma temperature and frequency, attributed to the applied magnetic field. The higher electron density near the antenna and the increased attenuation of the extraordinary wave result in negligible differences in signal attenuation profiles. However, changes are observed in the reflection coefficient depending on the magnetic field strength, and in the Faraday rotation depending on the field direction.The third part explores methods to duplicate flight conditions in ground facilities, concerning aerothermochemistry and radio communication blackout. A relevant condition related to radio blackout during the ARD reentry flight is selected, at an altitude of 60.6~km. The shock layer around an ARD model is replicated in the JAXA HIEST shock tunnel. The condition is numerically simulated for both flight and ground scenarios, highlighting the limitations of the binary scaling methodology. For the same flight condition, the local heat transfer simulation methodology is applied and tested in the Plasmatron facility, successfully replicating the same flow conditions. Finally, the electron number density profiles from both facilities are compared with those from flight.
  • RAJA KUMAR, DHEERAJ: AI-Driven Multi-Antenna Designs for Next Generations of Wireless Communication Systems
    Author: RAJA KUMAR, DHEERAJ
    Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis)
    Programme: DOCTORAL DEGREE IN SIGNAL THEORY AND COMMUNICATIONS
    Department: Department of Signal Theory and Communications (TSC)
    Mode: Normal
    Deposit date: 25/07/2025
    Deposit END date: 05/09/2025
    Thesis director: ANTON HARO, CARLES | MESTRE PONS, FRANCESC XAVIER
    Thesis abstract: As wireless networks evolve toward sixth-generation (6G) systems, the integration of advanced technologies like massive MIMO, millimeter-wave communication, intelligent reflecting surfaces, and massive multiple access schemes has led to significant increases in system complexity. Traditional signal processing and optimization techniques are grounded in explicit modeling and mathematical tractability. They increasingly fall short in addressing the high-dimensional, dynamic, and often non-linear behaviors of these systems. To bridge this gap, Artificial Intelligence and Machine Learning (AI/ML) methods have emerged as powerful alternatives, capable of learning directly from data and adapting to complex and uncertain environments.Rather than relying solely on conventional models or pure data-driven solutions, this dissertation advocates for hybrid approaches that combine model-based structures with learning-based flexibility. Hybrid methods mitigate the high data demands and limited interpretability of black-box models while overcoming the rigidity and assumptions of traditional schemes. This balance makes them particularly well-suited for the complex, imperfectly modeled conditions encountered in wireless systems.This PhD dissertation focuses on applying such hybrid methods to four key tasks in the wireless signal processing chain: channel estimation, symbol detection, digital precoding, and hybrid beamforming. Though each task poses unique technical challenges, they collectively contribute to the overarching goal of enabling robust, efficient, and scalable communication in next-generation systems.The first objective addresses channel estimation, a foundational task in massive MIMO systems where performance hinges on accurate knowledge of the channel state. In highly dynamic or non-linear environments, traditional estimators like LS or MMSE are often inaccurate. This work proposes low-complexity neural network architectures, specifically 1D-CNNs and Graph Neural Networks (GNNs), that serve as hybrid estimators by embedding structural insights while learning to compensate for model mismatches, showing strong performance across varying MIMO configurations.The second objective explores symbol detection in Rate-Splitting Multiple Access (RSMA), where receivers must decode a common message followed by private streams. The conventional RSMA receivers use successive interference cancellation (SIC) or joint detection schemes, that are capable of detecting all streams simultaneously. These are theoretically superior, but computationally infeasible in large-scale systems. To balance performance and complexity, this work introduces deep receiver architectures such as RS-Net+, which combine the interpretability of SIC with the adaptability of neural networks. These hybrid designs exhibit robustness to channel impairments and outperform both purely model-based and black-box neural schemes under linear and non-linear channel conditions.The third and fourth objectives focus on designing precoding techniques for RSMA, both in fully digital and hybrid analog-digital beamforming settings. Standard approaches like WMMSE, though effective, are iterative and computationally intensive. This thesis proposes self-supervised learning frameworks, based on GNN and MLP architectures, that learn precoding policies directly from channel data while respecting underlying model structures. These methods are then extended to hybrid beamforming scenarios, where learned digital precoders work in tandem with fixed analog beamformers to provide scalable and energy-efficient solutions.In summary, this dissertation advances the design of hybrid AI/ML solutions for core signal processing tasks in wireless communication systems. By combining domain knowledge with learning, it delivers architectures that are robust, interpretable, and well-suited to the challenges of future 6G networks.

Last update: 28/08/2025 04:30:21.