Doctoral School

Reading date: 01/10/2025

• 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
  Reading date: 01/10/2025
  Reading time: 12:00
  Reading place: ETSECCPB. UPC, Campus Nord Sala Actes del Vèrtex Plaça d'Eusebi Güell, 608034 Barcelona
  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.
  

• 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
  Reading date: 01/10/2025
  Reading time: 16:00
  Reading place: Street Jordi Girona 1-3. C1 building, Room C1-007
  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.
  

• DELGADO GUERRERO, JUAN ANTONIO: Learning latent structures for robotic assistance in daily manipulation tasks
  
  Author: DELGADO GUERRERO, JUAN 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 AUTOMATIC CONTROL, ROBOTICS AND VISION
  Department: Institute of Robotics and Industrial Informatics (IRI)
  Mode: Normal
  Deposit date: 02/09/2025
  Reading date: pending
  Reading time: pending
  Reading place: pending
  Thesis director: TORRAS GENIS, CARMEN | COLOMÉ FIGUERAS, ADRIÀ
  Thesis abstract: Robotic domestic assistance presents significant challenges due to the complexity of modeling everyday manipulation tasks, especially those involving deformable objects like cloth. Traditional approaches often struggle with high-dimensional state representations, dynamic uncertainties, and the need for safe human-robot interaction. This thesis addresses these challenges by developing novel machine learning methods based on latent variable models to enable efficient, adaptive, and safe robotic manipulation.First, we propose a Gaussian Process Latent Variable Model (GPLVM) framework combined with Bayesian Optimization (BO) to learn high-dimensional robot motion policies with minimal data. This approach reduces the parameter space dimensionality while preserving task-relevant features, achieving faster convergence than other existing model-free alternatives.Next, we extend this framework to contextual learning using Covariate GPLVM (c-GPLVM), allowing robots to adapt to environmental changes (e.g., user preferences, object positions) without retraining. Experiments in feeding and shoe-fitting tasks demonstrate improved generalization with fewer samples compared to state-of-the-art contextual policy search methods.For dynamic cloth manipulation, we introduce the Controlled Gaussian Process Dynamical Model (CGPDM), which embeds control actions into a low-dimensional latent space to predict cloth motion under robot manipulation. Evaluations in simulated and real-world bimanual cloth handling show that CGPDM accurately generalizes to unseen actions, even with limited training data.Finally, we address safety in human-robot interaction by proposing Cartesian control enhancements for redundant manipulators, including error saturation, singularity avoidance, and impedance tuning. These measures mitigate risks during physical interaction, ensuring stable and compliant robot behavior.Together, these contributions advance robotic cloth manipulation by combining data-efficient learning, context-aware adaptation, and safe control, paving the way for practical deployment in assistive and household robotics.
  

• ZIFAN, MOHSEN: Complementing urban gray with blue-green infrastructure for water management in Tehran
  
  Author: ZIFAN, MOHSEN
  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: 18/06/2025
  Reading date: 01/10/2025
  Reading time: 10:30
  Reading place: ETSAB (Esc. Técnica Sup. Arquitectura de Bcn)(Enlace a videoconferencia: https://meet.google.com/hku-uezm-rdo) Inicio conexión a las 10:00, hora BCN
  Thesis director: BOSCH GONZÁLEZ, MONTSERRAT | MARTI CASANOVAS, MIQUEL
  Thesis abstract: Water has always played a crucial role in urban development, especially in areas with abundant supply. It enhances urban spaces, creating mem- orable environments for citizens. However, in Iran’s climate, the significance of water has often been overlooked in modern urban planning. Historically, water shaped the structure and meaning of Iranian cities, but today its importance is neither recognized in theory nor in practice. Rapid urbanization has led to a loss of water’s role in urban infrastructure. It is essential to reevaluate water’s role in contemporary Iranian cities, both physically and conceptually, using historical lessons as valuable insights. This research examines integrating blue-green infrastructure into Tehran’s urban gray infrastructure to tackle water management challenges such as scarcity, pollution, and inefficient resource management exacerbated by rapid urbanization. It emphasizes the historical significance of traditional systems like Qanats, proposing their revival alongside alternative sources like rainwater harvesting to boost urban resilience and sustainability. It analyzes existing surface water management and the furrow network, identifying systemic inefficiencies and their impacts on hydrological dynamics. By analyzing existing frameworks and identifying inefficiencies and employing expert questionnaires, the study provides evidence-based recommendations for urban planners and policymakers. By advocating for the integration of blue-green infrastructure with current urban systems, this research aims to improve water management practices in Tehran, fostering a balanced approach that enhances environmental sustainability and the overall quality of life for its residents. This comprehensive framework aims to create a resilient urban future, blending historical wisdom with modern solutions.
  

Reading date: 02/10/2025

• FAVATA, ALESSANDRA: Clinical application of a wearable system for assessing upper limb motor function in children with Duchenne muscular dystrophy and spinal muscular atrophy
  
  Author: FAVATA, ALESSANDRA
  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 MECHANICAL, FLUIDS AND AEROSPACE ENGINEERING
  Department: Department of Mechanical Engineering (EM)
  Mode: Normal
  Deposit date: 18/07/2025
  Reading date: 02/10/2025
  Reading time: 11:00
  Reading place: Aula Capella de l’ETSEIB.
  Thesis director: FONT LLAGUNES, JOSEP MARIA | PÀMIES VILÀ, ROSA
  Thesis abstract: Duchenne Muscular Dystrophy (DMD) and Spinal Muscular Atrophy (SMA) are rare neuromuscular diseases characterized by progressive muscle weakness, leading to loss of ambulation, respiratory and cardiological complications, and ultimately to a premature death. Recent therapeutic advancements have introduced new possibilities for managing these disorders, focusing on preserving upper limb function to maintain patient's autonomy.Nevertheless, the clinical assessment tools rely on observational scales, which lack the sensitivity and objectivity needed to accurately track disease progression and therapeutic outcomes. In this context, the Inertial Measurement Units (IMUs), electronic devices able to quantitatively track the orientation of the body segment where they are attached, represent a valid alternative.For this reason, the main objective of this thesis is to validate the use of an IMU-based system to objectively quantify the upper limb motor function in children with neuromuscular diseases (NMDs). First, a systematic review was conducted to analyze the technical and clinical aspects of using an IMU-based system for clinical application, highlighting key challenges such as sensor-to-segment calibration, not always feasible for patients with motor impairment, and the lack of standardized biomechanical protocols. Additionally, an analysis of the biomechanical metrics generally implemented was performed, emphasizing the most meaningful to evaluate the motor status in people with motor diseases. Using an IMU-based system, it is possible to obtain well-known metrics like spatiotemporal or angular metrics. All of them have been proven to be useful for evaluating upper limb motor function in people with motor impairment. However, there is a lack of agreement regarding the clinical protocol and the biomechanical metrics that can help to better understand the progression of the diseases.To address these open challenges, an IMU-based system was developed, the ArmTracker, equipped with seven sensors to evaluate the kinematics of the upper limb in children with DMD and SMA. The first issue addressed was the sensor-to-segment calibration. A picture-based sensor-to-segment calibration was developed, allowing accurate kinematic analysis even in patients with motor impairment, such as children with NMDs. This method was validated by comparing IMU-derived motion data against the gold-standard in the motion analysis field. Furthermore, the kinematic data obtained with this method were compared against standard calibration methods, commonly implemented in clinical practice. Results, obtained in a group of healthy subjects, shown that the picture-based calibration technique led to accurate kinematic data while being more practical and feasible for clinical applications.The system was further tested in a clinical setting on a group of children with DMD and SMA. Kinematic metrics such as workspace area, curve efficiency, and range of motion were computed and compared with the results of the standard clinical scales. A strong correlation with clinical scores was found, confirming their validity for assessing upper limb motor function. Moreover, the system identified discrepancies with respect to the clinical score, suggesting that IMU-based metrics might provide additional insights beyond traditional scales. These metrics were further analyzed one year after the first assessment to assess their sensitivity in longitudinal assessment. The findings suggest that kinematic metrics, obtained with an IMU-based system, are useful not only to objectively assess upper limb motor function in children with NMDs, but may also support clinicians in conducting longitudinal evaluations.We hope that the work of this dissertation serves as a relevant guideline for future therapists and engineers in the field to enhance the understanding and the evolution of neuromuscular diseases.