https://hdl.handle.net/20.500.11984/1148 2026-09-08T15:27:21Z https://hdl.handle.net/20.500.11984/14658 Effect of Roll Forming Process Imperfections on the Buckling Capacity of Racked Beams Alberdi Orbegozo, Beñat; Oyanguren, Aitor; Ulacia, Ibai; Larrañaga Amilibia, Jon Ensuring the stability and safety of steel storage racks is essential to withstand applied loads over time. Rack columns are typically produced through a cold roll-forming, enabling high productivity and open-section profiles, known as uprights. However, roll-forming imperfections can adversely affect buckling capacity. Stub column compression tests, as defined by the EN 15512 standard, experimentally determine the buckling capacity, while numerical methods further analyze it. A common way to introduce geometric imperfections in FEM models is by superposing scaled eigenmodes obtained from an elastic buckling analysis. Although standards specify imperfection types (local, distortional, global) and magnitudes, combining them remains unclear, often requiring multiple scenarios that may overly penalize capacity, as some imperfections rarely occur simultaneously. This work determines imperfection values from predefined roll-forming imperfections and identifies which combination most significantly affects open-section column buckling capacity. The initial roll-forming imperfections were measured and each amplitude established. A FEM model incorporating these imperfections was developed and validated against experimental data. Finally, the effects of these errors and their most critical combination on buckling capacity were determined, contributing to improved design procedures and a more reliable assessment of structural performance. 2026-01-01T00:00:00Z https://hdl.handle.net/20.500.11984/14657 Using Runtime Information of Controllers for Safe Adaptation at Runtime: A Process Mining Approach da Silva, Jorge; Illarramendi, Miren; Iriarte, Asier The increasing complexity of current Software Systems is generating the urge to find new ways to check the correct functioning of models during runtime. Runtime verification helps ensure that a system is working as expected even after being deployed, essential when dealing with systems working in critical or autonomous scenarios. This paper presents an improvement to an existing tool, named CRESCO, linking it with another tool to enable performing periodical verification based on event logs. These logs help determine whether the functioning of the system is inadequate or not after the last periodic check. If the system is determined to be working incorrectly, new code files are automatically generated from the traces of the log file, so they can be replaced when a faulty scenario is to occur. Thanks to this improvement, the CRESCO components are able to evaluate their correctness and adapt themselves at runtime, making the system more robust against unforeseen faulty scenarios. 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.11984/14656 On the temporal sensitivity of the reference data in bias correction techniques for improving metocean datasets CALLEA, FRANCESCO; Martinez Perurena, Ander; Zarketa-Astigarraga, Ander; Penalba, Markel; Cervelli, G.; Giorgi, G.; Robertson, B.; Iglesias, G. The paper presents a preliminary study limited to the use of wave height for the data corresponding to the Gulf of Biscay, a location with a very dominant North-West wave rose. It benchmarks different bias correction (BC) techniques and evaluates their performance, as well as the sensitivity of the BC to the reference dataset employed for the identification of the BC parameters. More precisely, the amount of data (number of years) and the selected period (exact years) are analysed. Overall, the results demonstrate that the Gumbel-based BC techniques overperform the linearly-spaced BC techniques, the directional-adjusted Gumbel Quantile Mapping technique showing the lowest bias. With respect to the sensitivity of the reference data, BC seems to provide satisfactory results even when only 1 year of data are used. However, the dispersion among the different selected periods is large, resulting in large uncertainties. This dispersion reduces significantly once 3 or more years of data are used, independently of the selected period. 2024-01-01T00:00:00Z https://hdl.handle.net/20.500.11984/14653 Power Transformer Modelling for the Evaluation of a Reinforcement Learning-Based Inrush Current Minimization Strategy Ugarte Valdivielso, Jone; Barrenetxea, Manex; Torres, Asier; Stewart, Brian G.; Aizpurua Unanue, Jose Ignacio Inrush currents in power transformers can affect the transformer lifetime and the reliability of electrical grids. Various inrush current minimization techniques have been proposed to mitigate these effects. To validate any minimization strategy, an accurate transformer model is required. This study focuses on developing a transformer model for inrush current minimization. The transformer is modelled through duality-based transformation, recognized for its accurate core representation. In addition, the laboratory environment and its constituent components are also modelled. The developed model is validated against real inrush current measurements. Finally, the model is employed to test a Reinforcement Learning (RL) based inrush current minimization strategy. The performance of the proposed method is validated by contrasting its outcomes with inrush current data obtained from traditional uncontrolled Circuit Breaker (CB) operations. The findings indicate that the proposed inrush current minimization approach reduces the peak inrush current by 77% compared to traditional uncontrolled CB switching. 2025-01-01T00:00:00Z