http://hdl.handle.net/10366/4360 Tue, 15 Sep 2026 23:00:48 GMT 2026-09-15T23:00:48Z http://hdl.handle.net/10366/172769 Identifying favourable zones for geothermal exploration at the regional scale remains challenging, particularly in areas where conventional geophysical surveys are spatially limited or economically unfeasible. This study presents an integrated framework for delineating geothermal favourability across the Iberian Peninsula using global gravity and magnetic products combined with subsurface thermal information. EGM2008, WGM2012, EMAG2, and WDMAM2 were compared and harmonized through geostatistical modelling, anisotropic ordinary kriging, and common-grid processing. Potential-field transformations and spectral coherence analysis were used to derive a Geophysical Favourability Index (FI_geof). This index was integrated with temperature at 100 m depth using weighted fuzzy logic, applying FuzzyLinear and FuzzyLarge membership functions with a 60% FI_geof and 40% temperature weighting, to obtain the Geothermal Favourability Index (FI_geot). Quantitative comparison and cross-validation indicated that EGM2008 and EMAG2 were the most suitable primary reference products within their respective datasets, whereas WGM2012 and WDMAM2 provided complementary regional-scale information. The fuzzy integration identified the highest favourability mainly in Galicia and the Levante–Betic sector, where elevated FI_geot values coincide with heat-flow values of approximately 96–154 mW m−2 and comparatively high geothermal gradients. Around 20% of the study area was classified within the highest favourability category. The resulting FI_geot should be interpreted as a regional screening and prioritization tool rather than as direct evidence of an exploitable geothermal resource. Overall, the proposed methodology provides a reproducible approach for identifying priority areas for further geothermal investigation in large and incompletely characterized regions. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/172769 2026-01-01T00:00:00Z http://hdl.handle.net/10366/172767 [EN] Extreme weather events increasingly affect the safety, durability, and operational performance of road infrastructure, creating the need for sustainable thermal management solutions. Among the available technologies, low-enthalpy geothermal systems offer significant advantages by providing continuous heating and cooling capabilities with reduced environmental impact compared to conventional maintenance practices. This study presents the methodology developed within the GEO-ROAD project to assess shallow geothermal resources across Spain and support the future deployment of geothermal road systems. The proposed framework integrates geological, thermal, and satellite-derived geophysical information through a unified GIS-based workflow, combining multivariate statistical analysis, map algebra, and automated geospatial processing to generate a regional geothermal potential model. In addition to conventional geological characterization, the methodology incorporates magnetic and gravity data from satellite missions, airborne surveys, and ground-based observations to improve the spatial representation of subsurface conditions. The resulting geothermal potential assessment constitutes a key component of the GEO-ROAD digital platform, where it will be combined with climatic risk maps and road infrastructure information to identify the most suitable locations for geothermal applications. By linking geothermal resource assessment with infrastructure-oriented decision-making, the proposed methodology provides a scalable and transferable framework for supporting the planning of sustainable and climate-resilient road thermal management systems. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/172767 2026-01-01T00:00:00Z http://hdl.handle.net/10366/172766 [EN] The depopulation of rural areas across Southern Europe has accelerated the deterioration and disappearance of cultural heritage assets, many of which remain undocumented, undervalued, or at risk. This article presents a hybrid methodology that combines AI techniques with expert knowledge to improve the identification, classification, and validation of cultural heritage in rural territories. The proposed system processes unstructured textual data to extract and categorize information on tangible and intangible heritage elements. A pilot case study was carried out in the Valle de Amblés and Sierra de Ávila (province of Ávila, Spain), a region characterized by severe demographic decline but high heritage potential. Four municipalities (Amavida, Cardeñosa, La Torre, and Solosancho) were selected for testing based on heritage diversity, available documentation, and accessibility. In each case, AI-generated outputs were compared with manually curated validation files to assess the detection performance and applicability of the system. Results show that while traditional fieldwork and historical interpretation remain essential, AI can significantly enhance the speed and scalability of heritage documentation, particularly in under-resourced areas. The methodology reinforces the value of interdisciplinary collaboration and supports the development of territorial heritage strategies for sustainable rural regeneration. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/172766 2026-01-01T00:00:00Z http://hdl.handle.net/10366/172641 [EN] Road infrastructures are fundamental for ensuring connectivity, safety, and the efficient functioning of transportation networks. However, extreme weather conditions, particularly low temperatures and ice formation, pose significant risks to user safety and road conditions. This research focuses on the development of a geospatial model designed to identify road sections exposed to these harsh weather conditions. The model integrates different sources of information, including meteorological and satellite data, topographic information, and road maintenance plans, through a consistent methodology for data processing and analysis. A combination of geospatial analysis and advanced processing techniques was employed to identify and map regions most vulnerable to the formation of ice. The results show good spatial agreement between the areas identified by the model and the available local information, supporting its ability to characterize areas with greater susceptibility to winter-related hazards. This work highlights the potential of the developed geospatial model to support the planning of preventive and maintenance measures on roads. By providing spatial information on susceptibility to low temperatures and ice formation, the model can serve as a decision-support tool for road management, contributing to the planning of targeted interventions and improved road safety. Overall, this study underscores the importance of integrating advanced geospatial techniques into infrastructure management to improve response strategies in the face of extreme weather events. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/172641 2026-01-01T00:00:00Z http://hdl.handle.net/10366/172148 ‎[EN] Geothermal systems offer an effective solution for enhancing the resilience of road infrastructure to extreme weather events. Their key advantages (such as independence from external climatic conditions and the potential for continuous operation) make them significant improvement over conventional approaches, including de-icing or protecting road surfaces from high temperatures. Within this context, the GEO-ROAD project aims to develop a predictive platform to identify the most promising locations for the deployment of geothermal energy in road infrastructure. A central component of this research is the methodology used to characterize the subsurface energy resource across the study area (Spain) and to develop corresponding geothermal potential models. Following an initial geological and thermal assessment, a refined model is presented that integrates geophysical data from satellite missions, airborne surveys, and field measurements. Finally, this work highlights the importance of high-precision geothermal potential models, which provide a robust foundation for optimizing the use of geothermal energy, particularly in the road sector considered. Mon, 01 Jun 2026 00:00:00 GMT http://hdl.handle.net/10366/172148 2026-06-01T00:00:00Z http://hdl.handle.net/10366/171966 [ES] La creciente integración de los UAV en arquitectura e ingeniería exige nuevas metodologías formativas adaptadas a contextos profesionales reales. Sin embargo, la enseñanza universitaria continúa limitada por restricciones normativas y condicionantes logísticos que reducen la práctica real de vuelo. Este trabajo presenta una metodología educativa inmersiva basada en simuladores profesionales combinados con controladores físicos, que permite desarrollar competencias técnicas, operativas y de toma de decisiones en un entorno seguro y repetible. La propuesta integra aprendizaje activo, misiones basadas en retos y evaluación por competencias, estructurándose en fases progresivas: selección de herramientas de simulación, aplicación de rúbricas iniciales y finales, análisis de resultados, transición a prácticas reales y mejora continua. Desde la perspectiva arquitectónica y de la edificación, la metodología facilita la formación en inspección de edificios, planificación de vuelos y captura de datos mediante fotogrametría y análisis térmico, ofreciendo un modelo escalable y transferible para la innovación educativa en educación superior. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/171966 2026-01-01T00:00:00Z http://hdl.handle.net/10366/171965 [ES] Oppida y Verracos: investigación y divulgación del patrimonio de la Edad del Hierro en el occidente de la meseta Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/171965 2026-01-01T00:00:00Z http://hdl.handle.net/10366/171964 [ES] Propuesta metodológica a través de las tecnologías de la información y la comunicación para la revitalización de zonas rurales con problemas demográficos: el patrimonio como vector estratégico de desarrollo. Thu, 01 Jan 2026 00:00:00 GMT http://hdl.handle.net/10366/171964 2026-01-01T00:00:00Z http://hdl.handle.net/10366/170882 [EN] Roof modelling provides useful information for energy analysis, but the methodologies traditionally applied are based on data acquired through aerial vehicles. This requirement makes necessary two data acquisition campaigns: one from indoors and another from outdoors. However, most energy studies can be performed using regularized and simplified models where most of the information of the exhaustive acquisitions is not used. Therefore, this paper proposes a semi-automatic procedure for the 3D modelling of roofs using indoor point clouds, reducing the acquisition campaigns to the indoors campaign. The methodology is based on the hypothesis that surfaces have no thickness, which makes the algorithm especially useful in industrial environments where there are no false ceilings and therefore, the contribution of the roof in the energy behaviour of the building is more important. The methodology is tested on six different scenarios, obtaining their regularized models with relative errors lower than 2% in ideal conditions. Fri, 01 Apr 2022 00:00:00 GMT http://hdl.handle.net/10366/170882 2022-04-01T00:00:00Z http://hdl.handle.net/10366/170881 [EN] This study investigates the application of soft magnetic composite (SMC) materials in alternator core manufacturing for bladeless wind turbines operating under the principle of vortex-induced vibration (VIV), employing additive manufacturing (AM) technologies. Through a comparative analysis of alternator prototypes featuring air, SMC, and iron cores, the investigation aims to evaluate the performance of SMC materials as an alternative to the most commonly used material (iron) in VIV BWT, by assessing damping, resonance frequency, magnetic hysteresis, and energy generation. Results indicate that while alternators with iron cores exhibit superior energy generation (peaking at 3830 mV and an RMS voltage of 1019 mV), those with SMC cores offer a promising compromise with a peak voltage of 1150 mV and RMS voltage of 316 mV, mitigating eddy current losses attributed to magnetic hysteresis. Notably, SMC cores achieve a damping rate of 60%, compared to 67% for air cores and 59% for iron cores, showcasing their potential to enhance the efficiency and sustainability of bladeless wind turbines (BWTs). Furthermore, the adaptability of AM in optimizing designs and accommodating intricate shapes presents significant advantages for future advancements. This study underscores the pivotal role of innovative materials and manufacturing processes in driving progress towards more efficient and sustainable renewable energy solutions. Tue, 25 Jun 2024 00:00:00 GMT http://hdl.handle.net/10366/170881 2024-06-25T00:00:00Z http://hdl.handle.net/10366/170869 [EN] This study presents a comprehensive exploration centred on the morphology and surface structure of bladeless wind turbines (BWTs) aimed at optimizing their wind energy harvesting capability. Unlike conventional wind technology where vortex-induced vibration (VIV) is seen as problematic due to aeroelastic resonance, this effect becomes advantageous in BWT energy harvesters, devoid of frictional contact or gears. The primary objective of this study is to develop an optimal BWT design for maximizing energy output. Specifically, this study delves into optimizing the energy performance of these VIV wind energy harvesters, investigating how the geometry (shape and roughness) influences their operating range, known as Lock-In range. The results demonstrate how variations in geometry (convergent, straight, or divergent) can shift the Lock-In range to different Reynolds numbers (Re), modelled by the equation: Re (max Lock-In) = 0.30 α + 4.06. Furthermore, this study highlights the minimal impact of roughness within the considered test conditions. Wed, 27 Mar 2024 00:00:00 GMT http://hdl.handle.net/10366/170869 2024-03-27T00:00:00Z http://hdl.handle.net/10366/170868 [EN] InfraRed Thermography (IRT) has proven to be a valuable diagnostic tool due to its real-time, remote, and non-destructive operation yielding accurate detection of the position of defect areas in building façade ceramic tiling. Ceramic tiles coating building façades are widely used throughout the world because of their technical and aesthetic characteristics. However, the detachment of ceramic tiles and the water infiltration in deep layers are still common problems. So, this paper proposes active infrared thermography as a thermographic acquisition mode, in contrast to the common use of passive thermography, and segmentation of defect areas and automation in the thermal image processing as added values never before proposed in the ceramic tiling thermographic inspection. For that, specimens of ceramic tiling for building façades were tested under different laboratory conditions, with inserted corks (simulating detachments), and by injecting water into holes drilled in the back surfaces (simulating water infiltration), as defects. Good results have been obtained in all the tests, both in dry and wet conditions in the specimens and for surfaces with homogeneous and heterogeneous surface properties, serving the introduction of this workflow for a first and fast inspection in ceramic tiling building façades. Future research will work with the fine-tuning phase of the methodology by applying it to real case studies. Tue, 01 Mar 2022 00:00:00 GMT http://hdl.handle.net/10366/170868 2022-03-01T00:00:00Z http://hdl.handle.net/10366/170867 [EN] Infrastructure inspection is fundamental to keep its service performance at the highest level. For that, special attention should be paid to the most severe defects in order to be able to subsequently mitigate or even eliminate them. Therefore, this paper introduces the combination of an automatic thermogram pre-processing algorithm and a Deep Learning (DL) model, Mask R-CNN, applied to thermal images acquired from different infrastructures (buildings, heritage sites and civil infrastructures) with water-related problems and thermal bridges. The pre-processing algorithm developed is based on thermal fundamentals. As an output, the thermal contrast between defect and defect-free areas is increased in each image. Then, Mask R-CNN is trained using the pre-processing algorithm outputs as input dataset to automatically detect, segment and classify each defect area. The training process of Mask R-CNN is improved by the prior application of the proposed pre-processing algorithm in terms of time. This shows the capacity of thermal fundamentals to improve the performance of the DL models for their application to the InfraRed Thermography (IRT) field. In addition, DL models are introduced for the first time in the thermographic inspection of water-related problems and thermal bridges when inspecting an infrastructure. Mon, 18 Apr 2022 00:00:00 GMT http://hdl.handle.net/10366/170867 2022-04-18T00:00:00Z http://hdl.handle.net/10366/170864 [EN] Renewable hydrogen is an emerging solution to the need for decarbonization of the current society, with local deployments being at the core of most implementations. It is currently in early stage of implementation, so there are not many previous experiences to standardize decision-making and the most relevant criteria. However, the lack of experts in the field of renewable hydrogen makes it difficult to design an optimal value chain. For this reason, this paper proposes a specific framework based on Geographic Information Systems, Multi-Criteria Decision Analysis and Intelligent Optimization (specifically two Genetic Algorithms denoted as Methods A and B) for the decision-making regarding the selection of optimal sites for the implementation of the renewable energy value chain from a holistic perspective; that is, considering topographic, economic, social, environmental, and demand criteria. The proposed framework is validated through the comparison of its results with those of the most extended methods in the state of the art. The results show that the application of the proposed framework implies an increase in accuracy in the determination of the locations with the highest Land Suitability Index for the renewable hydrogen value chain. Specifically, an increase in accuracy of 1.35 % (Method A) and 3.25 % (Method B) is observed with respect to the most widely used method in the literature: Analytic Hierarchy Process. Spain has been selected as a case study to validate the applicability of the proposed framework, which has facilitated the identification the optimal municipalities for the local implementation of renewable hydrogen in the country. It has been demonstrated that of the 60 projects in advanced levels of development, 87% (50 projects) have a high level of Land Suitability Index placing them in the first quartile of the ranking. In terms of investment, these projects represent around €468 million (87.7 %). It can therefore be concluded that the renewable hydrogen financing strategy of Spain can be slightly improved with the results of the proposed framework. Sat, 15 Feb 2025 00:00:00 GMT http://hdl.handle.net/10366/170864 2025-02-15T00:00:00Z http://hdl.handle.net/10366/170834 [EN] This study explores how urban morphological and environmental factors influence Urban Heat Islands (UHIs) using a geospatial modeling approach. The aim of the research is to develop a methodology to assess UHI effects, emphasizing the role of urban morphology, land use, and vegetation in nighttime heat accumulation. A micro-scale analysis with a 50 m resolution is conducted by integrating a custom QGIS plugin with open-access data, ensuring broad applicability. The 50 m resolution was chosen because it allows for the capture of local variations in UHI intensity while maintaining the scalability of the urban analysis across different city contexts. Non-parametric statistical analyses (ANOVA, Kruskal–Wallis H test, and correlation assessments) were used to evaluate the relationships between the urban parameters—wind corridors, altitude, vegetation (NDVI), surface water (NDWI), and the Sky View Factor (SVF)—and Nighttime Land Surface Temperature (LST). Given that UHI variations during summer, particularly in cities of the Iberian Peninsula, are closely linked to summer heat severity, this factor was considered to classify the cities for the study. Correlation analyses confirm that all tested factors influence LST, with wind corridors being the least significant. The model performance evaluation shows the highest errors in cities with lower summer severity (RMSE = 1.586 °C, MAE = 1.2686 °C, MAPE = 6.99%) and the best performance in warmer cities (RMSE = 1.4 °C, MAE = 1.14 °C, MAPE = 4.5%). Validation in four cities of the Iberian Peninsula confirmed the model’s reliability, with the worst RMSE value of 2.04 °C. These findings contribute to a better understanding of the factors driving UHIs and provide a scalable assessment framework. Wed, 28 May 2025 00:00:00 GMT http://hdl.handle.net/10366/170834 2025-05-28T00:00:00Z http://hdl.handle.net/10366/170832 [EN] The fi­rst appearance of concern for the good condition of a building dates back to ancient times. In recent years, with the emergence of new inspection technologies and the growing concern about climate change and people’s health, the concern about the integrity of building structures has been extended to their analysis as insulating envelopes. In addition, the growing network of historic buildings gives this sector special attention. Therefore, this study presents a comprehensive review of the application of two of the most common and most successful Non-Destructive Techniques (NDTs) when inspecting a building: InfraRed Thermography (IRT) and Ground-Penetrating Radar (GPR). To the best knowledge of the authors, it is the fi­rst time that a joint compilation of the state-of-the-art of both IRT and GPR for building evaluation is performed in the same work, with special emphasis on applications that integrate both technologies. The authors briefly explain the performance of each NDT, along with the individual and collective advantages of their uses in the building sector. Subsequently, an in-depth analysis of the most relevant references is described, according to the building materials to be studied and the purpose to be achieved: structural safety, energy efficiency and well-being, and heritage preservation. Then, three different case studies are presented with the aim of illustrating the potential of the combined use of IRT and GPR in the evaluation of buildings for the purposes defi­ned. Last, the final remarks and future lines are described on the application of these two interesting inspection technologies in the preservation and conservation of the building sector. Mon, 05 Sep 2022 00:00:00 GMT http://hdl.handle.net/10366/170832 2022-09-05T00:00:00Z