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<title>Articulos Científicos Ing. Ind.</title>
<link href="http://hdl.handle.net/10498/6772" rel="alternate"/>
<subtitle/>
<id>http://hdl.handle.net/10498/6772</id>
<updated>2026-11-04T21:47:53Z</updated>
<dc:date>2026-11-04T21:47:53Z</dc:date>
<entry>
<title>From Coastal Flooding to Residual Beach Functionality: Implications of Sea-Level Rise for Coastal Management in Southern Spain</title>
<link href="http://hdl.handle.net/10498/40398" rel="alternate"/>
<author>
<name>Contreras de Villar, Antonio</name>
</author>
<author>
<name>Contreras de Villar, Francisco</name>
</author>
<author>
<name>Vidal Pérez, Juan Manuel</name>
</author>
<author>
<name>Pérez Moreno, Cristina</name>
</author>
<author>
<name>Alonso del Rosario, José Juan</name>
</author>
<author>
<name>López García, Patricia</name>
</author>
<author>
<name>Jigena Antelo, Bismarck</name>
</author>
<author>
<name>Muñoz Pérez, Juan José</name>
</author>
<id>http://hdl.handle.net/10498/40398</id>
<updated>2026-10-20T04:01:28Z</updated>
<published>2026-10-16T00:00:00Z</published>
<summary type="text">From Coastal Flooding to Residual Beach Functionality: Implications of Sea-Level Rise for Coastal Management in Southern Spain
Contreras de Villar, Antonio; Contreras de Villar, Francisco; Vidal Pérez, Juan Manuel; Pérez Moreno, Cristina; Alonso del Rosario, José Juan; López García, Patricia; Jigena Antelo, Bismarck; Muñoz Pérez, Juan José
Sea-level rise may substantially reduce the dry space available on sandy beaches, although its effects depend strongly on local beach characteristics. This study evaluates the residual spatial functionality of five beaches in southern Spain using two normalized indicators: residual available surface area (FA) and residual available beach width (FW). Both express the proportion of dry beach space remaining relative to a common present-day reference condition without wave run-up. The analysis builds on previously calculated inundation results for present conditions and representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios for 2026–2045 and 2081–2100, including astronomical tide, meteorological residual, sea-level rise, and wave run-up. Marked differences are observed among the studied beaches across both time periods. Under RCP 4.5 in 2026–2045, Torreguadiaro retains 85.1% of its reference area and 79.8% of its reference mean width. Rinconcillo retains 31.9% and 38.7%, respectively. Under RCP 8.5 in 2081–2100, these values decrease to 75.6%&#13;
and 65.3% at Torreguadiaro. At Rinconcillo, they decrease to 7.1% and 17.6%. FA and FW also evolve differently among sites: their values remain relatively close at Malagueta but diverge markedly at Rinconcillo under the most severe scenario. Joint consideration of FA and FW therefore provides complementary information on the amount and configuration of residual dry beach space and may support comparison among beaches from a coastal management perspective.
</summary>
<dc:date>2026-10-16T00:00:00Z</dc:date>
</entry>
<entry>
<title>Engineering Lessons from Four Decades of Beach Nourishment in Spain: A Review of Quality Control, Monitoring and Adaptive Coastal Management</title>
<link href="http://hdl.handle.net/10498/40142" rel="alternate"/>
<author>
<name>Contreras-de-Villar, Antonio</name>
</author>
<author>
<name>Poullet, Patricio</name>
</author>
<author>
<name>Garcia-Lopez, Santiago</name>
</author>
<author>
<name>Martell, Raul</name>
</author>
<author>
<name>Contreras de Villar, Francisco</name>
</author>
<author>
<name>Jigena Antelo, Bismarck</name>
</author>
<author>
<name>Muñoz-Perez, Juan J.</name>
</author>
<id>http://hdl.handle.net/10498/40142</id>
<updated>2026-08-01T04:01:23Z</updated>
<published>2026-07-30T00:00:00Z</published>
<summary type="text">Engineering Lessons from Four Decades of Beach Nourishment in Spain: A Review of Quality Control, Monitoring and Adaptive Coastal Management
Contreras-de-Villar, Antonio; Poullet, Patricio; Garcia-Lopez, Santiago; Martell, Raul; Contreras de Villar, Francisco; Jigena Antelo, Bismarck; Muñoz-Perez, Juan J.
Beach nourishment has become one of the principal soft-engineering strategies for mitigating coastal erosion while preserving the recreational, environmental and protective functions of sandy beaches. During the last four decades, Spain has accumulated extensive experience in beach nourishment, with annual nourishment volumes approaching 10 million m3 under a wide range of coastal settings. Many of the best-documented engineering examples originate from the Gulf of Cadiz, although the lessons synthesized here are interpreted within the broader context of Spanish coastal practice. This structured narrative review combines publications retrieved from Web of Science and Google Scholar with Spanish technical reports, project records, and institutional sources. It focuses on borrow-area characterization, sediment compatibility, construction quality control, post-nourishment monitoring, and adaptive management. Rather than providing a chronological description of nourishment projects, this review examines how repeated project implementation, monitoring and maintenance have progressively reduced uncertainty and improved engineering practice. Long-term nourishment performance is controlled more by sediment compatibility, local morphodynamics, and construction quality than by the total sediment volume placed. It also demonstrates that systematic monitoring provides the feedback required to improve future project design, optimize maintenance strategies and support evidence-based coastal management. The principal contribution of four decades of Spanish beach nourishment has been the progressive development of an engineering framework in which quality control, long-term monitoring and accumulated field experience continuously refine project planning and management. Although derived from Spanish practice, the lessons synthesized are broadly applicable to nourishment programs facing increasing pressures from climate change, sediment scarcity and growing environmental constraints.
</summary>
<dc:date>2026-07-30T00:00:00Z</dc:date>
</entry>
<entry>
<title>Drones para vigilar las playas en la era del cambio climático</title>
<link href="http://hdl.handle.net/10498/39620" rel="alternate"/>
<author>
<name>Contreras de Villar, Francisco</name>
</author>
<author>
<name>Garcia, Javier</name>
</author>
<author>
<name>Muñoz Pérez, Juan José</name>
</author>
<id>http://hdl.handle.net/10498/39620</id>
<updated>2026-05-21T04:04:24Z</updated>
<published>2026-01-29T00:00:00Z</published>
<summary type="text">Drones para vigilar las playas en la era del cambio climático
Contreras de Villar, Francisco; Garcia, Javier; Muñoz Pérez, Juan José
Durante una tormenta, una playa puede perder en cuestión de horas la arena que tardó meses en acumularse. Esos cambios, que antes solo podían medirse recorriendo la costa con equipos pesados y muchas horas de trabajo, hoy pueden registrarse con precisión milimétrica desde el aire. Basta con planificar un vuelo y enviar un dron sobre la línea de costa. Investigadores de la Universidad de Cádiz han analizado cómo pequeños ajustes en la planificación del vuelo pueden marcar una gran diferencia en los resultados.&#13;
&#13;
Comprender cómo evoluciona una playa resulta esencial para anticipar procesos de erosión, diseñar obras costeras o evaluar los impactos del cambio climático. Sin embargo, durante décadas, medir este entorno dinámico requería recorrerlo a pie usando equipos topográficos o GPS de alta precisión. Son métodos fiables, pero costosos, lentos y dependientes del clima.&#13;
&#13;
Nuestro equipo de la Universidad de Cádiz ha demostrado que existe una alternativa más ágil y accesible: usar drones para realizar nivelaciones de playa de alta precisión. La investigación responde a una pregunta clave: ¿cuáles son las mejores condiciones para volar un dron si queremos medir una playa con precisión de centímetros?
</summary>
<dc:date>2026-01-29T00:00:00Z</dc:date>
</entry>
<entry>
<title>Hyperspectral imaging and artificial neural networks for oil spills automatic detection</title>
<link href="http://hdl.handle.net/10498/39529" rel="alternate"/>
<author>
<name>Carrasco García, María Gema</name>
</author>
<author>
<name>Rodríguez García, María Inmaculada</name>
</author>
<author>
<name>González Enrique, Francisco Javier</name>
</author>
<author>
<name>Ruiz Águilar, Juan Jesús</name>
</author>
<author>
<name>Alcántara Pérez, José Manuel</name>
</author>
<author>
<name>Turias Domínguez, Ignacio José</name>
</author>
<id>http://hdl.handle.net/10498/39529</id>
<updated>2026-05-07T04:02:33Z</updated>
<published>2026-02-01T00:00:00Z</published>
<summary type="text">Hyperspectral imaging and artificial neural networks for oil spills automatic detection
Carrasco García, María Gema; Rodríguez García, María Inmaculada; González Enrique, Francisco Javier; Ruiz Águilar, Juan Jesús; Alcántara Pérez, José Manuel; Turias Domínguez, Ignacio José
The detection of hydrocarbon spills in marine environments is a critical challenge due to their severe impact on aquatic ecosystems. Hyperspectral imaging technology has emerged as a promising tool for detecting such spills, offering high-resolution spectral data for accurate identification. This work aims to develop an automated procedure for detecting hydrocarbon spills in local marine areas using a compact hyperspectral camera installed on a drone. The methodology integrates machine learning with hyperspectral technology, employing hyperspectral signatures of various hydrocarbons and water to create a spectral knowledge base for classification. A novel approach is used in the classifier training, where hyperspectral signatures are directly utilized as input to an artificial neural network, bypassing the need for extensive image labelling, eliminating the requirement for a large image dataset, and significantly reducing the computational costs associated with deep learning models. The trained classifiers, using autoencoders for encoding hyperspectral signatures, were applied to classify hyperspectral profiles (hyperpixels) in hyperspectral images. The results were highly promising, with the classifiers achieving near-perfect accuracy in detecting contaminated water and distinguishing between different hydrocarbons. Recall and F-score values above 0.90 demonstrate the effectiveness of the proposed approach, offering an efficient, scalable solution for real-time monitoring of marine spills.
</summary>
<dc:date>2026-02-01T00:00:00Z</dc:date>
</entry>
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