{"id":243251,"date":"2025-05-06T13:39:28","date_gmt":"2025-05-06T11:39:28","guid":{"rendered":"https:\/\/cimne.com\/research\/research-clusters\/machine-learning-and-models-in-hydro-environmental-engineering\/machine-learning-in-civil-engineering\/"},"modified":"2026-04-17T14:34:10","modified_gmt":"2026-04-17T12:34:10","slug":"aprendizaje-automatico-ingenieria-civil","status":"publish","type":"page","link":"https:\/\/cimne.com\/es\/investigacion\/clusters-de-investigacion\/aprendizaje-automatico-modelos-ingenieria-hidroambiental\/aprendizaje-automatico-ingenieria-civil\/","title":{"rendered":"Aprendizaje autom\u00e1tico en ingenier\u00eda civil"},"content":{"rendered":"<p>[et_pb_section fb_built=\u00bb1&#8243; disabled_on=\u00bboff|off|off\u00bb admin_label=\u00bbIntro desktop\u00bb module_id=\u00bbfirst-section\u00bb _builder_version=\u00bb4.27.3&#8243; positioning=\u00bbnone\u00bb custom_margin=\u00bb0px||0px||false|false\u00bb custom_padding=\u00bb0px||0px||false|false\u00bb border_width_all=\u00bb1px\u00bb border_color_all=\u00bb#d9d9d9&#8243; 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_builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; global_colors_info=\u00bb{}\u00bb][et_pb_text _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bbdefault\u00bb text_text_color=\u00bb#004996&#8243; text_font_size=\u00bb1.5rem\u00bb text_line_height=\u00bb1.8rem\u00bb width=\u00bb50%\u00bb width_tablet=\u00bb100%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|desktop\u00bb custom_margin=\u00bb||||true|false\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p>Este grupo desarrolla herramientas basadas en ML para resolver problemas complejos de ingenier\u00eda en los campos hidr\u00e1ulico, geomec\u00e1nico y medioambiental, combinando datos de sensores, modelizaci\u00f3n num\u00e9rica y soluciones pr\u00e1cticas de software para el an\u00e1lisis predictivo y el apoyo a la toma de decisiones.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=\u00bb1px Line Row\u00bb _builder_version=\u00bb4.27.4&#8243; 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_builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; global_colors_info=\u00bb{}\u00bb][\/et_pb_column][et_pb_column type=\u00bb1_3&#8243; _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; global_colors_info=\u00bb{}\u00bb][et_pb_text _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bbdefault\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p>El Grupo de Investigaci\u00f3n de Aprendizaje Autom\u00e1tico en Ingenier\u00eda Civil del CIMNE se centra en resolver problemas complejos de ingenier\u00eda integrando t\u00e9cnicas de aprendizaje autom\u00e1tico (AM) con datos procedentes de sensores, simulaciones num\u00e9ricas y modelizaci\u00f3n f\u00edsica.<\/p>\n<p><\/p>\n<p>El grupo tiene una amplia experiencia en la aplicaci\u00f3n de m\u00e9todos de ML a infraestructuras hidr\u00e1ulicas, como presas, aliviaderos y sistemas de abastecimiento de agua, con un s\u00f3lido historial en la supervisi\u00f3n del estado estructural, la detecci\u00f3n de anomal\u00edas y el mantenimiento predictivo.<\/p>\n<p><\/p>\n<p>M\u00e1s all\u00e1 de la hidr\u00e1ulica, el grupo tambi\u00e9n explora aplicaciones en geomec\u00e1nica, control medioambiental y procesos industriales. Las actividades de investigaci\u00f3n abarcan todo el aliento del ML, desde el preprocesamiento de datos y el desarrollo de algoritmos hasta la cuantificaci\u00f3n de la incertidumbre y la interpretabilidad. <\/p>\n<p><\/p>\n<p>&nbsp;<\/p>\n<p>[\/et_pb_text][\/et_pb_column][et_pb_column type=\u00bb1_3&#8243; _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; global_colors_info=\u00bb{}\u00bb][et_pb_text _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bbdefault\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p>Las t\u00e9cnicas incluyen modelos de conjunto, aprendizaje profundo y estrategias h\u00edbridas que combinan ML con modelos num\u00e9ricos como CFD y DEM. El grupo mantiene una fuerte orientaci\u00f3n pr\u00e1ctica, desarrollando soluciones de software personalizadas con interfaces de usuario para su despliegue en el mundo real, y expandi\u00e9ndose a \u00e1reas como la predicci\u00f3n de la calidad del agua y la desinfecci\u00f3n de aguas residuales. <\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=\u00bb1_3,1_3,1_3&#8243; _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbc7c24b71-68f1-4ba2-bc79-49dcbcce5ea1&#8243; custom_margin=\u00bb|0px||0px|false|false\u00bb custom_padding=\u00bb|0px|50px|0px|false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_column type=\u00bb1_3&#8243; _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; global_colors_info=\u00bb{}\u00bb][\/et_pb_column][et_pb_column type=\u00bb1_3&#8243; _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; global_colors_info=\u00bb{}\u00bb][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/05\/ML_1.jpg\u00bb title_text=\u00bbML_1&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb100%\u00bb module_alignment=\u00bbcenter\u00bb height=\u00bb220px\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][\/et_pb_column][et_pb_column type=\u00bb1_3&#8243; _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; global_colors_info=\u00bb{}\u00bb][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/05\/ML_2.jpg\u00bb title_text=\u00bbML_2&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb100%\u00bb module_alignment=\u00bbcenter\u00bb height=\u00bb220px\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=\u00bb1&#8243; admin_label=\u00bbResearch Tab\u00bb module_id=\u00bbsection-splitter-menu\u00bb module_class=\u00bbresearch-content tab-content\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbfed47f3e-3ebe-4259-a156-523c8e0b8966&#8243; locked=\u00bboff\u00bb collapsed=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_row column_structure=\u00bb1_4,3_4&#8243; gutter_width=\u00bb2&#8243; admin_label=\u00bbAreas de investigaci\u00f3n\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbc7c24b71-68f1-4ba2-bc79-49dcbcce5ea1&#8243; custom_padding=\u00bb0px|0px|32px|0px|false|false\u00bb locked=\u00bboff\u00bb collapsed=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_column type=\u00bb1_4&#8243; _builder_version=\u00bb4.27.0&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; global_colors_info=\u00bb{}\u00bb][et_pb_heading title=\u00bbAreas de investigaci\u00f3n\u00bb admin_label=\u00bbH2&#8243; _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh2&#8243; title_text_color=\u00bb#004996&#8243; title_font_size=\u00bb2rem\u00bb title_line_height=\u00bb2rem\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][\/et_pb_column][et_pb_column type=\u00bb3_4&#8243; _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb56e57fde-4561-4cf6-a771-0a733a7201b1&#8243; border_color_right=\u00bb#d9d9d9&#8243; global_colors_info=\u00bb{}\u00bb][et_pb_heading title=\u00bbActividades de investigaci\u00f3n con t\u00e9cnicas de aprendizaje autom\u00e1tico\u00bb admin_label=\u00bbMachine learning for dam behavior prediction\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb2rem\u00bb title_line_height=\u00bb2.25rem\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_heading title=\u00bbAprendizaje autom\u00e1tico para predecir el comportamiento de las presas\u00bb admin_label=\u00bbMachine learning for dam behavior prediction\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh4&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb custom_padding=\u00bb0px|||||\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Desarrollo de metodolog\u00edas y software para el an\u00e1lisis de datos de monitorizaci\u00f3n de presas, incluida la generaci\u00f3n de modelos predictivos ML y su interpretaci\u00f3n, con el objetivo final de apoyar la toma de decisiones en materia de seguridad de presas. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=923\" rel=\"noopener\">DOLMEN<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra1.jpg\u00bb title_text=\u00bbmachlearn-1ra1&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Software para la evaluaci\u00f3n de la seguridad de presas mediante ML: capturas de pantalla de la aplicaci\u00f3n <a class=\"in-cell-link\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352711023002947\" target=\"_blank\" rel=\"noopener\">SOLDIER <\/a>. <a class=\"in-cell-link\" href=\"https:\/\/github.com\/cimnemadrid\/SOLDIER\" target=\"_blank\" rel=\"noopener\">Repo de GitHub<\/a>.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_gallery gallery_ids=\u00bb238190,238193&#8243; posts_number=\u00bb2&#8243; orientation=\u00bbportrait\u00bb show_title_and_caption=\u00bboff\u00bb show_pagination=\u00bboff\u00bb zoom_icon_color=\u00bbRGBA(255,255,255,0)\u00bb hover_overlay_color=\u00bbRGBA(255,255,255,0)\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb module_alignment=\u00bbcenter\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_gallery][et_pb_heading title=\u00bbAprendizaje autom\u00e1tico avanzado para la detecci\u00f3n y localizaci\u00f3n de anomal\u00edas\u00bb admin_label=\u00bbAdvanced machine learning for anomaly detection and localization\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Exploramos las posibilidades del Aprendizaje Profundo y otros algoritmos avanzados de ML, como los Autoencoders, para curar los datos de monitorizaci\u00f3n, detectar anomal\u00edas y localizar posibles da\u00f1os estructurales.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra2.png\u00bb title_text=\u00bbmachlearn-1ra2&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p>Estructura del autocodificador (izquierda) y detecci\u00f3n de datos de vigilancia an\u00f3malos (derecha). Source: https:\/\/doi.org\/10.1007\/s13349-025-00910-4. <\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbNuevas herramientas computacionales para la evaluaci\u00f3n de la seguridad de las presas basada en la fiabilidad  \u00bb admin_label=\u00bbNew computational tools for reliability-based dam safety assessment \u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Uso de modelos ML como apoyo al an\u00e1lisis MEF para predecir la respuesta de las presas, incluyendo el an\u00e1lisis de incertidumbre y riesgo. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=846\" rel=\"noopener\">TRISTAN<\/a>  <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra3.png\u00bb title_text=\u00bbmachlearn-1ra3&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Detecci\u00f3n de anomal\u00edas en presas: ejemplo de red de vigilancia (izquierda) y modelo num\u00e9rico para simular sucesos an\u00f3malos (derecha). Source: <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/w13172387\" rel=\"noopener\">https:\/\/doi.org\/10.3390\/w13172387<\/a>  <\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbAn\u00e1lisis de estructuras hidr\u00e1ulicas\u00bb admin_label=\u00bbNew computational tools for reliability-based dam safety assessment \u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">An\u00e1lisis del comportamiento hidr\u00e1ulico de aliviaderos y desag\u00fces de fondo de presas combinando m\u00e9todos num\u00e9ricos (PFEM, superficie libre) y t\u00e9cnicas ML.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra4.png\u00bb title_text=\u00bbmachlearn-1ra4&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Rendimiento hidr\u00e1ulico del aliviadero: ejemplo de geometr\u00eda (izquierda) y relaci\u00f3n entre valores observados y predichos a partir de modelos ML de capacidad de descarga. Source: <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/doi.org\/10.3390\/w11030544\" rel=\"noopener\">https:\/\/doi.org\/10.3390\/w11030544<\/a>  <\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbOptimizaci\u00f3n inteligente de los procesos industriales  \u00bb admin_label=\u00bbSmart optimization of industrial processes \u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Soporte y optimizaci\u00f3n de procesos de dise\u00f1o de deformaci\u00f3n met\u00e1lica rotacional. Uso de un marco de Gemelos Digitales basado en el MEF combinado con t\u00e9cnicas de clasificaci\u00f3n ML. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=885\" rel=\"noopener\">OPTIPRO<\/a>   <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra5.png\u00bb title_text=\u00bbmachlearn-1ra5&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">An\u00e1lisis de procesos de conformado de metales: equipo industrial (izquierda) e interfaz gr\u00e1fica de usuario para la parametrizaci\u00f3n del proceso (derecha)<\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbCalidad del agua y t\u00e9cnicas de tratamiento del agua\u00bb admin_label=\u00bbWater quality and water treatment techniques\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Aplicaci\u00f3n de modelos ML para la predicci\u00f3n del estado de la calidad del agua en masas de agua y la evaluaci\u00f3n de tratamientos avanzados de eliminaci\u00f3n de contaminantes del agua. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=938\" rel=\"noopener\">DIGIT4WATER<\/a>  <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra6.png\u00bb title_text=\u00bbmachlearn-1ra6&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Predicci\u00f3n de tratamientos avanzados de aguas residuales con t\u00e9cnicas de ML. M\u00e1s informaci\u00f3n: <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/doi.org\/10.1016\/j.jenvman.2024.123537\" rel=\"noopener\">https:\/\/doi.org\/10.1016\/j.jenvman.2024.123537<\/a>  <\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbPrevisi\u00f3n de la calidad del aire\u00bb admin_label=\u00bbAir quality forecasting\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Aplicaci\u00f3n de modelos ML para la predicci\u00f3n del estado de la calidad del aire. Relacionado con: Proyectos <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/piksel-web.cimne.com\/projecte-piksel\/\" rel=\"noopener\">PIKSEL<\/a>, <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=1041\" rel=\"noopener\">PRONURB<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra7.png\u00bb title_text=\u00bbmachlearn-1ra7&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Predicci\u00f3n basada en ML de la concentraci\u00f3n de O3 troposf\u00e9rico en la <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/piksel-web.cimne.com\/repte-3\/\" rel=\"noopener\">plataforma PIKSEL<\/a>.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbIdentificaci\u00f3n de riesgos por rotura de presas con sustitutos ML\u00bb admin_label=\u00bbIdentification of hazards due to dam failure with ML surrogates\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Estimaci\u00f3n autom\u00e1tica de los da\u00f1os potenciales en caso de fallo de los embalses fuera del cauce. Relacionado con: Proyecto <a class=\"in-cell-link\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=886\" target=\"_blank\" rel=\"noopener\">ACROPOLIS<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra8.png\u00bb title_text=\u00bbmachlearn-1ra8&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Capturas de pantalla del <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2352711024000281#sec0002\" rel=\"noopener\">software ACROPOLIS<\/a>. Disponible en <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/github.com\/nathaliasilvac\/ACROPOLIS\" rel=\"noopener\">GitHub<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbCalibraci\u00f3n de modelos num\u00e9ricos con ML  \u00bb admin_label=\u00bbAir quality forecasting\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Calibraci\u00f3n de los par\u00e1metros del M\u00e9todo de los Elementos Discretos (DEM) combinando el c\u00e1lculo num\u00e9rico de alto rendimiento con el ML. Relacionado con: Proyectos <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=846\" rel=\"noopener\">TRISTAN,<\/a> <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=713\" rel=\"noopener\">HIRMA<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-1ra9.png\u00bb title_text=\u00bbmachlearn-1ra9&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||32px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Calibraci\u00f3n de los par\u00e1metros DEM del comportamiento de la arcilla: modelo num\u00e9rico para simular las pruebas de comportamiento de la arcilla (izquierda), y an\u00e1lisis de calibraci\u00f3n mediante ML (centro y derecha). Source: <a class=\"in-cell-link\" href=\"https:\/\/doi.org\/10.1007\/s40571-022-00550-1\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1007\/s40571-022-00550-1<\/a> <\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbActividades de investigaci\u00f3n con m\u00e9todos num\u00e9ricos\u00bb admin_label=\u00bbMachine learning for dam behavior prediction\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb2rem\u00bb title_line_height=\u00bb2.25rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_heading title=\u00bbComportamiento termomec\u00e1nico de las presas de hormig\u00f3n\u00bb admin_label=\u00bbThermo-mechanical behavior of concrete dams\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Simulaci\u00f3n del comportamiento de presas de hormig\u00f3n durante las fases de construcci\u00f3n y explotaci\u00f3n integrando cargas termomec\u00e1nicas de alto detalle. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=677\" rel=\"noopener\">ACOMBO<\/a>; Aplicaci\u00f3n inform\u00e1tica \u00ab<a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/pypi.org\/project\/KratosDamApplication\/\" rel=\"noopener\">DamApplication<\/a>\u00bb (integrada en el marco Kratos). <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-2ra1.jpg\u00bb title_text=\u00bbmachlearn-2ra1&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Modelizaci\u00f3n de presas de hormig\u00f3n: simulaci\u00f3n de la fase de construcci\u00f3n (izquierda), campo de desplazamientos (centro) y campo de tensiones (derecha). Source: <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/doi.org\/10.1007\/s11831-020-09439-9\" rel=\"noopener\">https:\/\/doi.org\/10.1007\/s11831-020-09439-9<\/a> <\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbDise\u00f1o de aliviaderos de bloques en forma de cu\u00f1a  \u00bb admin_label=\u00bbDesign of wedge-shaped block spillways \u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Simulaci\u00f3n CFD mediante modelizaci\u00f3n MEF euleriana y simulaci\u00f3n de estabilidad de bloques mediante modelizaci\u00f3n DEM. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=826\" rel=\"noopener\">PABLO<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-2ra2.jpg\u00bb title_text=\u00bbmachlearn-2ra2&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Simulaci\u00f3n de aliviaderos de bloques en forma de cu\u00f1a: an\u00e1lisis hidr\u00e1ulico (izquierda), dise\u00f1o de bloques (centro) y an\u00e1lisis de estabilidad de bloques (derecha)<\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbDise\u00f1o industrial de compuertas fusibles de presas  \u00bb admin_label=\u00bbIndustrial design of dam fuse gates \u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Simulaciones de interacci\u00f3n fluido-s\u00f3lido mediante modelizaci\u00f3n PFEM+DEM para calcular los siguientes procesos: flujo de descarga para diferentes posiciones de la compuerta, velocidad de ca\u00edda de la compuerta y fuerza de impacto compuerta-pared. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=827\" rel=\"noopener\">COFRE<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-2ra3.jpg\u00bb title_text=\u00bbmachlearn-2ra3&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Simulaci\u00f3n de puertas fusibles: dise\u00f1o de la geometr\u00eda (izquierda), simulaci\u00f3n 2D de la interacci\u00f3n fluido-s\u00f3lido (centro) y simulaci\u00f3n 3D (derecha)<\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbAn\u00e1lisis CFD de estructuras hidr\u00e1ulicas: aliviaderos altamente convergentes, cuencas amortiguadoras y modelizaci\u00f3n de sistemas de drenaje  \u00bb admin_label=\u00bbAir quality forecasting\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Simulaci\u00f3n de fen\u00f3menos hidr\u00e1ulicos complejos en 3D, mediante modelos FEM y PFEM, como la posici\u00f3n de la superficie libre del salto hidr\u00e1ulico, los campos de presi\u00f3n y velocidad y la identificaci\u00f3n de zonas con riesgo de erosi\u00f3n.  <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-2ra4.jpg\u00bb title_text=\u00bbmachlearn-2ra4&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">An\u00e1lisis CFD de estructuras hidr\u00e1ulicas: aliviaderos altamente convergentes (izquierda) y simulaci\u00f3n de cuencos amortiguadores (derecha)<\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbAn\u00e1lisis del comportamiento del balasto ferroviario con el M\u00e9todo de los Elementos Discretos (DEM)\u00bb admin_label=\u00bbAir quality forecasting\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Simulaci\u00f3n de infraestructuras ferroviarias frente a las acciones del cambio clim\u00e1tico y evaluaci\u00f3n de la respuesta del balasto ferroviario mediante un modelo DEM. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/vnews\/10470\" rel=\"noopener\">RESILTRACK<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-2ra5.jpg\u00bb title_text=\u00bbmachlearn-2ra5&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Simulaci\u00f3n de infraestructuras ferroviarias: simulaci\u00f3n del comportamiento del balasto (izquierda), an\u00e1lisis de calibraci\u00f3n (centro) y simulaci\u00f3n de infraestructuras ferroviarias (derecha)<\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbModelizaci\u00f3n num\u00e9rica de Redes de Distribuci\u00f3n de Agua (RDA)\u00bb admin_label=\u00bbNumerical modelling of Water Distribution Networks (WDN)\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Desarrollo de modelos num\u00e9ricos para la simulaci\u00f3n de fugas mediante solucionadores avanzados basados en la presi\u00f3n. Relacionado con: Proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=828\" rel=\"noopener\">SMILER<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-2ra6.jpg\u00bb title_text=\u00bbmachlearn-2ra6&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Esquema metodol\u00f3gico de utilidad num\u00e9rica avanzada para simular casos masivos de escenarios de fugas en las RDAs<\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbEnfoques h\u00edbridos\u00bb admin_label=\u00bbMachine learning for dam behavior prediction\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb2rem\u00bb title_line_height=\u00bb2.25rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_heading title=\u00bbPredicci\u00f3n flexible y precisa del comportamiento de las presas\u00bb admin_label=\u00bbFlexible and accurate prediction of dam behavior\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Enfoques inteligentes para aumentar la flexibilidad y precisi\u00f3n de la predicci\u00f3n del comportamiento de las presas combinando resultados de m\u00e9todos num\u00e9ricos y modelos ML mediante datos de monitorizaci\u00f3n. Relacionado con: proyecto <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/www.cimne.com\/sgp\/rtd\/Project.aspx?id=923\" rel=\"noopener\">DOLMEN<\/a>. <\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-3ra1.jpg\u00bb title_text=\u00bbmachlearn-3ra1&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Predicci\u00f3n del desplazamiento de presas de arco con enfoques h\u00edbridos.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_heading title=\u00bbPredicci\u00f3n del caudal a corto plazo con modelos h\u00edbridos\u00bb admin_label=\u00bbAir quality forecasting\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb title_level=\u00bbh3&#8243; title_font=\u00bb|700|||||||\u00bb title_font_size=\u00bb1.5rem\u00bb title_line_height=\u00bb1.8rem\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_heading][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Metodolog\u00edas para combinar los resultados de los modelos num\u00e9ricos 2D (basados en el software IBER) con modelos basados en ML que tengan en cuenta las precipitaciones y los valores pasados del caudal para obtener predicciones precisas y r\u00e1pidas con 3 horas de antelaci\u00f3n del caudal en caso de inundaci\u00f3n.<\/span><\/p>\n<p>[\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/cimne.com\/wp-content\/uploads\/2025\/08\/machlearn-3ra2.jpg\u00bb title_text=\u00bbmachlearn-3ra2&#8243; show_in_lightbox=\u00bbon\u00bb align=\u00bbcenter\u00bb module_class=\u00bbfit-img\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bb1f4e2daa-a4c7-40db-b678-0b21ac6841bd\u00bb width=\u00bb60%\u00bb width_tablet=\u00bb60%\u00bb width_phone=\u00bb100%\u00bb width_last_edited=\u00bbon|phone\u00bb module_alignment=\u00bbcenter\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb12px\u00bb text_orientation=\u00bbcenter\u00bb width=\u00bb60%\u00bb module_alignment=\u00bbcenter\u00bb custom_padding=\u00bb||20px||false|false\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb]<\/p>\n<p><span data-sheets-root=\"1\">Hidrograma observado frente a predicciones con enfoques h\u00edbridos. Source: <a class=\"in-cell-link\" target=\"_blank\" href=\"https:\/\/doi.org\/10.1080\/02626667.2024.2426720\" rel=\"noopener\">https:\/\/doi.org\/10.1080\/02626667.2024.2426720<\/a> <\/span><\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=\u00bb1px Line Row\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb width=\u00bb100%\u00bb max_width=\u00bb2500px\u00bb custom_margin=\u00bb0px||0px||true|false\u00bb custom_padding=\u00bb0px||0px||true|false\u00bb collapsed=\u00bbon\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_column type=\u00bb4_4&#8243; _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_divider show_divider=\u00bboff\u00bb _builder_version=\u00bb4.27.4&#8243; _module_preset=\u00bbdefault\u00bb width=\u00bb100%\u00bb height=\u00bb0px\u00bb custom_margin=\u00bb0px||0px||true|false\u00bb custom_padding=\u00bb0px||0px||true|false\u00bb border_width_bottom=\u00bb1px\u00bb border_color_bottom=\u00bb#d9d9d9&#8243; 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