Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC)
Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC’s engineering properties (i.e., Poisson’s Ratio -v-, Elastic Modulus -E-,...
- Autores:
-
Polo Mendoza, Rodrigo
Martínez Arguelles, Gilberto
Peñabaena Niebles, Rita
Duque, Jose
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13827
- Acceso en línea:
- https://hdl.handle.net/11323/13827
https://repositorio.cuc.edu.co/
- Palabra clave:
- Computational modelling
Concrete structures
Construction materials
Deep neural networks
Machine learning
Portland cement concrete
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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| dc.title.eng.fl_str_mv |
Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC) |
| title |
Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC) |
| spellingShingle |
Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC) Computational modelling Concrete structures Construction materials Deep neural networks Machine learning Portland cement concrete |
| title_short |
Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC) |
| title_full |
Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC) |
| title_fullStr |
Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC) |
| title_full_unstemmed |
Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC) |
| title_sort |
Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC) |
| dc.creator.fl_str_mv |
Polo Mendoza, Rodrigo Martínez Arguelles, Gilberto Peñabaena Niebles, Rita Duque, Jose |
| dc.contributor.author.none.fl_str_mv |
Polo Mendoza, Rodrigo Martínez Arguelles, Gilberto Peñabaena Niebles, Rita Duque, Jose |
| dc.subject.proposal.eng.fl_str_mv |
Computational modelling Concrete structures Construction materials Deep neural networks Machine learning Portland cement concrete |
| topic |
Computational modelling Concrete structures Construction materials Deep neural networks Machine learning Portland cement concrete |
| description |
Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC’s engineering properties (i.e., Poisson’s Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC’s engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-11-25T18:03:51Z |
| dc.date.available.none.fl_str_mv |
2024-11-25T18:03:51Z |
| dc.date.issued.none.fl_str_mv |
2024-05-03 |
| dc.type.none.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.content.none.fl_str_mv |
Text |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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| dc.identifier.citation.none.fl_str_mv |
Polo-Mendoza, R., Martinez-Arguelles, G., Peñabaena-Niebles, R. et al. Development of a Machine Learning (ML)-Based Computational Model to Estimate the Engineering Properties of Portland Cement Concrete (PCC). Arab J Sci Eng 49, 14351–14365 (2024). https://doi.org/10.1007/s13369-024-08794-0 |
| dc.identifier.issn.none.fl_str_mv |
2193-567X |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/13827 |
| dc.identifier.doi.none.fl_str_mv |
10.1007/s13369-024-08794-0 |
| dc.identifier.eissn.none.fl_str_mv |
2191-4281 |
| dc.identifier.instname.none.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.none.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.none.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
Polo-Mendoza, R., Martinez-Arguelles, G., Peñabaena-Niebles, R. et al. Development of a Machine Learning (ML)-Based Computational Model to Estimate the Engineering Properties of Portland Cement Concrete (PCC). Arab J Sci Eng 49, 14351–14365 (2024). https://doi.org/10.1007/s13369-024-08794-0 2193-567X 10.1007/s13369-024-08794-0 2191-4281 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/13827 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartofjournal.none.fl_str_mv |
Arabian journal for science and engineering |
| dc.relation.references.none.fl_str_mv |
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Mater. 20, 1–17 (2024). https://doi.org/ 10.1016/j.cscm.2023.e02800 Li, Z.: Extracting spatial effects from machine learning model using local interpretation method: an example of SHAP and XGBoost. Comput. Environ. Urban Syst. 96, 1–18 (2022). https://doi.org/10. 1016/j.compenvurbsys.2022.101845 Kim, Y.; Kim, Y.: Explainable heat-related mortality with random forest and SHapley Additive exPlanations (SHAP) models. Sustain. Cities Soc. 79, 1–15 (2022). https://doi.org/10.1016/j.scs.2022.10 3677 Lin, K.; Gao, Y.: Model interpretability of financial fraud detection by group SHAP. Expert Syst. Appl. 210, 1–9 (2022). https://doi. org/10.1016/j.eswa.2022.118354 Kashifi, M.T.: Investigating two-wheelers risk factors for severe crashes using an interpretable machine learning approach and SHAP analysis. 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In: 31st International Symposium on Computer Architecture and High Performance Computing (SBACPAD), pp. 160–167 (2019) Aghapour, Z.; Sharifian, S.; Taheri, H.: Task offloading and resource allocation algorithm based on deep reinforcement learning for distributed AI execution tasks in IoT edge computing environments. Comput. Netw. 223, 1–17 (2023). https://doi.org/10.1016/ j.comnet.2023.109577 Assaf, A.M.; Haron, H.; Hamed, H.N.A.H.; Ghaleb, F.A.; Dalam, M.E.; Eisa, T.A.E.: Improving solar radiation forecasting utilizing data augmentation model generative adversarial networks with convolutional support vector machine (GAN-CSVR). Appl. Sci. 13, 1–23 (2023). https://doi.org/10.3390/app132312768 Harrou, F.; Dairi, A.; Dorbane, A.; Sun, Y.: Energy consumption prediction in water treatment plants using deep learning with data augmentation. Res. Eng. 20, 1–14 (2023). https://doi.org/10.1016/ j.rineng.2023.101428 Liu, K.-H.; Xie, T.-Y.; Cai, Z.-K.; Chen, G.-M.; Zhao, X.-Y.: Datadriven prediction and optimization of axial compressive strength for FRP-reinforced CFST columns using synthetic data augmentation. Eng. Struct. 300, 1–16 (2024). https://doi.org/10.1016/j.engs truct.2023.117225 Mumuni, A.; Mumuni, F.: Data augmentation: a comprehensive survey of modern approaches. Array 16, 1–27 (2022). https://doi. org/10.1016/j.array.2022.100258 Maharana, K.; Mondal, S.; Nemade, B.: A review: data preprocessing and data augmentation techniques. Glob. Transitions Proc. 3, 91–99 (2022). https://doi.org/10.1016/j.gltp.2022.04.020 Walubita, L.F.; Martinez-Arguelles, G.; Polo-Mendoza, R.; IckLee, S.; Fuentes, L.: Comparative environmental assessment of rigid, flexible, and perpetual pavement: a case study of Texas. Sustainability 14, 1–22 (2022). https://doi.org/10.3390/su14169983 Gupta, S.; Chaudhary, S.: State of the art review on supplementary cementitious materials in India—II: characteristics of SCMs, effect on concrete and environmental impact. J. Clean. Prod. 357, 1–19 (2022). https://doi.org/10.1016/j.jclepro.2022.131945 Sharma, R.K.; Singh, D.; Dasaka, S.M.: Investigating supplementary cementitious materials’ effects on stabilized aggregate performance, behaviour, and design aspects. Constr. Build. Mater. 411, 1–16 (2024). https://doi.org/10.1016/j.conbuildmat.2023.13 4564 |
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© The Author(s) 2024. |
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Atribución 4.0 Internacional (CC BY 4.0) |
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Atribución 4.0 Internacional (CC BY 4.0) © The Author(s) 2024. https://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
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15 páginas |
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Springer nature |
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Atribución 4.0 Internacional (CC BY 4.0)© The Author(s) 2024.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Polo Mendoza, RodrigoMartínez Arguelles, GilbertoPeñabaena Niebles, RitaDuque, Jose2024-11-25T18:03:51Z2024-11-25T18:03:51Z2024-05-03Polo-Mendoza, R., Martinez-Arguelles, G., Peñabaena-Niebles, R. et al. Development of a Machine Learning (ML)-Based Computational Model to Estimate the Engineering Properties of Portland Cement Concrete (PCC). Arab J Sci Eng 49, 14351–14365 (2024). https://doi.org/10.1007/s13369-024-08794-02193-567Xhttps://hdl.handle.net/11323/1382710.1007/s13369-024-08794-02191-4281Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC’s engineering properties (i.e., Poisson’s Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC’s engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders.15 páginasapplication/pdfengSpringer natureGermanyhttps://link.springer.com/article/10.1007/s13369-024-08794-0Development of a Machine Learning (ML)-based computational model to estimate the engineering properties of Portland Cement Concrete (PCC)Artículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Arabian journal for science and engineeringLiu, Y.; Du, P.; Tan, K.H.; Du, Y.; Su, J.; Shi, C.: Experimental and analytical studies on residual flexural behaviour of reinforced alkali-activated slag-based concrete beams after exposure to fire. Eng. Struct. 298, 1–14 (2024). https://doi.org/10.1016/j.engstruct. 2023.117035Singh, A.; Bhadauria, S.S.; Thakare, A.A.; Kumar, A.; Mudgal, M.; Chaudhary, S.: Durability assessment of mechanochemically activated geopolymer concrete with a low molarity alkali solution. Case Stud. Constr. Mater. 20, 1–19 (2024). https://doi.org/10.1016/ j.cscm.2023.e02715Singh, P.R.; Vanapalli, K.R.; Jadda, K.: Durability assessment of fly ash, GGBS, and silica fume based geopolymer concrete with recycled aggregates against acid and sulfate attack. J. Build. Eng. 82, 1–17 (2024). https://doi.org/10.1016/j.jobe.2023.108354Polo-Mendoza, R.; Mora, O.; Duque, J.; Turbay, E.; MartinezArguelles, G.; Fuentes, L.; Guerrero, O.; Perez, S.: Environmental and economic feasibility of implementing perpetual pavements (PPs) against conventional pavements: a case study of Barranquilla city, Colombia. Case Stud. Constr. 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Mater. 411, 1–16 (2024). https://doi.org/10.1016/j.conbuildmat.2023.13 456414365143511049Computational modellingConcrete structuresConstruction materialsDeep neural networksMachine learningPortland cement concretePublicationORIGINALDevelopment of a Machine Learning (ML)-Based Computational Model.pdfDevelopment of a Machine Learning (ML)-Based Computational Model.pdfapplication/pdf2763682https://repositorio.cuc.edu.co/bitstreams/640afd64-f03c-4432-8110-0b128dee2e56/download79594e959ef1498796741ba97faf792aMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/8cf055cf-ed8d-4c7b-8f2c-67b2c2294da4/download73a5432e0b76442b22b026844140d683MD52TEXTDevelopment of a Machine Learning (ML)-Based Computational Model.pdf.txtDevelopment of a Machine Learning (ML)-Based Computational Model.pdf.txtExtracted texttext/plain59311https://repositorio.cuc.edu.co/bitstreams/9d705359-a8b8-47cf-941c-34b11dd4c562/downloadbcd06dbcaa28451dcd13a9358418e60eMD53THUMBNAILDevelopment of a Machine Learning (ML)-Based Computational Model.pdf.jpgDevelopment of a Machine Learning (ML)-Based Computational Model.pdf.jpgGenerated Thumbnailimage/jpeg15392https://repositorio.cuc.edu.co/bitstreams/7fab4be2-3787-4eed-8146-d6507fe868ea/downloadb32a28d234a79b8dbeaeb806a370cf9bMD5411323/13827oai:repositorio.cuc.edu.co:11323/138272024-11-26 04:00:33.545https://creativecommons.org/licenses/by/4.0/© The Author(s) 2024.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).</li>
      <li>Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.</li>
      <li>Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.</li>
          <li>Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
        </ol>
      </li>
      <li>Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.</li>
      <li>Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.</li>
      <li>Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.</li>
      <li>Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.</li>
      <li>Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.</li>
    </ol>
  </li>
  <br/>
</ol>
 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