Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils
The Resilient Modulus (Mr) is perhaps the most relevant and widely used parameter to characterise the soil behaviour under repetitive loading for pavement applications. Accordingly, it is a crucial parameter controlling the mechanistic-empirical pavement design. Nonetheless, determining the Mr by la...
- Autores:
-
Polo Mendoza, Rodrigo
Duque, Jose
Mašín, David
Turbay, Emilio
Acosta, Carlos
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/14276
- Acceso en línea:
- https://hdl.handle.net/11323/14276
https://repositorio.cuc.edu.co/
- Palabra clave:
- Deep neural networks
Resilient modulus
US soils
Statistical methods
- Rights
- closedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils |
| title |
Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils |
| spellingShingle |
Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils Deep neural networks Resilient modulus US soils Statistical methods |
| title_short |
Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils |
| title_full |
Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils |
| title_fullStr |
Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils |
| title_full_unstemmed |
Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils |
| title_sort |
Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils |
| dc.creator.fl_str_mv |
Polo Mendoza, Rodrigo Duque, Jose Mašín, David Turbay, Emilio Acosta, Carlos |
| dc.contributor.author.none.fl_str_mv |
Polo Mendoza, Rodrigo Duque, Jose Mašín, David Turbay, Emilio Acosta, Carlos |
| dc.subject.proposal.eng.fl_str_mv |
Deep neural networks Resilient modulus US soils Statistical methods |
| topic |
Deep neural networks Resilient modulus US soils Statistical methods |
| description |
The Resilient Modulus (Mr) is perhaps the most relevant and widely used parameter to characterise the soil behaviour under repetitive loading for pavement applications. Accordingly, it is a crucial parameter controlling the mechanistic-empirical pavement design. Nonetheless, determining the Mr by laboratory tests is not always possible due to the high consumption of time and financial resources. Thus, developing new indirect approaches for estimating the MR is necessary. Precisely, this article investigates the application of Deep Neural Networks (DNNs) and statistical methods to predict the Mr of soils. For that purpose, the Long-Term Pavement Performance (LTPP) database was implemented. It includes 64 701 datasets resulting from coarse-grained and fine-grained soil samples considering a wide range of grain size distribution and subjected to different stress levels. The input parameters were the bulk stress, octahedral shear stress, and the percentage of soil particles passing through the different sieves (3”, 2”, 3/2”, 1”, 3/4”, 1/2”, 3/8”, No. 4, No. 10, No. 40, No. 80, and No. 200) and the output was the Mr. The results suggest that while conventional mathematical models are unable to predict the influence of the grain size distribution and stress level on the Mr, the proposed DNNs were able to reproduce very accurate predictions. Notably, the proposed computational models have been uploaded to a GitHub repository and have become a valuable tool for forecasting the Mr when experimental measurements are not feasible |
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2023 |
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2023-09-20 |
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2025-06-04T21:46:40Z |
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2025-06-04T21:46:40Z |
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Polo-Mendoza, R., Duque, J., Mašín, D., Turbay, E., & Acosta, C. (2023). Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils. International Journal of Pavement Engineering, 24(1). https://doi.org/10.1080/10298436.2023.2257852 |
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1029-8436 |
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10298436.2023.2257852 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Polo-Mendoza, R., Duque, J., Mašín, D., Turbay, E., & Acosta, C. (2023). Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils. International Journal of Pavement Engineering, 24(1). https://doi.org/10.1080/10298436.2023.2257852 1029-8436 10298436.2023.2257852 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/14276 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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International Journal of Pavement Engineering |
| dc.relation.references.none.fl_str_mv |
AASHTO., 2021. T307-99: Standard Method of Test for Determining the Resilient Modulus of Soils and Aggregate Materials. In American Association of State Highway and Transportation Officials (pp. 1–41 Abbasi, A., et al., 2010. Detecting fake websites: the contribution of statistical learning theory. Management Information Systems (MIS) Quarterly, 34 (3), 435–461. doi:10.2307/25750686 Abdeljaber, O., et al., 2017. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154–170. doi:10.1016/j.jsv.2016.10.043 Aboelela, A. E., et al., 2022. Characterisation and modelling of subgrade resilient modulus for pavement structural design in Egypt. Road Materials and Pavement Design, 1–24. doi:10.1080/14680629.2022.2152725 Adem, K, 2022. Impact of activation functions and number of layers on detection of exudates using circular hough transform and convolutional neural networks. Expert Systems with Applications, 203 (117583), 1–7. doi:10.1016/j.eswa.2022.117583 Aitkenhead, M. J., and Coull, M. C, 2019. Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling. part 2: mapping of soil ecosystem services. Soil Use and Management, 35 (2), 217–231. doi:10.1111/sum.12491 Al-Dulaimi, Y. F., et al., 2022. Predicting resilient modulus of unbound granular base/subbase material. Mansoura Engineering Journal, 47 (2), 1–10. doi:10.21608/bfemu.2022.223409 AlAfandy, K. A., Omara, H., Lazaar, M., and Al Achhab, M. (2022). Chapter 5: machine learning. In: S. S. Kshatri, K. Thakur, M. H. M. Khan, D. Singh, and G. R. Sinha, eds. Computational intelligence and applications for pandemics and healthcare (pp. 83–113). Pennsylvania: IGI Global Albaeni, A., et al., 2017. Regional variation in outcomes of hospitalized patients having Out-of-hospital cardiac arrest. The American Journal of Cardiology, 120 (3), 421–427. doi:10.1016/j.amjcard.2017.04.045 Alnedawi, A., et al., 2022. Integrated and holistic knowledge map of resilient modulus studies for pavement materials: a scientometric analysis and bibliometric review of research frontiers and prospects. Transportation Geotechnics, 33 (100711), 1–22. doi:10.1016/j.trgeo.2021.100711 Alyasseri, Z. A. A., et al., 2021. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Systems, 39 (e12759), 1–32. doi:10.1111/exsy.12759 Apicella, A., et al., 2021. A survey on modern trainable activation functions. Neural Networks, 138, 14–32. doi:10.1016/j.neunet.2021.01.026 Arshad, M, 2019. Development of a correlation between the resilient modulus and CBR value for granular blends containing natural aggregates and RAP/RCA materials. Advances in Materials Science and Engineering, 2019), doi:10.1155/2019/8238904 Atreya, A. R., et al., 2022. Geographic variation and temporal trends in management and outcomes of cardiac arrest complicating acute myocardial infarction in the United States. Resuscitation, 170, 339–348. doi:10.1016/j.resuscitation.2021.11.002 Bahrami, M., et al., 2019. Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: measure MSEs between targets & ANN for Fe–CuO/Eg–water nanofluid. Physica A: Statistical Mechanics and its Applications, 519, 159–168. doi:10.1016/j.physa.2018.12.031 Banerjee, A., et al., 2020. Variation of resilient modulus of subgrade soils over a wide range of suction states. Journal of Geotechnical and Geoenvironmental Engineering, 146 (9), 1–18. doi:10.1061/(ASCE)GT.1943-5606.0002332 Bargagli Stoffi, F. J., Cevolani, G., and Gnecco, G., 2022. Simple models in complex worlds: occam’s razor and statistical learning theory. Minds and Machines, 32 (1), 13–42. doi:10.1007/s11023-022-09592-z Bastola, N. R., et al., 2022. Artificial neural network prediction model for in situ resilient modulus of subgrade soils for pavement design applications. Innovative Infrastructure Solutions, 7 (54), 1–12. doi:10.1007/s41062-021-00659-x Basu, S., et al., 2018. Deep neural networks for texture classification - A theoretical analysis. Neural Networks, 97, 173–182. doi:10.1016/j.neunet.2017.10.001 Bengio, Y., Courville, A., and Vincent, P, 2013. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (8), 1798–1828. doi:10.1109/TPAMI.2013.50 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbPolo Mendoza, RodrigoDuque, Josevirtual::1203-1Mašín, DavidTurbay, EmilioAcosta, Carlos2025-06-04T21:46:40Z2025-06-04T21:46:40Z2023-09-20Polo-Mendoza, R., Duque, J., Mašín, D., Turbay, E., & Acosta, C. (2023). Implementation of deep neural networks and statistical methods to predict the resilient modulus of soils. International Journal of Pavement Engineering, 24(1). https://doi.org/10.1080/10298436.2023.22578521029-8436https://hdl.handle.net/11323/1427610298436.2023.2257852Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The Resilient Modulus (Mr) is perhaps the most relevant and widely used parameter to characterise the soil behaviour under repetitive loading for pavement applications. Accordingly, it is a crucial parameter controlling the mechanistic-empirical pavement design. Nonetheless, determining the Mr by laboratory tests is not always possible due to the high consumption of time and financial resources. Thus, developing new indirect approaches for estimating the MR is necessary. Precisely, this article investigates the application of Deep Neural Networks (DNNs) and statistical methods to predict the Mr of soils. For that purpose, the Long-Term Pavement Performance (LTPP) database was implemented. It includes 64 701 datasets resulting from coarse-grained and fine-grained soil samples considering a wide range of grain size distribution and subjected to different stress levels. The input parameters were the bulk stress, octahedral shear stress, and the percentage of soil particles passing through the different sieves (3”, 2”, 3/2”, 1”, 3/4”, 1/2”, 3/8”, No. 4, No. 10, No. 40, No. 80, and No. 200) and the output was the Mr. The results suggest that while conventional mathematical models are unable to predict the influence of the grain size distribution and stress level on the Mr, the proposed DNNs were able to reproduce very accurate predictions. Notably, the proposed computational models have been uploaded to a GitHub repository and have become a valuable tool for forecasting the Mr when experimental measurements are not feasible2 páginasapplication/pdfengTaylor and Francis Ltd.United Kingdomhttps://www.tandfonline.com/doi/full/10.1080/10298436.2023.2257852Implementation of deep neural networks and statistical methods to predict the resilient modulus of soilsArtículo 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_970fb48d4fbd8a85International Journal of Pavement EngineeringAASHTO., 2021. T307-99: Standard Method of Test for Determining the Resilient Modulus of Soils and Aggregate Materials. In American Association of State Highway and Transportation Officials (pp. 1–41Abbasi, A., et al., 2010. Detecting fake websites: the contribution of statistical learning theory. Management Information Systems (MIS) Quarterly, 34 (3), 435–461. doi:10.2307/25750686Abdeljaber, O., et al., 2017. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 154–170. doi:10.1016/j.jsv.2016.10.043Aboelela, A. E., et al., 2022. Characterisation and modelling of subgrade resilient modulus for pavement structural design in Egypt. Road Materials and Pavement Design, 1–24. doi:10.1080/14680629.2022.2152725Adem, K, 2022. Impact of activation functions and number of layers on detection of exudates using circular hough transform and convolutional neural networks. Expert Systems with Applications, 203 (117583), 1–7. doi:10.1016/j.eswa.2022.117583Aitkenhead, M. J., and Coull, M. C, 2019. Digital mapping of soil ecosystem services in Scotland using neural networks and relationship modelling. part 2: mapping of soil ecosystem services. Soil Use and Management, 35 (2), 217–231. doi:10.1111/sum.12491Al-Dulaimi, Y. F., et al., 2022. Predicting resilient modulus of unbound granular base/subbase material. Mansoura Engineering Journal, 47 (2), 1–10. doi:10.21608/bfemu.2022.223409AlAfandy, K. A., Omara, H., Lazaar, M., and Al Achhab, M. (2022). Chapter 5: machine learning. In: S. S. Kshatri, K. Thakur, M. H. M. Khan, D. Singh, and G. R. Sinha, eds. Computational intelligence and applications for pandemics and healthcare (pp. 83–113). Pennsylvania: IGI GlobalAlbaeni, A., et al., 2017. Regional variation in outcomes of hospitalized patients having Out-of-hospital cardiac arrest. The American Journal of Cardiology, 120 (3), 421–427. doi:10.1016/j.amjcard.2017.04.045Alnedawi, A., et al., 2022. Integrated and holistic knowledge map of resilient modulus studies for pavement materials: a scientometric analysis and bibliometric review of research frontiers and prospects. Transportation Geotechnics, 33 (100711), 1–22. doi:10.1016/j.trgeo.2021.100711Alyasseri, Z. A. A., et al., 2021. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. Expert Systems, 39 (e12759), 1–32. doi:10.1111/exsy.12759Apicella, A., et al., 2021. A survey on modern trainable activation functions. Neural Networks, 138, 14–32. doi:10.1016/j.neunet.2021.01.026Arshad, M, 2019. Development of a correlation between the resilient modulus and CBR value for granular blends containing natural aggregates and RAP/RCA materials. Advances in Materials Science and Engineering, 2019), doi:10.1155/2019/8238904Atreya, A. R., et al., 2022. Geographic variation and temporal trends in management and outcomes of cardiac arrest complicating acute myocardial infarction in the United States. Resuscitation, 170, 339–348. doi:10.1016/j.resuscitation.2021.11.002Bahrami, M., et al., 2019. Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: measure MSEs between targets & ANN for Fe–CuO/Eg–water nanofluid. Physica A: Statistical Mechanics and its Applications, 519, 159–168. doi:10.1016/j.physa.2018.12.031Banerjee, A., et al., 2020. Variation of resilient modulus of subgrade soils over a wide range of suction states. Journal of Geotechnical and Geoenvironmental Engineering, 146 (9), 1–18. doi:10.1061/(ASCE)GT.1943-5606.0002332Bargagli Stoffi, F. J., Cevolani, G., and Gnecco, G., 2022. Simple models in complex worlds: occam’s razor and statistical learning theory. Minds and Machines, 32 (1), 13–42. doi:10.1007/s11023-022-09592-zBastola, N. R., et al., 2022. Artificial neural network prediction model for in situ resilient modulus of subgrade soils for pavement design applications. Innovative Infrastructure Solutions, 7 (54), 1–12. doi:10.1007/s41062-021-00659-xBasu, S., et al., 2018. Deep neural networks for texture classification - A theoretical analysis. Neural Networks, 97, 173–182. doi:10.1016/j.neunet.2017.10.001Bengio, Y., Courville, A., and Vincent, P, 2013. Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (8), 1798–1828. doi:10.1109/TPAMI.2013.50124Deep neural networksResilient modulusUS soilsStatistical methodsPublicationdf39f28b-a9f9-4156-b1f6-d8c1e7202032virtual::1203-1df39f28b-a9f9-4156-b1f6-d8c1e7202032virtual::1203-10000-0002-9663-1741virtual::1203-1LICENSElicense.txtlicense.txttext/plain; charset=utf-815543https://repositorio.cuc.edu.co/bitstreams/ea9907a7-6074-4da7-8530-a691edcab28c/download73a5432e0b76442b22b026844140d683MD51ORIGINALImplementation of deep neural networks and statistical methods to predict the resilient modulus of soils.pdfImplementation of deep neural networks and statistical methods to predict the resilient modulus of soils.pdfapplication/pdf405942https://repositorio.cuc.edu.co/bitstreams/ed3039dc-48dc-4909-802c-9b5c2ade9124/download5ba55c9c0f1cdb3b8a19c5751d67cc3dMD52TEXTImplementation of deep neural networks and statistical methods to predict the resilient modulus of 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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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