Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces

Autores:: Rodríguez-Calderón, Wilson
Pallares-Muñoz, Myriam-Rocío
Jurado-Cabañes, Carlos

Tipo de recurso:: Article of journal

Fecha de publicación:: 2024

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.en-US.fl_str_mv	Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces
title	Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces
spellingShingle	Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces Design of experiments Finite element Monte Carlo methods Probabilistic sensitivities Response surface method Structural reliability Confiabilidad estructural Diseño de experimentos Elementos finitos Método de superficie de respuesta Métodos Monte Carlo Sensibilidades probabilísticas
title_short	Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces
title_full	Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces
title_fullStr	Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces
title_full_unstemmed	Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces
title_sort	Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces
dc.creator.fl_str_mv	Rodríguez-Calderón, Wilson Pallares-Muñoz, Myriam-Rocío Jurado-Cabañes, Carlos
dc.contributor.author.eng.fl_str_mv	Rodríguez-Calderón, Wilson Pallares-Muñoz, Myriam-Rocío Jurado-Cabañes, Carlos
dc.subject.none.fl_str_mv	Design of experiments Finite element Monte Carlo methods Probabilistic sensitivities Response surface method Structural reliability Confiabilidad estructural Diseño de experimentos Elementos finitos Método de superficie de respuesta Métodos Monte Carlo Sensibilidades probabilísticas
topic	Design of experiments Finite element Monte Carlo methods Probabilistic sensitivities Response surface method Structural reliability Confiabilidad estructural Diseño de experimentos Elementos finitos Método de superficie de respuesta Métodos Monte Carlo Sensibilidades probabilísticas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-08-29 21:27:51 2026-04-16T17:47:33Z
dc.date.available.none.fl_str_mv	2024-08-29 21:27:51
dc.date.issued.none.fl_str_mv	2024-08-29
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.doi.none.fl_str_mv	10.19053/uptc.01211129.v33.n69.2024.17855
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dc.relation.references.none.fl_str_mv	A. Haldar, S. Mahadevan, Probability, Reliability, and Statistical Methods in Engineering Design. Wiley, 2000. G. A. Fenton, D. V. Griffiths, Risk Assessment in Geotechnical Engineering. 2008. K. K. Phoon, J. Ching, Risk and Reliability in Geotechnical Engineering. CRC Press, 2017. A. T. Beck, R. E. Melchers, Structural reliability analysis and prediction, Third edit. John Wiley & Sons Ltd, 2018. Z. Yang, J. Ching, “A novel simplified geotechnical reliability analysis method,” Appl. Math. Model., vol. 74, pp. 337-349, 2019. https://doi.org/10.1016/j.apm.2019.04.055 C. L. B. Yu, B. Ning, “Probabilistic durability assessment of concrete structures in marine environments: Reliability and sensitivity analysis,” China Ocean Eng., vol. 31, no. 1, pp. 63-73, 2017. https://doi.org/10.1007/s13344-017-0008-3 C. R. Ávila da Silva Júnior, P. D. Damazio, L. C. Matioli, J. L. Cavichiolo, “A counterexample to FORM and SORM,” Eng. Comput., vol. 37, no. 6, pp. 2127-2135, Jan. 2020. https://doi.org/10.1108/EC-06-2019-0286 R. Rackwitz, B. Flessler, “Structural reliability under combined random load sequences,” Comput. Struct., vol. 9, no. 5, e1978. https://doi.org/10.1016/0045-7949(78)90046-9 H. M. Gomes, “Técnicas de Avaliação da Confiabilidade em Estruturas de Concreto Armado Structural reliability analysis and prediction,” Doctoral dissertation, Universidade Federal do Rio Grande do Sul, 2001. W. Rodríguez, A. M. Awruch, J. L. Tamayo, H. M. Gomes, “Aplicação da análise da confiabilidade estrutural em treliças usando Ansys e ferramentas desenvolvidas em Matlab e Fortran [Paper presentation],” 2018. W. Rodríguez, “Análise de confiabilidade em problemas de interação solo-estaca incluindo campos estocásticos,” Doctoral dissertation, Universidade Federal do Rio Grande do Sul, 2020. W. Rodríguez, M. R. Pallares, “Comparação do Método Form e Monte Carlo Sequenciais e Paralelizados Numa Análise De Confiabilidade da Função Estado Limite Explícita (Viga em Plastificação) [Paper presentation],” 2021. S. Kaewunruen, C. Ngamkhanong, J. Jiang, “21 - Reliability quantification of the overhead line conductor,” in Rail Infrastructure Resilience, R. Calçada and S. Kaewunruen, Eds. Woodhead Publishing, 2022, pp. 441-462. P. Wang, L. Yang, N. Zhao, L. Li, D. Wang, “A New SORM Method for Structural Reliability with Hybrid Uncertain Variables,” Appl. Sci., vol. 11, no. 1, 2021. https://doi.org/10.3390/app11010346 H. A. Jensen, F. Mayorga, C. Papadimitriou, “Reliability sensitivity analysis of stochastic finite element models,” Comput. Methods Appl. Mech. Eng., vol. 296, 2015. https://doi.org/10.1016/j.cma.2015.08.007. A. Haldar, S. Mahadevan, Reliability Assessment Using Stochastic Finite Element Analysis. Wiley, 2000. J. Zhang, “Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey,” Wiley Interdiscip. Rev. Comput. Stat., 2020. https://doi.org/10.1002/wics.1539 F. Zhou, Y. Hou, H. Nie, “On High-Dimensional Time-Variant Reliability Analysis with the Maximum Entropy Principle,” Int. J. Aerosp. Eng., vol. 2022, e6612864, 2022. https://doi.org/10.1155/2022/6612864 N. Metropolis, S. Ulam, “The Monte Carlo Method,” J. Am. Stat. Assoc., vol. 44, no. 247, pp. 335-341, Sep. 1949. https://doi.org/10.1080/01621459.1949.10483310 H. M. Gomes, Apostila e slides da disciplina Confiabilidade em Sistemas Mecânicos. UFRGS, 2004. A. S. Nowak, K. R. Collins, Reliability of Structures, Second Edition. Taylor & Francis, 2012. W. Rodríguez, M. R. Pallares, J. A. Pulecio, “Rheological model of creep and relaxation in asphaltic mixtures using the transformed Carson Laplace, MAXIMA and Maple,” ARPN J. Eng. Appl. Sci., vol. 14, no. 2, 2019. W. Rodríguez, M. R. Pallares, J. A. Pulecio, “Análise de Sensibilidades Probabilísticas e Confiabilidade Estrutural em Treliças de Pontes Usando Ansys [Paper presentation],” 2021. A. Senova, A. Tobisova, R. Rozenberg, “New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method,” Sustainability, vol. 15, no. 2, 2023. https://doi.org/10.3390/su15021006 P. L. E. Bruno Tuffin, Monte Carlo and Quasi-Monte Carlo Methods - MCQMC 2018, Rennes, France, July 1–6, 1st ed. Springer International Publishing, 2020. Z. Zheng, H. Dai, M. Beer, “Efficient structural reliability analysis via a weak-intrusive stochastic finite element method,” Probabilistic Eng. Mech., vol. 71, 2023. https://doi.org/10.1016/j.probengmech.2023.103414 R. L. Iman, W. J. Conover, “Small sample sensitivity analysis techniques for computer models, with an application to risk assessment,” Commun. Stat. - Theory Methods, vol. 9, no. 17, 1980. https://doi.org/10.1080/03610928008827996 M. R. Abyani, M. Bahaari, “A Comparative Reliability Study of Corroded Pipelines Based on Monte Carlo Simulation and Latin Hypercube Sampling Methods,” Int. J. Press. Vessel. Pip., e104079, 2020. https://doi.org/10.1016/j.ijpvp.2020.104079 Z. Wang, “Comparative Study of Latin Hypercube Sampling and Monte Carlo Method in Structural Reliability Analysis,” Highlights Sci. Eng. Technol., vol. 28, pp. 61-69, 2022. https://doi.org/10.54097/hset.v28i.4061 Q. Gu, Y. Liu, Y. Li, C. Lin, “Finite element response sensitivity analysis of three-dimensional soil-foundation-structure interaction (SFSI) systems,” Earthq. Eng. Eng. Vib., vol. 17, no. 3, 2018. https://doi.org/10.1007/s11803-018-0462-9 R. Yang, W. Li, Y. Liu, “A novel response surface method for structural reliability,” AIP Adv., vol. 12, no. 1, p. 15205, Jan. 2022. https://doi.org/10.1063/5.0074702 B. Sudret, “Meta-models for structural reliability and uncertainty quantification,” arXiv Prepr. arXiv1203.2062, 2012. P. Liu, D. Shang, Q. Liu, Z. Yi, K. Wei, “Kriging Model for Reliability Analysis of the Offshore Steel Trestle Subjected to Wave and Current Loads,” J. Mar. Sci. Eng., vol. 10, no. 1, 2022. https://doi.org/10.3390/jmse10010025 J. E. Hurtado, Structural Reliability: Statistical Learning Perspectives. Springer Berlin Heidelberg, 2004. S. Saraygord, F. Enayatollahi, X. Xu, X. Liang, “Machine learning-based methods in structural reliability analysis: A review,” Reliab. Eng. Syst. Saf., vol. 219, e108223, 2022. https://doi.org/10.1016/j.ress.2021.108223 S. Reh, J.-D. Beley, S. Mukherjee, E. H. Khor, “Probabilistic finite element analysis using ANSYS,” Struct. Saf., vol. 28, no. 1, pp. 17-43, 2006. https://doi.org/10.1016/j.strusafe.2005.03.010. ANSYS, ANSYS Mechanical APDL Theory Reference. 2015. M. R. Pallares, W. Rodríguez, “Modelación de un sistema oscilante caótico empleando programas libres de Sistemas de Álgebra Computacional [Paper presentation],” 2010. https://www.iiis.org/cds2010/cd2010csc/cisci_2010/paperspdf/ca897gu.pdf Maxima, “Maxima, a Computer Algebra System. Version 5.47.0.”, 2023. https://maxima.sourceforge.io/ ANSYS, ANSYS Mechanical APDL Advanced Analysis Guide. 2015.
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dc.relation.citationedition.eng.fl_str_mv	Núm. 69 , Año 2024 : July-September 2024: Open Science at the Service of Engineering
dc.rights.none.fl_str_mv	Wilson Rodríguez-Calderón, Myriam-Rocío Pallares-Muñoz, Carlos Jurado-Cabañes - 2024
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spelling	Rodríguez-Calderón, WilsonPallares-Muñoz, Myriam-RocíoJurado-Cabañes, Carlos2024-08-29 21:27:512026-04-16T17:47:33Z2024-08-29 21:27:512024-08-290121-1129https://repositorio.uptc.edu.co/handle/001/2157910.19053/uptc.01211129.v33.n69.2024.178552357-5328https://doi.org/10.19053/uptc.01211129.v33.n69.2024.17855text/xmlapplication/pdfUniversidad Pedagógica y Tecnológica de ColombiaWilson Rodríguez-Calderón, Myriam-Rocío Pallares-Muñoz, Carlos Jurado-Cabañes - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/17855Design of experimentsFinite elementMonte Carlo methodsProbabilistic sensitivitiesResponse surface methodStructural reliabilityConfiabilidad estructuralDiseño de experimentosElementos finitosMétodo de superficie de respuestaMétodos Monte CarloSensibilidades probabilísticasAdvancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response SurfacesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85A. Haldar, S. Mahadevan, Probability, Reliability, and Statistical Methods in Engineering Design. Wiley, 2000.G. A. Fenton, D. V. Griffiths, Risk Assessment in Geotechnical Engineering. 2008.K. K. Phoon, J. Ching, Risk and Reliability in Geotechnical Engineering. CRC Press, 2017.A. T. Beck, R. E. Melchers, Structural reliability analysis and prediction, Third edit. John Wiley & Sons Ltd, 2018.Z. Yang, J. Ching, “A novel simplified geotechnical reliability analysis method,” Appl. Math. Model., vol. 74, pp. 337-349, 2019. https://doi.org/10.1016/j.apm.2019.04.055C. L. B. Yu, B. Ning, “Probabilistic durability assessment of concrete structures in marine environments: Reliability and sensitivity analysis,” China Ocean Eng., vol. 31, no. 1, pp. 63-73, 2017. https://doi.org/10.1007/s13344-017-0008-3C. R. Ávila da Silva Júnior, P. D. Damazio, L. C. Matioli, J. L. Cavichiolo, “A counterexample to FORM and SORM,” Eng. Comput., vol. 37, no. 6, pp. 2127-2135, Jan. 2020. https://doi.org/10.1108/EC-06-2019-0286R. Rackwitz, B. Flessler, “Structural reliability under combined random load sequences,” Comput. Struct., vol. 9, no. 5, e1978. https://doi.org/10.1016/0045-7949(78)90046-9H. M. Gomes, “Técnicas de Avaliação da Confiabilidade em Estruturas de Concreto Armado Structural reliability analysis and prediction,” Doctoral dissertation, Universidade Federal do Rio Grande do Sul, 2001.W. Rodríguez, A. M. Awruch, J. L. Tamayo, H. M. Gomes, “Aplicação da análise da confiabilidade estrutural em treliças usando Ansys e ferramentas desenvolvidas em Matlab e Fortran [Paper presentation],” 2018.W. Rodríguez, “Análise de confiabilidade em problemas de interação solo-estaca incluindo campos estocásticos,” Doctoral dissertation, Universidade Federal do Rio Grande do Sul, 2020.W. Rodríguez, M. R. Pallares, “Comparação do Método Form e Monte Carlo Sequenciais e Paralelizados Numa Análise De Confiabilidade da Função Estado Limite Explícita (Viga em Plastificação) [Paper presentation],” 2021.S. Kaewunruen, C. Ngamkhanong, J. Jiang, “21 - Reliability quantification of the overhead line conductor,” in Rail Infrastructure Resilience, R. Calçada and S. Kaewunruen, Eds. Woodhead Publishing, 2022, pp. 441-462.P. Wang, L. Yang, N. Zhao, L. Li, D. Wang, “A New SORM Method for Structural Reliability with Hybrid Uncertain Variables,” Appl. Sci., vol. 11, no. 1, 2021. https://doi.org/10.3390/app11010346H. A. Jensen, F. Mayorga, C. Papadimitriou, “Reliability sensitivity analysis of stochastic finite element models,” Comput. Methods Appl. Mech. Eng., vol. 296, 2015. https://doi.org/10.1016/j.cma.2015.08.007.A. Haldar, S. Mahadevan, Reliability Assessment Using Stochastic Finite Element Analysis. Wiley, 2000.J. Zhang, “Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey,” Wiley Interdiscip. Rev. Comput. Stat., 2020. https://doi.org/10.1002/wics.1539F. Zhou, Y. Hou, H. Nie, “On High-Dimensional Time-Variant Reliability Analysis with the Maximum Entropy Principle,” Int. J. Aerosp. Eng., vol. 2022, e6612864, 2022. https://doi.org/10.1155/2022/6612864N. Metropolis, S. Ulam, “The Monte Carlo Method,” J. Am. Stat. Assoc., vol. 44, no. 247, pp. 335-341, Sep. 1949. https://doi.org/10.1080/01621459.1949.10483310H. M. Gomes, Apostila e slides da disciplina Confiabilidade em Sistemas Mecânicos. UFRGS, 2004.A. S. Nowak, K. R. Collins, Reliability of Structures, Second Edition. Taylor & Francis, 2012.W. Rodríguez, M. R. Pallares, J. A. Pulecio, “Rheological model of creep and relaxation in asphaltic mixtures using the transformed Carson Laplace, MAXIMA and Maple,” ARPN J. Eng. Appl. Sci., vol. 14, no. 2, 2019.W. Rodríguez, M. R. Pallares, J. A. Pulecio, “Análise de Sensibilidades Probabilísticas e Confiabilidade Estrutural em Treliças de Pontes Usando Ansys [Paper presentation],” 2021.A. Senova, A. Tobisova, R. Rozenberg, “New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method,” Sustainability, vol. 15, no. 2, 2023. https://doi.org/10.3390/su15021006P. L. E. Bruno Tuffin, Monte Carlo and Quasi-Monte Carlo Methods - MCQMC 2018, Rennes, France, July 1–6, 1st ed. Springer International Publishing, 2020.Z. Zheng, H. Dai, M. Beer, “Efficient structural reliability analysis via a weak-intrusive stochastic finite element method,” Probabilistic Eng. Mech., vol. 71, 2023. https://doi.org/10.1016/j.probengmech.2023.103414R. L. Iman, W. J. Conover, “Small sample sensitivity analysis techniques for computer models, with an application to risk assessment,” Commun. Stat. - Theory Methods, vol. 9, no. 17, 1980. https://doi.org/10.1080/03610928008827996M. R. Abyani, M. Bahaari, “A Comparative Reliability Study of Corroded Pipelines Based on Monte Carlo Simulation and Latin Hypercube Sampling Methods,” Int. J. Press. Vessel. Pip., e104079, 2020. https://doi.org/10.1016/j.ijpvp.2020.104079Z. Wang, “Comparative Study of Latin Hypercube Sampling and Monte Carlo Method in Structural Reliability Analysis,” Highlights Sci. Eng. Technol., vol. 28, pp. 61-69, 2022. https://doi.org/10.54097/hset.v28i.4061Q. Gu, Y. Liu, Y. Li, C. Lin, “Finite element response sensitivity analysis of three-dimensional soil-foundation-structure interaction (SFSI) systems,” Earthq. Eng. Eng. Vib., vol. 17, no. 3, 2018. https://doi.org/10.1007/s11803-018-0462-9R. Yang, W. Li, Y. Liu, “A novel response surface method for structural reliability,” AIP Adv., vol. 12, no. 1, p. 15205, Jan. 2022. https://doi.org/10.1063/5.0074702B. Sudret, “Meta-models for structural reliability and uncertainty quantification,” arXiv Prepr. arXiv1203.2062, 2012.P. Liu, D. Shang, Q. Liu, Z. Yi, K. Wei, “Kriging Model for Reliability Analysis of the Offshore Steel Trestle Subjected to Wave and Current Loads,” J. Mar. Sci. Eng., vol. 10, no. 1, 2022. https://doi.org/10.3390/jmse10010025J. E. Hurtado, Structural Reliability: Statistical Learning Perspectives. Springer Berlin Heidelberg, 2004.S. Saraygord, F. Enayatollahi, X. Xu, X. Liang, “Machine learning-based methods in structural reliability analysis: A review,” Reliab. Eng. Syst. Saf., vol. 219, e108223, 2022. https://doi.org/10.1016/j.ress.2021.108223S. Reh, J.-D. Beley, S. Mukherjee, E. H. Khor, “Probabilistic finite element analysis using ANSYS,” Struct. Saf., vol. 28, no. 1, pp. 17-43, 2006. https://doi.org/10.1016/j.strusafe.2005.03.010.ANSYS, ANSYS Mechanical APDL Theory Reference. 2015.M. R. Pallares, W. Rodríguez, “Modelación de un sistema oscilante caótico empleando programas libres de Sistemas de Álgebra Computacional [Paper presentation],” 2010. https://www.iiis.org/cds2010/cd2010csc/cisci_2010/paperspdf/ca897gu.pdfMaxima, “Maxima, a Computer Algebra System. Version 5.47.0.”, 2023. https://maxima.sourceforge.io/ANSYS, ANSYS Mechanical APDL Advanced Analysis Guide. 2015.e1785569e1785533Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/17855/14940https://revistas.uptc.edu.co/index.php/ingenieria/article/download/17855/14449Núm. 69 , Año 2024 : July-September 2024: Open Science at the Service of EngineeringPublicationOREORE.xmltext/xml2775https://repositorio.uptc.edu.co/bitstreams/6c8f4705-3a16-445a-8ede-027297f5bd1a/download35659bbf885c309e74c02cb670daa771MD51falseAnonymousREAD001/21579oai:repositorio.uptc.edu.co:001/215792026-10-29 14:05:30.797https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Advancing Probabilistic Frame Analysis: A Comprehensive Approach Using Monte Carlo Simulation and Response Surfaces

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