Ir al menú de navegación principal Ir al contenido principal Ir al pie de página del sitio

Avanzando en el análisis probabilístico de marcos: un enfoque integral mediante simulaciones Monte Carlo y superficies de respuesta

Resumen

Se realizó un análisis probabilístico de elementos finitos en ANSYS que amplía el enfoque del pórtico canónico de Haldar y Mahadevan. El modelo se parametriza con once variables aleatorias que incluyen las cargas impuestas, las propiedades geométricas de las secciones, las longitudes de los miembros, el módulo elástico y el desplazamiento horizontal límite. El modelo incorpora un análisis de las sensibilidades probabilísticas de las variables y la evaluación de la probabilidad de falla de la estructura. El criterio de falla se introduce a través de una función de estado límite de desplazamiento horizontal. Los resultados de la confiabilidad estructural del pórtico utilizando los métodos de Monte Carlo (MC), Monte Carlo con Muestreo por Hipercubo Latino (MCLH) y Superficie de Respuesta con polinomios lineales (RSM-LIN) y cuadráticos (RSM-QUAX) son contundentes y se destacan los bajos costos computacionales logrados con los métodos RSM. Finalmente, se pronostican aplicaciones futuras en pórticos que incluyan no linealidad geométrica y de materiales, carga dinámica y otros escenarios de carga extrema.

Palabras clave

confiabilidad estructural, diseño de experimentos, elementos finitos, método de superficie de respuesta, métodos Monte Carlo, sensibilidades probabilísticas

PDF (English)

Citas

  1. A. Haldar, S. Mahadevan, Probability, Reliability, and Statistical Methods in Engineering Design. Wiley, 2000.
  2. G. A. Fenton, D. V. Griffiths, Risk Assessment in Geotechnical Engineering. 2008.
  3. K. K. Phoon, J. Ching, Risk and Reliability in Geotechnical Engineering. CRC Press, 2017.
  4. A. T. Beck, R. E. Melchers, Structural reliability analysis and prediction, Third edit. John Wiley & Sons Ltd, 2018.
  5. 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
  6. 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
  7. 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
  8. 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
  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.
  10. 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.
  11. W. Rodríguez, “Análise de confiabilidade em problemas de interação solo-estaca incluindo campos estocásticos,” Doctoral dissertation, Universidade Federal do Rio Grande do Sul, 2020.
  12. 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.
  13. 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.
  14. 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
  15. 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.
  16. A. Haldar, S. Mahadevan, Reliability Assessment Using Stochastic Finite Element Analysis. Wiley, 2000.
  17. 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
  18. 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
  19. 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
  20. H. M. Gomes, Apostila e slides da disciplina Confiabilidade em Sistemas Mecânicos. UFRGS, 2004.
  21. A. S. Nowak, K. R. Collins, Reliability of Structures, Second Edition. Taylor & Francis, 2012.
  22. 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.
  23. W. Rodríguez, M. R. Pallares, J. A. Pulecio, “Análise de Sensibilidades Probabilísticas e Confiabilidade Estrutural em Treliças de Pontes Usando Ansys [Paper presentation],” 2021.
  24. 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
  25. 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.
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. B. Sudret, “Meta-models for structural reliability and uncertainty quantification,” arXiv Prepr. arXiv1203.2062, 2012.
  33. 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
  34. J. E. Hurtado, Structural Reliability: Statistical Learning Perspectives. Springer Berlin Heidelberg, 2004.
  35. 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
  36. 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.
  37. ANSYS, ANSYS Mechanical APDL Theory Reference. 2015.
  38. 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
  39. Maxima, “Maxima, a Computer Algebra System. Version 5.47.0.”, 2023. https://maxima.sourceforge.io/
  40. ANSYS, ANSYS Mechanical APDL Advanced Analysis Guide. 2015.

Descargas

Los datos de descargas todavía no están disponibles.

Artículos similares

<< < 1 2 

También puede {advancedSearchLink} para este artículo.