Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
This article compares two optimization methods considering random variations in design parameters. One is reliability-based design optimization, which depends on the availability of the joint probability density function. A more practical alternative is robust optimization, which does not require the estimation of failure probability. It accounts for the random response of the structure through definitions of objective functions and constraints, incorporating mean values and response variances. An important element of the algorithm involves approximating unknown responses of the structures and employing efficient statistical moment estimation methods. The kriging method was used in this paper. Additionally, the article evaluates two experimental plan techniques: the classical random sampling plan and the OLH plan.
Czasopismo
Rocznik
Tom
Strony
377--388
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
- Kielce University of Technology, Faculty of Civil Engineering and Architecture, Kielce, Poland
autor
- Kielce University of Technology, Faculty of Civil Engineering and Architecture, Kielce, Poland
Bibliografia
- 1. Aoues Y., Chateauneuf A., 2010, Benchmark study of numerical methods for reliability-based design optimization, Structural and Multidisciplinary Optimization, 41, 2, 277-294.
- 2. Beck A.T., Gomes W.J.S., Lopez R.H., Miguel L.F.F., 2015, A comparison between robust and risk-based optimization under uncertainty, Structural and Multidisciplinary Optimization, 52, 3, 479-492.
- 3. Chen W., Fu W., Biggers S.B., Latour R.A., 2000, An affordable approach for robust design of thick laminated composite structure, Optimization and Engineering, 1, 3, 305-322.
- 4. Doltsinis I., Kang Z., Cheng G., 2005, Robust design of non-linear structures using optimization methods, Computer Methods Applied Mechanics and Engineering, 194, 12-16, 1179–1795.
- 5. Dudzik A., Potrzeszcz-Sut B., 2021, Hybrid approach to the first order reliability method in the reliability analysis of a spatial structure, Applied Sciences, 11, 2, 648.
- 6. Hwang K.-H., Lee K.-W., Park G.-J., 2001, Robust optimization of an automobile rearview mirror for vibration reduction, Structural and Multidisciplinary Optimization, 21, 4, 300-308.
- 7. Kubicka K., Radoń U., 2018, Influence of randomness of buckling coefficient on the reliability index’s value under fire conditions, Archives of Civil Engineering, 64, 3, 173-179.
- 8. Kuschel N., Rackwitz R., 1997, Two basic problems in reliability-based structural optimization, Mathematical Methods of Operational Research, 46, 3, 309-333.
- 9. Li Y.Q., Cui Z.S., Ruan X.Y., Zhang D.J., 2006, CAE-based six sigma robust optimization for deep-drawing process of sheet metal, The International Journal of Advanced Manufacturing Technology, 30, 631-637.
- 10. Liefvendahl M., Stocki R., 2006, A study on algorithms for optimization of Latin hypercubes, Journal of Statistical Planning and Inference, 136, 9, 3231-3247.
- 11. Lopez R.H., Beck A.T., 2012, Reliability-based design optimization strategies based on FORM: a review, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 34, 4, 506-514.
- 12. Mochocki W., Obara P., Radoń U., 2020, Impact of the wind load probability distribution and connection types on the reliability index of truss towers, Journal of Theoretical and Applied Mechanics, 58, 2, 403-414.
- 13. Mochocki W., Radoń U., 2019, Analysis of basic failure scenarios of a truss tower in a probabilistic approach, Applied Sciences, 9, 13, 1-17.
- 14. Numpress computer system, http://www.numpress.ippt.pan.pl/ [Accessed: 27.09.2023].
- 15. Radoń U., Szaniec W., Zabojszcza P., 2021, Probabilistic approach to limit states of a steel dome, Materials, 14, 19, 5528.
- 16. Sbaraglia F., Farokhi H., Aliabadi F.M.H., 2018, Robust and reliability-based design optimization of a composite floor beam, Key Engineering Materials, 774, 486-491.
- 17. Schittkowski K., Zillober C., Zotemantel R., 1994, Numerical comparison of nonlinear programming algorithms for structural optimization, Structural Optimization, 7, 1-19.
- 18. Simpson T.W., Mauery T.M., Korte J.J., Mistree F., 2001, Kriging models for global approximation in simulation-based multidisciplinary design optimization, AIAA Journal, 39, 12, 2233-2241.
- 19. Stocki R., 2010, Reliability analysis and resistance optimization of complex structures and technological processes (in Polish), PRACE IPPT, 2.
- 20. Streicher H., Rackwitz R., 2002, Structural optimization – a one level approach, [In:] AMAS Workshop on Reliability-Based Design and Optimization – RBO’02, Jendo S., Doliński K., Kleiber M., (Eds.).
- 21. Strurel computer system http://www.Strurel.de [Accessed: 27.09.2023].
- 22. Youn B. D., Choi K. K., 2004, A new response surface methodology for reliability-based design optimization, Computers and Structures, 82, 2-3, 241-256.
- 23. Zabojszcza P., Radoń U., 2019, The impact of node location imperfections on the reliability of single-layer steel domes, Applied Sciences, 9, 2742.
- 24. Zabojszcza P., Radoń U., 2020, Stability analysis of the single-layer dome in probabilistic description by the Monte Carlo method, Journal of Theoretical and Applied Mechanics, 58, 2, 425-436.
- 25. Zabojszcza P., Radoń U., 2022, Optimization of steel roof framing taking into account the random nature of design parameters, Materials, 15, 14, 5017.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f01c2f8f-5127-40b9-b186-e3851060ca40
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