The reliability index estimation of truss bars with interval uncertainty of statistical data
The reliability index estimation of truss bars with interval uncertainty of statistical data

The reliability index estimation of truss bars with interval uncertainty of statistical data

DOI: 10.37153/2618-9283-2023-4-30-44

Authors:  

Alexander E. Inkov
post-graduate student, lecturer of industrial and civil engineering department, Vologda State University, Russia

Anastiasia A. Soloveva

post-graduate student, lecturer of industrial and civil engineering department, Vologda State University, Russia


Sergey A. Solovev
Cand. Sci. (Engineering), associate professor of industrial and civil engineering department, Vologda State University, Russia


Rubric:     Theoretical and experimental studies   
Key words: reliability, uncertainty, truss, reliability index, safety, interval, non-probabilistic approach, risk
Annotation:

The article presents an approach to evaluation the reliability index of steel truss bars with the uncertainty of random variables expressed in the presence of information only about the bounds of variability. Different methods of estimating the bounds of variability for random variables are presented. The new approach is also developed using the provisions of the theory of possibility and the Dvoretzky–Kiefer–Wolfowitz inequality (DKW). The reliability index allows to compare various design solutions by the safety criterion, identify structural elements with the highest failure probability for monitoring the technical state and to quantify the increase in the safety level with strengthening of structural elements. The Monte Carlo statistical simulation data reflect the analogy of the non-probabilistic reliability index in the considered approach with the non-failure probability of the truss bar.

Used Books:

1. Mkrtychev O.V., Shchedrin O.S., Lokhova E.M. Determination of individual coefficients on the basis of probabilistic analysis. Vestnik MGSU [Monthly Journal on Construction and Architecture]. 2022, vol. 17, no. 10, pp. 1331–1346. DOI:10.22227/1997-0935.2022.10.1331-1346 [In Russian].

2. Adishchev V.V., Shmakov D.S. Method of constructing the membership function with "direct" processing of initial data. Proceedings of the Novosibirsk State University of Architecture and Civil Engineering (Sibstrin). 2013, vol. 16, no. 2, pp. 45–66. [In Russian].

3. Jiang C., Zheng J., Han X. Probability-interval hybrid uncertainty analysis for structures with both aleatory and epistemic uncertainties: a review. Structural and Multidisciplinary Optimization. 2018, vol. 57, no. 6, pp. 2485–2502.

4. Ben-Haim Y., Elishakoff I. Convex models of uncertainty in applied mechanics. Amsterdam, Oxford, New York, Tokyo: Elsevier, 1990. 240 p.

5. Elishakoff I., Daphnis A. Simple application of interval analyses to structural safety: standard versus parameterised versions. International Journal of Sustainable Materials and Structural Systems. 2018, vol. 3, no. 3–4, pp. 203–217.

6. Wang R., Wang X., Wang L., Chen X. Efficient computational method for the non-probabilistic reliability of linear structural systems. Acta Mechanica Solida Sinica. 2016, vol. 29, no. 3, pp. 284–299.

7. Tao J., Jian-Jun C., Ya-Lan X. A semi-analytic method for calculating non-probabilistic reliability index based on interval models. Applied Mathematical Modelling. 2007, vol. 31, no. 7, pp. 1362–1370.

8. Guo S.X., Lu Z.Z. A non-probabilistic robust reliability method for analysis and design optimization of structures with uncertain-but-bounded parameters. Applied Mathematical Modelling. 2015, vol. 39, no. 7, pp. 1985–2002.

9. Kang Z., Luo Y. Non-probabilistic reliability-based topology optimization of geometrically nonlinear structures using convex models. Computer Methods in Applied Mechanics and Engineering. 2009, vol. 198, no. 41–44, pp. 3228–3238.

10. Duncan J.M. Factors of safety and reliability in geotechnical engineering. Journal of Geotechnical Engineering. 2000, vol. 126, no. 4, pp. 307–316.

11. Utkin V.S., Utkin L.V. Raschet nadezhnosti stroitel'nyh konstrukcij pri razlichnyh sposobah opisaniya nepolnoty informacii [Structural reliability analysis with different approaches to describing the incompleteness of data]. Vologda: VoGTU, 2009. 126 p. (In Russian)

12. Dvoretzky A., Kiefer J., Wolfowitz J. Asymptotic minimax character of the sample distribution function and of the classical multinomial estimator. The Annals of Mathematical Statistics. 1956, no. 27(3), pp. 642–669. DOI:10.1214/aoms/1177728174

13. Pradlwarter H.J., Schuëller G.I. The use of kernel densities and confidence intervals to cope with insufficient data in validation experiments. Computer Methods in Applied Mechanics and Engineering. 2008, vol. 197, no. 29–32, pp. 2550–2560.

14. Sharyj S.P. Konechnomernyj interval'nyj analiz [Finite-dimensional interval analysis]. Novosibirsk: Izdatel'stvo XYZ, 2022. 654 p. [In Russian]

15. Elishakoff I. Safety Factors and Reliability: Friends or Foes? Berlin: Springer Netherlands, 2004. 296 p. DOI: 10.1007/978-1-4020-2131-2

16. Guo S.X. A non-probabilistic model of structural reliability based on interval analysis. Chinese Journal of Computational Mechanics. 2001, vol. 18, no. 1, pp. 56–60.

17. Wang X.J., Qiu Z.P., Elishakoff I. Non-probabilistic set-theoretic model for structural safety measure. Acta Mechanica. 2008, vol. 198, no. 1, pp. 51–64.

18. Soloveva A.A., Solovev S.A. Reliability analysis of planar steel trusses based on p-box models. Vestnik MGSU [Monthly Journal on Construction and Architecture]. 2021, vol. 16, no. 2, pp. 153–167. DOI: 10.22227/1997-0935.2021.2.153-167 [In Russian].

19. Karpov D.F. The algorithm of complex diagnostics of technical condition of building structures on thermograms analysis. Stroitel'nye materialy i izdeliya [Construction Materials and Products]. 2019, vol. 2, no. 2, pp. 23–28. DOI: 10.34031/2618-7183-2019-2-2-23-28 [In Russian)]

20. Jiang C., Ni B.Y., Han X., Tao Y.R. Non-probabilistic convex model process: a new method of time-variant uncertainty analysis and its application to structural dynamic reliability problems. Computer Methods in Applied Mechanics and Engineering. 2014, vol. 268, pp. 656–676.

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