AbstractAbout AuthorsReferences
The reliability of building structures is one of the key indicators of mechanical safety. Modern problems of assessing the reliability index of building structures related to the analysis of statistical data and the construction of mathematical models of limit states are considered. The use of p-boxes as promising and effective models of random variables making it possible to describe more validly probability distribution functions is demonstrated. The numerical example of the reliability index evaluation reflects the fact that even samples based on a large volume of experimental data require the use of the provisions of interval analysis, fuzzy analysis and other modern theories of data analysis. A numerical example shows the problem of invariance of mathematical models of limit states for probabilistic reliability analysis, as a result of which different forms of one mathematical model lead to different estimates of the reliability index. The development of numerical methods for calculating building structures and the increase in computing power does not make it possible to increase the reliability of probabilistic assessment of the reliability of building structures based on classical probabilistic and statistical methods. There is a need to develop reliability analysis methods based on modern methods and computational algorithms. Perspective directions for the development of methods for analyzing the reliability of building structures are noted.
S.A. SOLOVIEV1, Candidate of Sciences (Engineering),
A.A. SOLOVIEVA1, Postgraduate (This email address is being protected from spambots. You need JavaScript enabled to view it.),
N.P. UMNYAKOVA2,3, Doctor of Sciences (Engineering) (This email address is being protected from spambots. You need JavaScript enabled to view it.),
A.A. KOCHKIN1, Doctor of Sciences (Engineering) (This email address is being protected from spambots. You need JavaScript enabled to view it.)
A.A. SOLOVIEVA1, Postgraduate (This email address is being protected from spambots. You need JavaScript enabled to view it.),
N.P. UMNYAKOVA2,3, Doctor of Sciences (Engineering) (This email address is being protected from spambots. You need JavaScript enabled to view it.),
A.A. KOCHKIN1, Doctor of Sciences (Engineering) (This email address is being protected from spambots. You need JavaScript enabled to view it.)
1 Vologda State University (15, Lenina Street, 160000, Vologda, Russian Federation)
2 Research Institute of Building Physics, Russian Academy of Architecture and Construction Sciences, (21, Lokomotivny Driveway, Moscow, 127238, Russian Federation)
3 National Research Moscow State University of Civil Engineering (26, Yaroslavskoye Highway, Moscow, 129337, Russian Federation)
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2. Faes M. G., Daub M., Marelli S., Patelli E., Beer M. Engineering analysis with probability boxes: a review on computational methods. Structural Safety. 2021. Vol. 93, pp. 102092.
3. Тamrazyan A.G. Design of Structural Elements in the Event of the PreSet Reliability, Regular Load and Bearing Capacity Distribution. Vestnik MGSU. 2012. No. 10, pp. 109–115. (In Russian).
4. Utkin V.S., Solovev S.A., Yarigina O.V. Structural elements design on reliability level in case limited statistical data. Stroitelstvo I Reconstructiya. 2020. No. 1, pp. 81–91. (In Russian).
5. Kabir S., Papadopoulos Y. A review of applications of fuzzy sets to safety and reliability engineering. International Journal of Approximate Reasoning. 2018. Vol. 100, pp. 29–55.
6. Soloveva A.A., Solovev S.A. A research into the development of models of random variables as part of the structural reliability analysis performed in the absence of some statistical information. Vestnik MGSU. 2021. Vol. 16 (5), pp. 587–607. (In Russian).
7. Cao L., Liu J., Han X., Jiang C., Liu Q. An efficient evidence-based reliability analysis method via piecewise hyperplane approximation of limit state function. Structural and Multidisciplinary Optimization. 2018. Vol. 58 (1), pp. 201–213.
8. Jiang S. H., Papaioannou I., Straub D. Bayesian updating of slope reliability in spatially variable soils with in-situ measurements. Engineering Geology. 2018. Vol. 239, pp. 310–320.
9. Hasofer A.M., Lind N.C. Exact and Invariant Second-Moment Code Format. Journal of the Engineering Mechanics Division. 1974. Vol. 100. No. 1, pp. 111–121.
10. Choi S. K., Grandhi R., Canfield R.A. Reliability-based structural design. London: Springer-Verlag London, 2007. 306 p.
11. Tvedt L. Distribution of quadratic forms in normal space-application to structural reliability. Journal of engineering mechanics. 1990. Vol. 116. No. 6, pp. 1183–1197.
12. Glukhov D.O., Bogush R.P., Lazovsky Е.D., Glukhova T.M. Probability analysis of the structural reinforced concrete element. Vestnik Polotskogo gosudarstvennogo universiteta. Seriya F. Stroitel’stvo. Prikladnye nauki 2017. No. 16, pp. 67–76. (In Russian).
13. Keshtegar B., Meng Z. A hybrid relaxed first-order reliability method for efficient structural reliability analysis. Structural Safety. 2017. Vol. 66, pp. 84–93.
14. Keshtegar B., Chakraborty S. A hybrid self-adaptive conjugate first order reliability method for robust structural reliability analysis. Applied Mathematical Modelling. 2018. Vol. 53, pp. 319–332.
15. Liu X., Kuang Z., Yin L., Hu L. Structural reliability analysis based on probability and probability box hybrid model. Structural Safety. 2017. No. 68, pp. 73–84.
16. Simon C., Bicking F. Hybrid computation of uncertainty in reliability analysis with p-box and evidential networks. Reliability Engineering & System Safety. 2017. Vol. 167, pp. 629–638
17. Xu L., G. Cheng. Discussion on: moment methods for structural reliability. Structural Safety. 2003. Vol. 25. No. 2, pp. 193–199.
18. Adishchev V.V., Shmakov D.S. The method of constructing the membership function with «direct» processing of the initial data. Proceedings of NGASU. 2013. Vol. 16. No. 2, pp. 45–66. (In Russian).
19. Fang Y., Tee K.F. Structural reliability analysis using response surface method with improved genetic algorithm. Structural Engineering and Mechanics. 2017. Vol. 62. No. 2, pp. 139–142.
20. Linxiong H., Huacong L., Kai P., Hongliang X. A novel kriging based active learning method for structural reliability analysis. Journal of Mechanical Science and Technology. 2020. Vol. 34 (4), pp. 1545–1556.
21. Afshari S.S., Enayatollahi F., Xu X., Liang X. Machine learning-based methods in structural reliability analysis: A review. Reliability Engineering & System Safety. 2022. Vol. 219, pp. 108223.
For citation: Soloviev S.A., Solovieva A.A., Umnyakova N.P., Kochkin A.A. Analysis of the problems of assessing the reliability index of elements of building structures. Zhilishchnoe Stroitel’stvo [Housing Construction]. 2022. No. 7, pp. 32–39. (In Russian). DOI: https://doi.org/10.31659/0044-4472-2022-7-32-39