Monitoring of Bridge Malfunctions Based on Interval Estimates of the Variance of the Noise of Vibration Noisy Signals

The specific features of bridges are considered. It is noted that in the process of operation bridges are exposed to the influence of external factors, such as temperature changes, wind gusts, ice, heavy rain, seismic and landslide impacts, etc., which cause damage, corrosion, malfunction, etc. in bridge structures. Therefore, systems for monitoring the technical condition of bridges are developed. However, these monitoring systems do not allow to reveal the latent period of occurrence of a malfunction in the bridge structure and trace the dynamics of its development. It is noted that the formation of even the most insignificant faults in the bridge structures is accompanied by the emergence in the vibration signal of an additive noise correlated with the useful signal. Characteristics of this noise carry information about the formation of defects and malfunctions in the bridge structures. However, the noise cannot be isolated from the noisy vibration signal in order to calculate estimates of these characteristics using traditional methods. Therefore, algorithms for calculating the characteristics of the noise characteristics of the noisy vibration signal are developed. Technologies for early detection of bridge malfunctions are proposed, as well as analysis of the dynamics of their development using interval estimates of the noise variance. The matrix and model of the faulty technical condition of bridges are built. To control the dynamics of malfunction development, confidence intervals for the estimates of the variance of the noise of noisy vibration signal at different time instants are compiled. The correspondence between each value of the confidence interval of the variance of the noise of the noisy vibration signal and the degree of development of the malfunction or defect of the bridge structure is established. For each upper boundary of the confidence interval of the noise variance, the ratio of the noise variance to the variance of the useful signal is calculated, and the dynamics of the hazard degree development is determined. It is shown that using the developed algorithms and technologies in monitoring systems will make it possible to signal the emergence of malfunctions and defects in the latent period of their inception, which ensures safety, reliability and efficiency of the operation of bridge structures and reduces the risk of possible accidents.

1. Bętkowski P. Steel Relieving Constructions in Mining Areas as Temporary or Permanent Railway Bridges, Transport Problems, 2020, vol. 15, iss. 2, pp. 107—118, DOI: 10.21307/tp-2020-024.

2. Salem O., Salman B., Ghorai S. Accelerating construction of roadway bridges using alternative techniques and procurement methods, Transport, 2018, vol. 33, iss. 2, pp. 567—579, DOI: 10.3846/16484142.2017.1300942.

3. O’Brien E. J., Leahy C., Enright B., Caprani C. C. Validation of scenario modelling for bridge loading, The Baltic Journal of Road and Bridge Engineering, 2016, vol. 11, iss. 3, pp. 233—241, DOI: 10.3846/bjrbe.2016.27.hdl:10197/9252.

4. Arafa A., Ahmed N., Farghaly A. S., Chaallal O., Benmokrane B. Exploratory Study on Incorporating Glass FRP Reinforcement to Control Damage in Steel-Reinforced Concrete Bridge Pier Walls, Journal of Bridge Engineering, 2021, vol. 26, iss. 2, DOI: 10.1061/(ASCE)BE.1943-5592.0001648.

5. Kamaitis Z. Damage to concrete bridges due to reinforcement corrosion: Part I. Site investigations, Transport, 2002, vol. 17, iss. 4, pp. 137—142, DOI: 10.3846/16483840.2002.10414030.

6. Sužiedelytė-Visockienė J. Technique for definition of break size of road covering on the bridge, Transport, 2007, vol. 22, iss. 2, pp. 122—125, DOI: 10.3846/16484142.2007.9638110.

7. Daocheng Z., Bo Z., Sisi X. et al. Experimental study on dynamic effect of freestanding tower of sea-crossing bridge under wave load, Advances in Bridge Engineering, 2021, vol. 2, iss. 9, pp. 1—11, DOI: 10.1186/s43251-020-00032-5.

8. Jia H., Jia K., Sun C. et al. Preliminary numerical study on seismic response of ordinary long-span suspension bridges crossing active faults, Advances in Bridge Engineering, 2021, vol. 2, iss. 16, pp. 1—11, DOI: 10.1186/s43251-021-00035-w.

9. Mao J., Wang H., Xu Y. et al. Deformation monitoring and analysis of a long-span cable-stayed bridge during strong typhoons, Advances in Bridge Engineering, 2020, vol.1, iss. 8, pp. 1—19, DOI: 10.1186/s43251-020-00008-5.

10. Yadav D., Barai S. V. Fuzzy inference driven internet based bridge management system. Transport, 2005, vol. 20, iss. 1, pp. 37—44, DOI: 10.3846/16484142.2005.9637993.

11. Yu E., Wei H., Han Y. et al. Application of time series prediction techniques for coastal bridge engineering, Advances in Bridge Engineering, 2021, vol. 2, iss. 6, pp. 1—18, DOI: 10.1186/s43251-020-00025-4.

12. Aliev T. Noise control of the Beginning and Development Dynamics of Accidents, New York, Springer, 2019, 201 p., DOI: 10.1007/978-3-030-12512-7.

13. Yang Y. B., Lin C. W., Yau J. D. Extracting bridge frequencies from the dynamic response of a passing vehicle, Journal of Sound and Vibration, 2004, vol. 272, iss. 3—5, pp. 471—493, DOI:10.1016/S0022-460X(03)00378-X.

14. Xin H., Cheng L., Diender R. et al. Fracture acoustic emission signals identification of stay cables in bridge enginee ring application using deep transfer learning and wavelet analysis, Advances in Bridge Engineering, 2020, vol. 1, iss. 6, pp. 1—16, DOI: 10.1186/s43251-020-00006-7.

15. Olund J., DeWolf J. Passive Structural Health Monitoring of Connecticut’s Bridge Infrastructure, Journal of Infrastructure Systems, 2007, vol. 13, iss. 4, pp. 330—339, DOI: 10.1061/(ASCE)1076-0342(2007)13:4(330).

16. Wang Ling-bo, Wang Qiu-ling, Zhu Zhao, Zhao Yu. Current Status and Prospects of Research on Bridge Health Monitoring Technology, China Journal of Highway and Transport, 2021, vol. 34, iss. 12, pp. 25—45. DOI: 10.19721/j.cnki.1001-7372.2021.12.003.

17. Sung Y. C., Lin T. K., Chiu Y. T., Chang K. C., Chen K. L., Chang C. C. A bridge safety monitoring system for prestressed composite box-girder bridges with corrugated steel webs based on in-situ loading experiments and a long-term monitoring database, Engineering Structures, 2016, vol. 126, pp. 571—585, DOI: 10.1016/j.engstruct.2016.08.006.

18. Yang Y. B., Yang Judy P. State-of-the-Art Review on Modal Identification and Damage Detection of Bridges by Moving Test Vehicles, International Journal of Structural Stability and Dynamics, 2018, vol. 18, iss. 2, pp. 1850025, DOI: 10.1142/S0219455418500256.

19. Aliev T. A., Musaeva N. F. Decision-Making Technologies in Bridge Structure Monitoring Systems Based on the Noise Model. In: Proceedings of 5th International Conference on Problems of Cybernetics and Informatics (PCI), 2023, Baku, Azerbaijan, pp. 1—5, DOI: 10.1109/PCI60110.2023.10325948.

20. Aliev T. A., Musaeva N. F., Suleymanova M. T. Algorithms for constructing the confidence interval for the mathematical expectation of the noise and their application in the control of the dynamics of accident development, Mekhatronika, Avtomatizatsiya, Upravlenie, 2020, vol. 21, no 9, pp. 521—529 (in Russian), DOI: 10.17587/mau.21.521-529

21. Musaeva N. F. A robust method of estimation under ‘polluting’ rough errors, Automatic control and computer sciences, 2003, no. 6, pp. 50—64, available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-1542579519&origin=recordpage

22. Musaeva N. F. Technology for determining the magnitude of robustness as an estimate of statistical characteristic of noisy signal, Automatic control and computer sciences, 2005, vol. 39, no. 5, pp. 64—74, available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-25144507620&origin=recordpage.

23. Pishro-Nik H. Introduction to Probability, Statistics, and Random Processes. Kappa Research, LLC, 2014, 744 p., available at: https://www.e-booksdirectory.com/details.php?ebook=10166.

24. Aliev T. A., Musaeva N. F., Suleymanova M. T. Algorithms for Determining the Probability of Risks of Accidents in Tunnels Based on the Characteristics of the Noise of Noisy Signals, Mekhatronika, Avtomatizatsiya, Upravlenie, 2021, vol. 22, no. 7, pp. 353—364 (in Russian), DOI: 10.17587/mau.22.357-364.

25. Aliev T. A., Musaeva N. F., Babayev T. A. et al. Technologies and intelligent systems for adaptive vibration control in rail transport, Transport Problems: An International Scientific Journal, 2022, vol. 17, no. 3, pp. 31—38, DOI: 10.20858/TP.2022.17.3.03.

26. Musaeva N. F., Aliyev E. R., Sattarova U. E., Gadimov R. M. Technology and algorithms for monitoring of the technical condition of high-rise buildings, construction and strategic objects in seismically active regions by means of sets of informative attributes, 4th International Conference "Problems of Cybernetics and Informatics", PCI 2012 Proceedings, Baku, 2012, vol. 2, pp. 109—115, available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-84876004916&origin=recordpage.

27. Musaeva N. F., Aliyev E. R., Sattarova U. E., Rzayeva N. F. Correlation matrices in problems of identification of seismic stability and technical condition of high-rise buildings and building structures, 4th International Conference "Problems of Cybernetics and Informatics", PCI 2012 — Proceedings, Baku, 2012, vol. 2, pp. 116—123, available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-84875984284&origin=recordpage.