Probability of Damage States in Structures Using Dynamic Parameters and Bayesian Inference

Authors

  • Oscar Möller Universidad Nacional de Rosario, Facultad de Ciencias Exactas, Ingeniería y Agrimensura, Instituto de Mecánica Aplicada y Estructuras (IMAE). Rosario, Argentina.
  • Germán Lucero Universidad Nacional de Rosario, Facultad de Ciencias Exactas, Ingeniería y Agrimensura, Instituto de Mecánica Aplicada y Estructuras (IMAE). Rosario, Argentina.
  • Juan Pablo Ascheri Universidad Nacional de Rosario, Facultad de Ciencias Exactas, Ingeniería y Agrimensura, Instituto de Mecánica Aplicada y Estructuras (IMAE). Rosario, Argentina.

DOI:

https://doi.org/10.70567/mc.v41i5.28

Keywords:

Structural dynamics, Damage scenarios, Uncertainties, Bayesian inference, Environmental vibrations

Abstract

Structural health monitoring is a topic of interest due to its impact on infrastructure maintenance programs. It is necessary to consider whether or not there is damage, where it is located, its magnitude and the estimated residual useful life. The methodology implemented is to propose “damage scenarios” in a numerical model of the system, including uncertainties in the physical characteristics through random fields. For each damage scenario, the statistics of the dynamic properties, modes and frequencies are performed on the random variables by applying Monte Carlo. Once an acceleration-time measurement is recorded and the dynamic parameters are identified, the “a priori” probability of obtaining these dynamic parameters for each damage scenario is calculated with the statistics. Applying Bayesian inference, the “a posteriori” probability is calculated given the recorded measurement. The process is repeated until convergence. The final result indicates the probability of occurrence of each damage scenario. It is applied to a slab bridge model and it is concluded that the best option is to use the frequency vector as dynamic parameters.

References

Abrahamsen, P. (1997) A review of Gaussian random fields and correlation functions - Second edition. Norwegian Computing Center, Technical Report 917, Oslo, Norway.

Brincker, R., Zhang, L., & Andersen, P. (2000). Modal Identification from Ambient Responses using Frequency Domain Decomposition. Smart Materials and Structures. Vol. 10 (2001) 441-445. https://doi.org/10.1088/0964-1726/10/3/303

Feng, Z., Lin, Y., Wang, W., Hua, X., Chen, Z. (2020) Probabilistic updating of structural models for damage assessment using approximate bayesian computation. Sensors 2020, 20, 3197. https://doi.org/10.3390/s20113197

Geyer, S., Papaioannou, I., Graham-Brady, L., Straub, D. (2022) The spatial averaging method for non-homogeneous random fields with application to reliability analysis. Engineering Structures, Vol.253, 113751. https://doi.org/10.1016/j.engstruct.2021.113761

Hizal, C. (2021). Frequency domain data merging in operational modal analysis based on least squares approach. Measurement. Vol. 170, 108742. https://doi.org/10.1016/j.measurement.2020.108742

Huang, Y., Shao, C., Wu, B., Beck, J.L., Li, H. (2019) State-of-the-art review on Bayesian inference in structural system identification and damage assessment. Advances in Structural Engineering, Vol. 22(6) 1329-1351. https://doi.org/10.1177/1369433218811540

Hurtado, O.D., Ortiz, A.R., Gomez, D., Astroza, R. (2023) Bayesian model-updating implementation in a five-story building. Buildings 2023, 13, 1568. https://doi.org/10.23967/latam.2023.010

Jiang, X., Mahadevan, S. (2008) Bayesian Probabilistic Inference for Nonparametric Damage Detection of Structures. Journal of Engineering Mechanics, Vol. 134, No. 10. ©ASCE, ISSN 0733-9399/2008/10-820-831. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:10(820)

Jiang, X., Yuan, Y., Liu, X. (2013) Bayesian inference method for stochastic damage accumulation modeling. Reliability Engineering and System Safety 111, 126-138. https://doi.org/10.1016/j.ress.2012.11.006

Liu, Y., Li, J., Sun, S., Yu, B. (2019) "Advances in Gaussian random field generation: A review". Computational Geosciences 23, 1011-1047. https://doi.org/10.1007/s10596-019-09867-y

Lucero, G., Möller, O., Ascheri, J.P. (2024) Identificación de propiedades dinámicas de un modelo físico reducido de un puente losa mediante registro de vibraciones. Mecánica Computacional, en prensa.

Peeters, B. (2000). System Identification and Damage Detection in Civil Engineering. Katholieke Universiteit Leuven - Faculteit Toegepaste Wetenschappen.

Peeters, B., De Roeck, G. (1999), Reference-Based Stochastic Subspace Identification For Output- Only Modal Analysis. Mechanical Systems and Signal Processing. 13(6), 855-878. https://doi.org/10.1006/mssp.1999.1249

Quiroz, L.M. (2011) Probabilistic assessment of damage states using dynamic response parameters. Thesis of Master of Applied Science, University of British Columbia, Canada.

Uzun, M., Sun, H., Smit, D., Büyüköztürk, O. (2019) Structural damage detection using Bayesian inference and seismic interferometry. Struct Control Health Monit. 2019; 26:e2445. https://doi.org/10.1002/stc.2445

Downloads

Published

2024-11-08

Issue

Vol. 41 No. 5 (2024): Uncertainty Quantification and Stochastic Modeling

Section

Conference Papers in MECOM 2024

License

Copyright (c) 2024 Argentine Association for Computational Mechanics

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This publication is open access diamond, with no cost to authors or readers.

Only those papers that have been accepted for publication and have been presented at the AMCA congress will be published.

Most read articles by the same author(s)