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Value of information from vibration-based structural health monitoring extracted via Bayesian model updating

Kamariotis, Antonios, Chatzi, Eleni, Straub, Daniel·2022·Mechanical Systems and Signal Processing
DOI 10.1016/j.ymssp.2021.108465
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  1. Farrar, 2007, An introduction to structural health monitoring, Philos. Trans. R. Soc. A, 365, 303, 10.1098/rsta.2006.1928 · DOI
  2. M. Limongelli, E. Chatzi, E. Reynders, G. Lombaert, Towards extraction of vibration-based damage indicators, in: Proceedings of the 8th European Workshop on Structural Health Monitoring (EWSHM 2016), Bilbao, Spain, 2016, pp. 546–555.
  3. D. Straub, E. Chatzi, E. Bismut, et al. Value of information: A roadmap to quantifying the benefit of structural health monitoring, in: Proceedings of the 12th International Conference on Structural Safety and Reliability: ICOSSAR 2017, Vienna, Austria, 2017, pp. 3018–3029.
  4. Straub, 2005, Risk based inspection planning for structural systems, Struct. Saf., 27, 335, 10.1016/j.strusafe.2005.04.001 · DOI
  5. Luque, 2019, Risk-based optimal inspection strategies for structural systems using dynamic Bayesian networks, Struct. Saf., 76, 68, 10.1016/j.strusafe.2018.08.002 · DOI
  6. Vereecken, 2020, Bayesian decision analysis for the optimization of inspection and repair of spatially degrading concrete structures, Eng. Struct., 220, 10.1016/j.engstruct.2020.111028 · DOI
  7. M. Pozzi, A.D. Kiureghian, Assessing the Value of Information for long-term structural health monitoring, in: SPIE Conference on Health Monitoring of Structural and Biological Systems, San Diego, California, USA, 2011. · DOI
  8. Zonta, 2014, Value of information: impact of monitoring on decision making, Struct. Control Health Monit., 21, 1043, 10.1002/stc.1631 · DOI
  9. Straub, 2014, Value of information analysis with structural reliability methods, Struct. Saf., 49, 68, 10.1016/j.strusafe.2013.08.006 · DOI
  10. S. Thöns, R. Schneider, M. Faber, Quantification of the value of structural health monitoring information for fatigue deteriorating structural systems, in: Proceedings of the 12th International Conference on Applications of Statistics and Probability in Civil Engineering, Vancouver, Canada, 2015.
  11. Konakli, 2016, Numerical investigations into the value of information in lifecycle analysis of structural systems, ASCE-ASME J. Risk Uncertain. Eng. Syst. A, 2, B4015007, 10.1061/AJRUA6.0000850 · DOI
  12. Zhang, 2020, Voi-informed decision-making for SHM system arrangement, Struct. Health Monit., Special Issue, 1
  13. Iannacone, 2021, Quantifying the value of information from inspecting and monitoring engineering systems subject to gradual and shock deterioration, Struct. Health Monit., Special Issue, 1
  14. A. Kamariotis, D. Straub, E. Chatzi, Optimal maintenance decisions supported by SHM: A benchmark study, in: Proceedings of the Seventh International Symposium on Life-Cycle Civil Engineering: IALCCE 2020, Shanghai, China, 2020. · DOI
  15. Peeters, 1999, Reference-based stochastic subspace identification for output-only modal analysis, Mech. Syst. Signal Process., 13, 855, 10.1006/mssp.1999.1249 · DOI
  16. Au, 2013, Bayesian operational modal analysis: theory, computation, practice, Comput. Struct., 126, 3, 10.1016/j.compstruc.2012.12.015 · DOI
  17. Wright, 2020, How to tell the difference between a model and a digital twin, Adv. Model. Simul. Eng. Sci., 7, 10.1186/s40323-020-00147-4 · DOI · Paper page
  18. Vanik, 2000, Bayesian probabilistic approach to structural health monitoring, J. Eng. Mech., 126, 738
  19. Papadimitriou, 2001, Updating robust reliability using structural test data, Probab. Eng. Mech., 16, 103, 10.1016/S0266-8920(00)00012-6 · DOI
  20. Beck, 2002, Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation, J. Eng. Mech., 128, 380
  21. Simoen, 2015, Dealing with uncertainty in model updating for damage assessment: a review, Mech. Syst. Signal Process., 56, 123, 10.1016/j.ymssp.2014.11.001 · DOI · Paper page
  22. Yueng, 2011, Bayesian methods for updating dynamical models, Appl. Mech. Rev., 64
  23. Behmanesh, 2015, Hierarchical Bayesian model updating for structural identification, Mech. Syst. Signal Process., 64–65, 360, 10.1016/j.ymssp.2015.03.026 · DOI
  24. Ntotsios, 2009, Bridge health monitoring system based on vibration measurements, Bull Earthq. Eng., 7, 469, 10.1007/s10518-008-9067-4 · DOI
  25. Behmanesh, 2015, Probabilistic identification of simulated damage on the dowling hall footbridge through Bayesian finite element model updating, Struct. Control Health Monit., 22, 463, 10.1002/stc.1684 · DOI
  26. Argyris, 2020, Bayesian model-updating using features of modal data: Application to the metsovo bridge, Sens. Actuator Netw., 9, 27, 10.3390/jsan9020027 · DOI
  27. Simoen, 2013, Uncertainty quantification in the assessment of progressive damage in a 7-story full-scale building slice, J. Eng. Mech., 139, 1818
  28. Ierimonti, 2020, An innovative continuous Bayesian model updating method for base-isolated RC buildings using vibration monitoring data, Mech. Syst. Signal Process., 139, 10.1016/j.ymssp.2019.106600 · DOI
  29. K. Tatsis, E. Chatzi, A numerical benchmark for system identification under operational and environmental variability, in: 8th International Operational Modal Analysis Conference (IOMAC 19), Copenhagen, Denmark, 2019.
  30. Pandey, 1991, Damage detection from changes in curvature mode shapes, J. Sound Vib., 226, 217
  31. Reynders, 2008, Uncertainty bounds on modal parameters obtained from stochastic subspace identification, Mech. Syst. Signal Process., 22, 948, 10.1016/j.ymssp.2007.10.009 · DOI · Paper page
  32. Simoen, 2013, On prediction error correlation in Bayesian model updating, J. Sound Vib., 18, 4136, 10.1016/j.jsv.2013.03.019 · DOI · Paper page
  33. Beck, 1998, Updating models and their uncertainties. I: Bayesian statistical framework, J. Eng. Mech., 124, 455
  34. Haario, 2006, DRAM: Efficient adaptive MCMC, Stat. Comput., 16, 339, 10.1007/s11222-006-9438-0 · DOI
  35. Straub, 2020, Reliability analysis of deteriorating structural systems, Struct. Saf., 82, 10.1016/j.strusafe.2019.101877 · DOI
  36. Bismut, 2021, Optimal adaptive inspection and maintenance planning for deteriorating structural systems, Reliab. Eng. Syst. Saf., 215, 10.1016/j.ress.2021.107891 · DOI
  37. Elingwood, 2005, Risk-informed condition assessment of civil infrastructure: state of practice and research issues, Struct. Infrastruct. Eng., 1, 7, 10.1080/15732470412331289341 · DOI
  38. Frangopol, 2004, Probabilistic models for life-cycle performance of deteriorating structures: review and future directions, Prog. Struct. Eng. Mater., 6, 197, 10.1002/pse.180 · DOI
  39. Biondini, 2016, Life-cycle performance of deteriorating structural systems under uncertainty, J. Struct. Eng., 142, F4016001, 10.1061/(ASCE)ST.1943-541X.0001544 · DOI
  40. Prendergast, 2014, A review of bridge scour monitoring techniques, J. Rock Mech. Geotech. Eng., 6, 138, 10.1016/j.jrmge.2014.01.007 · DOI

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Metadata

Authors
Kamariotis, A., Chatzi, E., & Straub, D.
ORCID
Venue
Mechanical Systems and Signal Processing
Publisher
Elsevier BV
Published
2022-03
Volume
166
Pages
108465
ISSN
0888-3270
DOI
10.1016/j.ymssp.2021.108465
Cited by
128
References
0
Language
en
Licence
Funders
Institute for Advanced Study, Technische Universität München

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CoChat. (2026). Citation check: Value of information from vibration-based structural health monitoring extracted via Bayesian model updating. https://app.cochat.ai/papers/10.1016/j.ymssp.2021.108465
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Kamariotis, A., Chatzi, E., & Straub, D. (2022). Value of information from vibration-based structural health monitoring extracted via Bayesian model updating. Mechanical Systems and Signal Processing. https://doi.org/10.1016/j.ymssp.2021.108465