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Cited by 11

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Bayesian system identification for structures considering spatial and temporal correlation

Koune, Ioannis, Rózsás, Árpád, Slobbe, Arthur, Cicirello, Alice·2023·Data-Centric Engineering
DOI 10.1017/dce.2023.18
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Abstract

The decreasing cost and improved sensor and monitoring system technology (e.g., fiber optics and strain gauges) have led to more measurements in close proximity to each other. When using such spatially dense measurement data in Bayesian system identification strategies, the correlation in the model prediction error can become significant. The widely adopted assumption of uncorrelated Gaussian error may lead to inaccurate parameter estimation and overconfident predictions, which may lead to suboptimal decisions. This article addresses the challenges of performing Bayesian system identification for structures when large datasets are used, considering both spatial and temporal dependencies in the model uncertainty. We present an approach to efficiently evaluate the log-likelihood function, and we utilize nested sampling to compute the evidence for Bayesian model selection. The approach is first demonstrated on a synthetic case and then applied to a (measured) real-world steel bridge. The results show that the assumption of dependence in the model prediction uncertainties is decisively supported by the data. The proposed developments enable the use of large datasets and accounting for the dependency when performing Bayesian system identification, even when a relatively large number of uncertain parameters is inferred.

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Crossref · 10.1017/dce.2023.18

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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Figures

Image 1
CC-BY · From the PDF
Figure 2 Overview of the Bayesian inference approach used in this work.
Figure 2 Overview of the Bayesian inference approach used in this work.CC-BY · From the PDF
Figure 3 Illustration of space and time coordinate system. Influence lines along the time axis t are obtained for each sensor position x .
Figure 3 Illustration of space and time coordinate system. Influence lines along the time axis t are obtained for each sensor position x .CC-BY · From the PDF
Figure 4 Elevation view of the IJsselbridge (top), and typical cross-section including K-brace (bottom), with lengths shown in meters (from Rijkswaterstaat).
Figure 4 Elevation view of the IJsselbridge (top), and typical cross-section including K-brace (bottom), with lengths shown in meters (from Rijkswaterstaat).CC-BY · From the PDF
Figure 5 Illustration of the IJsselbridge FE model (left), lateral load function (right), and parametrization of the FE model (bottom).
Figure 5 Illustration of the IJsselbridge FE model (left), lateral load function (right), and parametrization of the FE model (bottom).CC-BY · From the PDF
Figure 6 Approximate location of sensors on the right girder. The prefix ' H ' is used to denote the sensors on the main structure of the IJsselbridge. Adapted from a Rijkswaterstaat internal report.
Figure 6 Approximate location of sensors on the right girder. The prefix ' H ' is used to denote the sensors on the main structure of the IJsselbridge. Adapted from a Rijkswaterstaat internal report.CC-BY · From the PDF
Figure 7 Stress influence lines obtained from the measurement campaign. The blue and red lines correspond to the measured response for different truck positions in the transverse direction of the bridge.
Figure 7 Stress influence lines obtained from the measurement campaign. The blue and red lines correspond to the measured response for different truck positions in the transverse direction of the bridge.CC-BY · From the PDF
Figure 8 Rectilinear grid of sensor and measurement positions considered in the synthetic case study for Nx ¼ Nt ¼ 1,5,10 f g .
Figure 8 Rectilinear grid of sensor and measurement positions considered in the synthetic case study for Nx ¼ Nt ¼ 1,5,10 f g .CC-BY · From the PDF
Figure 9 Relative error of the mean MAP estimates of probabilistic model parameters compared to the ground truth, and COV of MAP estimates as a function of grid size.
Figure 9 Relative error of the mean MAP estimates of probabilistic model parameters compared to the ground truth, and COV of MAP estimates as a function of grid size.CC-BY · From the PDF
Figure 10 Comparison of posterior mean and 90 % HD CIs for models with multiplicative uncertainty structure.
Figure 10 Comparison of posterior mean and 90 % HD CIs for models with multiplicative uncertainty structure.CC-BY · From the PDF
Figure 11 Comparison of posterior mean and 90 % HD CIs for models with additive uncertainty structure.
Figure 11 Comparison of posterior mean and 90 % HD CIs for models with additive uncertainty structure.CC-BY · From the PDF
Figure 12 Joint unidentifiability of the correlation length and model prediction uncertainty parameters for the exponential kernel considering additive (left) and multiplicative (right) model prediction uncertainty.
Figure 12 Joint unidentifiability of the correlation length and model prediction uncertainty parameters for the exponential kernel considering additive (left) and multiplicative (right) model prediction uncertainty.CC-BY · From the PDF
Figure 13 Comparison of posterior mean and 90 % highest density credible intervals for models with multiplicative uncertainty structure.
Figure 13 Comparison of posterior mean and 90 % highest density credible intervals for models with multiplicative uncertainty structure.CC-BY · From the PDF
Figure 14 Comparison of posterior mean and 90 % highest density credible intervals for models with additive uncertainty structure.
Figure 14 Comparison of posterior mean and 90 % highest density credible intervals for models with additive uncertainty structure.CC-BY · From the PDF
Figure 15 Comparison of median and 90 % credible intervals of the posterior predictive stress distribution per model and sensor at the location of peak stress for a truck on the right lane. The red dashed lines denote the measurements.
Figure 15 Comparison of median and 90 % credible intervals of the posterior predictive stress distribution per model and sensor at the location of peak stress for a truck on the right lane. The red dashed lines denote the measurements.CC-BY · From the PDF
Figure A1. Peak stress response at selected sensors as a function of log 10 Kr ð Þ (left) and log 10 Kv ð Þ (right).
Figure A1. Peak stress response at selected sensors as a function of log 10 Kr ð Þ (left) and log 10 Kv ð Þ (right).CC-BY · From the PDF
Image 17
CC-BY · From the PDF

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Citations

Cites

Show all 2 references

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Cited by

Metadata

Authors
Koune, I., Rózsás, Á., Slobbe, A., & Cicirello, A.
ORCID
Venue
Data-Centric Engineering
Publisher
Cambridge University Press (CUP)
Published
2023
Volume
4
ISSN
2632-6736
DOI
10.1017/dce.2023.18
Cited by
11
References
2
Language
en
Licence
creativecommons.org/licenses/by/4.0 (unspecified, 295 d)
Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWA.1431.20.002)

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