Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 11

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

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
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1017/dce.2023.18

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 45

  1. 10.1063/1.1699114 · DOI · Paper page
  2. 10.1061/(ASCE)0733-9399(1998)124:4(455) · DOI
  3. 10.1002/stc.424 · DOI
  4. 10.1002/stc.1684 · DOI
  5. Quarteroni, 2007, Numerical Mathematics
  6. 10.1002/stc.2140 · DOI
  7. 10.1093/mnras/staa278 · DOI
  8. Cheong, S (2016) Parameter Estimation for the Spatial Ornstein-Uhlenbeck Process with Missing Observations. PhD Thesis, University of Wisconsin Milwaukee UWM.
  9. Koune, I (2021) Bayesian System Identification for Structures Considering Spatial and Temporal Dependencies. MSc Thesis, Delft University of Technology.
  10. 10.1016/j.ymssp.2011.04.001 · DOI
  11. 10.1201/b16018 · DOI · Paper page
  12. MacKay, 2003, Information Theory, Inference and Learning Algorithms
  13. 10.1093/biomet/57.1.97 · DOI · Paper page
  14. Duvenaud, DK (2014) Automatic Model Construction with Gaussian Processes. PhD Thesis, University of Cambridge.
  15. 10.1111/1467-9868.00294 · DOI · Paper page
  16. 10.1080/1361593022000029467 · DOI
  17. 10.1016/j.ymssp.2017.01.040 · DOI · Paper page
  18. 10.1016/j.ymssp.2011.05.019 · DOI
  19. 10.1098/rsta.2006.1925 · DOI
  20. 10.1016/j.apenergy.2019.114394 · DOI
  21. 10.1214/06-BA127 · DOI
  22. 10.1016/j.cageo.2017.11.012 · DOI
  23. 10.1080/15732479.2022.2131847 · DOI
  24. 10.1177/1369433218811540 · DOI
  25. 10.1016/j.aei.2015.07.007 · DOI
  26. 10.1016/j.ymssp.2014.11.001 · DOI · Paper page
  27. Hoeting, 1999, Bayesian model averaging: A tutorial, Statistical Science, 14, 382
  28. 10.1002/9781118443118 · DOI
  29. 10.1080/01621459.2017.1415907 · DOI
  30. 10.3390/s16050748 · DOI
  31. 10.1111/j.1467-8667.2006.00432.x · DOI
  32. Jeffreys, 2003, Theory of Probability
  33. 10.1002/suco.201700169 · DOI
  34. Ye, 2014, Structural health monitoring of civil infrastructure using optical fiber sensing technology: A comprehensive review, The Scientific World Journal, 2014, 1
  35. 10.1016/j.jsv.2013.03.019 · DOI · Paper page
  36. 10.1016/j.jsv.2011.07.036 · DOI
  37. Stegle, 2011, Efficient inference in matrix-variate Gaussian models with iid observation noise, Advances in Neural Information Processing Systems, 24, 630
  38. Rogers, TJ (2018) Towards Bayesian System Identification: With Application to SHM of Offshore Structures. PhD Thesis, University of Sheffield.
  39. 10.1016/j.engstruct.2014.08.035 · DOI
  40. 10.1002/suco.201700287 · DOI
  41. 10.1016/j.ijfatigue.2014.08.003 · DOI
  42. 10.1002/env.854 · DOI
  43. 10.1016/j.ymssp.2021.107760 · DOI · Paper page
  44. 10.1061/(ASCE)0733-9399(2004)130:2(192) · DOI
  45. 10.1016/S0267-7261(98)00008-6 · DOI

Cited by

See all 11

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)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Bayesian system identification for structures considering spatial and temporal correlation. https://app.cochat.ai/papers/10.1017/dce.2023.18
The paper
Koune, I., Rózsás, Á., Slobbe, A., & Cicirello, A. (2023). Bayesian system identification for structures considering spatial and temporal correlation. Data-Centric Engineering. https://doi.org/10.1017/dce.2023.18

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.