Challenges of implementing bridge model updating in industry practice
Bibliographic record
Abstract
Model updating aims to update an analysis model (e.g. a finite element model) of an engineering structure in order to closely represent the true condition and performance of the physical structure. Model updating of bridges has been an active research field for more than two decades, yet the confidence and practical usefulness of bridge model updating results may be subject to questioning. While model updating may have worked well for many other engineering applications, it has found to be challenging and problematic to implement such practice on bridge structures. More recently, there has been a vision of developing bridge digital twins which can automatically update the model in near real time as new monitoring data become available. This paper aims to elaborate on the critical issues that have not been addressed properly to enable real-world implementation of bridge model updating. A series of industry facing semi-structured interviews have been conducted with 19 bridge professionals (owners, operators and consultants) to aid in investigating the technical and practical challenges of implementing bridge model updating in practice. It is envisioned that the outcomes of this paper will inform future research regarding model updating and digital twin development for bridge applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".