Development of Digital Twin Concept for Real-Time Detection of Abnormal Changes in Structural Behaviour
Bibliographic record
Abstract
The paper aims to extend the definition of Digital Twin (DT) concept to be able to identify small severity damages by incorporating mathematical formulations in construction of neural networks.Advanced modelling techniques such as Reduced Basis (RB) method and artificial neural networks were used during the offline stage to develop a DT model to detect abnormal changes in structural behaviour during the online monitoring stage.Finite element model was used with RB model order reduction technique for construction of a low-dimensional space to speed the analysis during the online stage.Different damage scenarios were implemented by reducing the effective stiffness of several structural members to test the ability of the established DT model to detect damages once they have appeared in the structure.In addition, the RB model was used to choose the optimal location of displacement sensors in a physical structure.The RB model was validated against experimental test results for a two-dimensional truss.A neural network was developed and trained to identify the location of damage once it has appeared during the operational stage.The constructed RB model was used again for identification of severity of damage identified by the optimized neural network.It was found that the developed method showed high accuracy in identifying small severity damages during the online stage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".