Deep learning-based bridge damage identification approach inspired by internal force redistribution effects
Bibliographic record
Abstract
Damage identification has always been one of the core functions of bridge structural health monitoring (SHM) systems. Damage identification techniques based on deep learning (DL) approaches have shown great promise recently. However, DL methods still need to be improved owing to their poor interpretability and generalization performance. The fundamental reason lies in the separation between physics-based mechanical principles and data-driven DL methods. To address this issue, this paper proposes a physics-inspired approach combining the data-driven method and the internal force redistribution effects to perform efficient damage identification. Firstly, the mechanical derivation of internal force redistribution is given based on a simplified three-span continuous bridge. Then, two types of typical damage scenarios including segment stiffness decrease and prestress loss are simulated to formulate the damage dataset with monitored field data noise added. Next, a modified Transformer model with multi-dimensional output is trained to obtain the complex dynamic spatiotemporal mapping among multiple measurement points from the intact structure as a benchmark model. Finally, the relationship between multiple damage patterns and the corresponding output regression residual distribution is studied, based on which the flexible combinations of the sensors are proposed as the test set to characterize the internal force redistribution due to damage. Validation on the extended dataset showed that this approach is effective to realize preliminary identification of damage patterns and resist interference from noise at the monitoring site.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".