An unsupervised context-free forecasting method for structural health monitoring by generative adversarial networks with progressive growing and self-attention
Bibliographic record
Abstract
Ensuring the robust operation of bridges demands swift and precise forecasting of structural performance within the health monitoring system. However, challenges arise in the realm of long-time series forecasting context-free data. These challenges encompass scenarios where there is a lack of reference data pre- and postforecasting, instances of missing data before forecasting (near-forecasting), or predictions of the distant future (far-forecasting). Addressing these issues, a current imperative is the development of a framework adept at efficiently and directly forecasting context-free long-time series data. This article introduces a framework, the convolutional generative adversarial network with progressive growing and self-attention (PSA-CGAN) mechanisms, tailored for forecasting context-free data. The approach employs generative adversarial networks in tasks related to long-time series. Additionally, progressive growing and self-attention mechanisms are harnessed to capture both long- and short-term features in the time series, notably enhancing the efficiency and accuracy of the forecasting method. The proposed method undergoes validation through application to two distinct bridge cases, confirming its generality and real-time forecasting prowess. On two bridges, PSA-CGAN can effectively predict acceleration data in various context-free scenarios and is capable of forecasting progressively changing damage data. It provides a valuable reference for predicting damage data. Additionally, the results indicate that PSA-CGAN is a promising and practical solution for the prediction of context-free data. It represents an efficient and rapid tool for damage prediction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".