Prediction method of condition degradation for network-level bridges based on U-Net++ convolutional neural network
Bibliographic record
Abstract
The condition degradation prediction for network-level bridges is conducive to the decision-making of bridge maintenance. However, traditional prediction methods mainly belong to shallow neural networks and have difficulty in extracting the common degradation features of network-level bridges, thus obtaining a limited prediction accuracy. To this end, this study proposes a prediction method of condition degradation for network-level bridges based on U-Net++ convolutional neural network , in which the U-Net++ is employed to capture the common degradation characteristics of network-level bridges by the fusion of multi-scale feature. Firstly, a dataset of bridge condition is established based on the inspection data of 539 bridges in a typical city. A correlation analysis is performed to preliminarily reveal the relevance between the key features of the bridge and the bridge condition level. Then, the U-Net++ network model is utilized to establish a nonlinear mapping relationship between the key features of the bridge and the bridge condition level. By the established model, the predication of bridge condition level can be achieved. Several machine learning models and three U-Net-based model are employed to verify the advantages of the proposed method. The results show that the proposed model may be the optimal network architecture for bridge condition degradation prediction. It overcomes the shortcomings of traditional U-Net model with a large fluctuation in the prediction and obtains a prediction accuracy of over 80 % for both the primary components of the bridges and the whole bridge.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".