Multi-Characteristic Parameter Classification Algorithm of Cracks on Bridge Substructures
Bibliographic record
Abstract
The forms of bridge cracks vary widely, but the automatic classification and identification of the effects of these cracks are difficult to achieve. Many recognition systems developed all over the world are based on recognition results and carry out human-machine dialogues. These systems rely on the manual recognition of crack types, but the manual approach not only has a low working efficiency but also a high error rate. In this study, a classification algorithm for cracks on bridge substructures based on multi-characteristic parameters was proposed to accurately identify cracks on concrete bridges and objectively and accurately evaluate the state of the bridge cracks. The geometric characteristics of the cracks in the substructure were extracted, and the projection vector, crack area, distribution density, and Euler number were obtained. Projection and wavelet denoising algorithms were used to first distinguish the linear cracks from the network cracks, and the number of holes in the crack image was employed as a parameter to further determine the crack type. Then, the Euler number was introduced to retain the image characteristic when the image required to be changed. Finally, the back propagation (BP) neural network system was used to achieve an accurate crack classification. This study was verified by experiments. Results demonstrate that the classification algorithm can effectively identify four types of cracks, namely, transverse, longitudinal, reflective, and meshed cracks. In the identification of transverse, longitudinal, and reflective cracks, the corresponding classification accuracies in this study were 12%, 3%, and 4% higher than the classification algorithm with the canny operator. This study can meet the requirements of crack classification accuracy in practical engineering and provide a scientific reference for the maintenance of bridges.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".