Large-Scale Civil Engineering Structure Deformation Monitoring Research Based on Image Recognition
Bibliographic record
Abstract
Large-scale civil engineering structures may experience deformations during construction and use due to various reasons, affecting the safety and service life of the structures.The application of image recognition technology in the field of civil engineering has promoted the research and development of related technologies, providing new technical means for the monitoring and evaluation of civil engineering structures.Existing image recognition methods may be affected by factors such as lighting, occlusion, and image quality when dealing with large-scale civil engineering structure deformation monitoring, resulting in reduced recognition accuracy.Therefore, this study conducts research on large-scale civil engineering structure deformation monitoring based on image recognition.Traditional civil engineering structure deformation detection methods are presented.A simple and intuitive curve expression is used to describe the deformation characteristics of civil engineering structures, and GCN is used to mine the feature information between adjacent feature points and long-distance related points to improve prediction performance.A graph convolution prediction module and a geometric auxiliary prediction module are set up for the constructed prediction model, and the setting objectives and structural principles of the two modules are explained.In response to the challenges of extracting large-scale civil engineering structure deformation curves, an automatic extraction method based on deep learning networks is proposed, achieving high-precision recognition and extraction of civil engineering structure deformation curves.Experimental results validate the effectiveness of the proposed method.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".