Construction-control technique for PC bridge with cantilever casting method
Why this work is in the frame
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Bibliographic record
Abstract
Adaptive control theory, structural parameter identification and error analysis are used to control the construction of PC cantilever casting bridge in the paper. The construction camber for main beam can be determined by BP artificial neural network. The deflection of main beam should be tested persistently. Strain sensors are previously located in beam to determine the concrete strain. The paper analyze some infection factors of strain test, such as concrete shrinkage, creep, temperature influence, and the strain correction method is proposed. So the stress and deflection of main beam in construction period can be controlled. Practical application approved that adaptive control theory combining with ANN could forecast appropriate camber. The factor of concrete shrinkage and creep could not be ignored on strain test of main beam. Measured value should be corrected to make us obtain objective stress status of structure. The correction method on measured strain which the paper proposed is feasible and accurate.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it