Application of Machine Learning in the Design of Precast Prestressed Concrete I-Girder Bridges
Bibliographic record
Abstract
This thesis describes a study that examines the feasibility, statistical accuracy, and practical reliability of integrating machine learning into the design of precast prestressed concrete I-girder bridges. A database consists of geometrical information extracted from actual construction drawings of existing bridges in Ontario and Alberta was compiled. Feasibility was assessed by exploring empirical trends related to proportions and quantities of primary materials of the I-girder bridges in the database. Statistical accuracy was evaluated by training, validating, and testing a variety of machine learning models configured with different algorithms, output structures, and input data types using a dataset processed based on the bridge database. The models replicate the actual design process, generating geometrical features of I-girder bridges based on a given set of information. Practical reliability was examined by checking the compliance of designs generated by the optimum machine learning models with physical design criteria pertaining to safety and serviceability. The effectiveness and limitations of the machine learning models were discussed.
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".