Investigating the Optimization of Terrestrial Laser Scanning Procedures for the Analysis of Concrete Bridge Pier Spalling
Bibliographic record
Abstract
Asset management of bridge infrastructure will become increasingly important in the coming years, especially since 16% of bridges in Canada will reach their expected life span of 50 years by 2030. There is a need to improve current subjective visual inspections of bridges and implement solutions that are more efficient and objective. Non-contact testing (NCT) technologies, such as terrestrial laser scanning (TLS) can be used to improve inspective efficiency and objectivity due to its high measurement accuracy, measurement speed, and interoperability with other technologies. By gathering point cloud data, TLS acts as a visual inspection tool to scan surface defects of structures, such as spalling, to better understand its overall condition. However, much of the research conducted regarding TLS only accounts for its implementation rather than improving the process itself. Due to this, there are no general guidelines or standards for the use of TLS in visual inspection practices. Consequently, the overall objective of this thesis is to investigate the impact of scan parameters and improve the TLS process for measuring spalling on cylindrical concrete bridge piers. Based on the results of this research, guidelines for implementing TLS to analyse concrete spalling were determined. These guidelines will be able to improve the efficiency and cost-effectiveness of visual inspections and could act as a catalyst for the adoption of TLS as a visual inspection tool. It was determined that the efficiency of TLS procedures can be improved by decreasing the scan resolution (to a point) which resulted in faster scan times and higher accuracy. Decreasing the scan resolution will further reduce scan times but result in larger errors. Furthermore, general guidelines for TLS implementation in the scanning of typical 3-lane highway bridges in Ontario were developed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".