GPR-Based Deterioration Mapping in Subway Networks
Bibliographic record
Abstract
Water leakage through soil has been considered the most serious problem and the main cause of concrete degradation in subway facilities. Several deterioration mechanisms are derived from water intrusion, among others are concrete cracking, spalling, and water voids. These mechanisms can compromise the structural integrity and jeopardize public safety. The detection and evaluation of concrete structures are predominantly conducted on the basis of visual inspection (VI) techniques, which are known to be time-consuming, subjective, and qualitative in nature. Although, these technologies may be consistent in finding surface defects, e.g., cracks, and spalling, they fall short in detecting subsurface distresses such as air voids, and water voids. Ground penetrating radar (GPR) has been widely used for the inspection and evaluation of concrete infrastructure. Nevertheless, few research endeavors were conducted for the detection and mapping of air/water voids. This paper presents a GPR-based assessment model for subway networks. The model performs damage identification and localization of air voids and water voids in the concrete subsurface. It provides a systematic approach for the detection and mapping through the incorporation of image-based analysis (IBA) and processing techniques. First, a defect detection scheme is designed to establish a consistent inspection pattern. Second, subsurface data are collected in a subway network facility. Third, the position and dimension of the detected distresses are mapped to estimate the severity of deterioration. The proposed method was implemented on assessing a segment in Montréal subway network. Validation of the results was conducted through visual inspection, digital images, thermal images and concrete coring samples which demonstrated high correlation and compatibility with the constructed GPR-based maps. The proposed system is expected to improve the quality of decision making as it can assist transportation agencies in identifying critical deficiencies and by focusing constrained funding on most deserving assets.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".