Bibliographic record
Abstract
Infrastructures which were constructed in the last century are approaching or have exceeded their design life. It is essential to maintain, repair and rehabilitate existing structures to ensure safety and develop a sensible rehabilitation plan. This paper suggests an inspection program for the condition evaluation of existing structures, particularly for structures that are approaching or exceeding their design/campaign lives. Cracking on concrete bridges is often induced by static and cyclic temperature loading. Throughout the service life of the structure, cracks will initiate, propagate, coalesce and form a greater damaged zone. Depending on the location and severity of the crack network; corrosion of reinforcement, debonding between concrete and reinforcement, and spalling of concrete may occur. The implementation of regular inspection or monitoring of structural condition is an essential component of asset management. Early detection of damaged zones can ease scheduling and reduce the cost of repair, which in turn also allows extension of structural lifespan without compromising safety. Similarly, for industrial properties, asset management and rehabilitation programs are paramount as they can significantly reduce capital expenditures in asset maintenance, if implemented prior to or at the early onset of structural degradation. This paper illustrates the use of NDT techniques for the condition assessment of a prestressed concrete bridge, as well as concrete storage silos. Both of which were either approaching or have exceeded their design campaign life and were needing a proper rehabilitation plan. The assessment included crack movement monitoring, crack depth determination, concrete strength estimation, and the detection of debonding between rebar and concrete.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".