Condition assessment of structural components using non-destructive techniques
Bibliographic record
Abstract
Condition assessment of components is an important aspect for determining repair strategies of ageing structural systems and for ascertaining the durability of the repairs once they are effected. Two projects have been undertaken by the 'Concrete Materials and Structural Technologies Group' at the Institute for Research in Construction of the National Research Council Canada for studying both aspects of repairs. The first project is investigating the effectiveness of a sonic wave technique in accurately determining the in-situ length of newly installed soil nails. Once soil nails are inserted into the ground, their length can only be readily verified by destructively removing them. This project examines the efficacy of using the impulse-response technique to ascertain the length of grouted and ungrouted soil nails containing one or more coupled sections of steel bars. The second project is studying the effect of a fibre-reinforced polymer (FRP) laminate on the internal conditions of a steel reinforced concrete column. Two adjacent columns, one wrapped with a FRP laminate and the second left in its existing condition have been monitored over a period of many years. The monitoring comprises annual non-destructive sonic and electro-chemical surveys and continuous on-site recording of relative humidities and temperatures. The objective of the study is to determine whether the laminate affects the internal electro-chemical environment within the wrapped column so as to reduce the durability of the primary steel reinforcement. This paper gives a brief overview of the two projects and summarises what has been learned to date.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".