Novel Sensors for Improving Reinforced Concrete Design and Assessment
Bibliographic record
Abstract
The rate that reinforced concrete (RC) infrastructure is being constructed around the world continues to increase, placing significant stress on the environment. Thus, it is important that any inefficiencies in RC design and assessment be minimized by structural engineers moving forward. For progress to be made on this front, RC behaviour must be measured in a more comprehensive way to further understand its complex behaviour and explore avenues for refinement. This thesis investigates the use of novel sensors for this purpose, with a particular focus on distributed fibre optic sensors (FOS). An experimental campaign containing 18 RC beam specimens was performed to assess the advantages of using FOS to measure RC behaviour. Practical methods were developed for measuring distributed deflections, crack widths, and distributed reinforcement strains. The results showed that full deflected shapes could be captured up until failure and all crack widths lower than 0.3 mm could be measured. Additionally, reinforcement strain profiles were accurately measured for steel reinforcement embedded in RC elements using FOS, amounting to hundreds of strain measurements spaced by 10 mm along each bar’s entire length. A load test on an RC building in Ottawa, Canada, was performed. FOS were used to measure beam deflections, curvatures, and cracking behaviour, providing insight into element stiffness and support conditions. The detailed data set captured was used to evaluate finite element models based on current design approaches. This highlighted how the accumulation of common conservative assumptions can cause deflection predictions more than five times larger than those measured, potentially leading to excess material use in design. Closure strips in the RC building were also monitored using a method developed in this work, which was able to capture shrinkage and temperature movement of the floor slabs. The field data demonstrated that experience based design approaches appear to be overly conservative, suggesting that further research into closure strips could lead to significant cost reductions if design processes are refined. However, the instrumentation setup developed to monitor closure strips in this study requires durability improvements, as four out of five transducers installed were compromised on site.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".