Cold temperature effect on the acoustic activity emitted in various concrete mixtures under special monitoring conditions
Bibliographic record
Abstract
Concrete mixtures with different microstructures and mechanical properties release different acoustic activity when they undergo cracking. This thesis aimed to investigate the effect of sub-freezing temperature (-20℃) on the waveforms of the acoustic emission (AE) in various concrete mixtures under flexural moment and abrasion forces. The study included many variables such as different coarse-to-fine aggregate ratios (C/F) (2.0 and 0.7), crumb rubber (CR) contents (0%, 10%, 20%, and 30%), rubber particle sizes (4.5 mm CR and 0.4 mm powder rubber), water-cement ratios (W/C) (0.4 and 0.55), fiber materials (polypropylene synthetic and steel), synthetic fiber lengths (19 mm and 38 mm), and volumes (0.2% and 1%), and sample temperatures (25℃ and -20℃). Samples from thirteen concrete mixtures were cast and tested under abrasion and monotonic fourpoint flexure moments, along with attaching piezoelectric AE sensors to monitor the AE activity throughout testing. Characteristics of AE signals such as the number of hits, signal amplitudes, cumulative signal strength (CSS), and wave rise time were collected and underwent various AE parameter-based analyses to correlate damage progression to the variation in the AE waveform. The results supported the ability of AE analysis to highlight abrasion damage progression and to detect the onset of micro- and macroflexural cracks at both temperatures. Compared to 25℃, cooling down samples’ temperature to -20℃ was found to decrease the values of the number of hits, CSS, severity (Sᵣ), and historic index (H (t)) and to increase b-values for the waves emitted under abrasion and flexure. Noticeably, the reduction in samples’ temperature decreased the emitted number of hits, CSS, Sᵣ, and H (t), and increased b-values till the onset of the first flexural macro-crack regardless of mixture composition. In addition, increasing CR content (up to 30%) decreased wave signal amplitudes significantly at 25℃ and was less noticeable at -20℃, which manifested the attenuation phenomenon at both temperatures. Eventually, the study developed user-friendly damage charts to estimate ranges of abrasion mass loss and wear depth and to classify the collected AE events, whether associated with flexure micro- or macro-cracks, exclusively considering temperature effect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".