Assessment of Crack Development in Engineered Cementitious Composites Based on Analysis of Acoustic Emissions
Bibliographic record
Abstract
In this study, acoustic emission (AE) analysis was employed to characterize the cracking behavior of various engineered cementitious composites (ECCs) containing different types of fibers. AE monitoring of prism samples cast with these ECCs was conducted in conjunction with four-point flexural tests. An additional conventional concrete (CC) mixture containing coarse aggregates was also examined for the aim of comparison. A number of AE parameters were collected during testing such as signal amplitude, signal strength, and number of hits. Moreover, b-value and intensity analyses were performed on the signal amplitude and signal strength data, respectively. These further analyses yielded three additional parameters including b-value, historic index [H(t)], and severity (Sr). The analysis of these AE parameters was found to be feasible in detecting the onset of micro and macrocracking stages in all ECCs with any fiber type. No significant variations in terms of the studied AE parameters were found between CC and ECCs at the microcracking stage. However, at the macrocracking and failure stages, the average number of hits, CSS, H(t), and Sr were higher, and the b-value was lower in ECCs compared with CC due to the increased number of cracks in ECCs at these stages. The results of this investigation also presented a developed damage characterization chart for all tested ECCs. This chart can successfully be used to identify the micro and macrocracking stages in ECCs based on the corresponding values of the AE intensity analysis parameters [H(t) and Sr].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".