Acoustic emission characteristics of cross-laminated timber-bamboo column with various layup configurations under axial compression
Bibliographic record
Abstract
The fast-growing Chinese fir and bamboo are abundant in China, which have been utilized to manufacture cross-laminated timber-bamboo (CLTB) and glued-laminated timber-bamboo (GLTB). This study was aimed at examining the layup configurations of CLTB and GLTB specimens on their axial compressive performance. The acoustic emission (AE) technique was employed to monitor the failure process of CLTB and GLTB column specimens. The results indicated that the compressive strength ( f c ) and modulus of elasticity (MOE) of CLTB were about 2.04 and 1.13 times of CLT, respectively. Compared with CLTB, the GLTB specimens had about 33 % and 30 % higher f c and MOE, respectively. Based on the AE parameter analysis, the failure process of all specimens could be grouped into gentle, steady, and steep periods, and the “fracture precursor characteristic area” could be employed to predict the failure. The specimens made of bamboo scrimber as outer longitudinal lamination released higher accumulated energy than those with fast-growing Chinese fir lumber. In addition, different optimal clustering number was obtained for the specimens with different layup configurations, which resulted in three kinds of signal clusters corresponding to different failure. The use of AE technique in this study might contribute to the health and safety monitoring of timber buildings.
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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".