Proposed Design Methods for Timber Members Subjected to Blast Loads
Bibliographic record
Abstract
Current blast design provisions for wood members are limited and may unintentionally lead to designs that are too conservative or not sufficiently safe. Novel methodologies for the design of light-frame wood stud walls, glulam members, as well as cross-laminated timber (CLT) panels subjected to blast loads are proposed and verified with published experimental full-scale test results. Additionally, full-scale static and dynamic experimental tests were conducted as part of this study to augment published data, thereby helping provide specific design parameters and modeling methodologies. Key design parameters such as dynamic increase factors, ductility ratios, and resistance curves were proposed and evaluated for the purpose of establishing accurate and representative design methods. The peak member resistance and maximum midspan displacements were used as performance metrics to verify the accuracy of the proposed analysis method and design methodologies. The results obtained from the proposed design methods were shown to establish the overall behavior of the timber members with reasonable accuracy, while correctly predicting the governing failure mode. The current blast design provisions were also evaluated and key shortcomings highlighted. Additionally, general design considerations for timber connections were introduced and are discussed in this paper.
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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 | 0.001 |
| 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) | 0.007 | 0.002 |
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".