FIRE RESISTANCE TESTING OF CLT-CONCRETE COMPOSITE FLOOR SLABS WITH STRIP NOTCH SHEAR CONNECTIONS
Bibliographic record
Abstract
The latest edition of the National Building Code of Canada (NBCC 2020) permits wood products to be used as the primary material in constructing up to twelve-story buildings.This results in an enduring increase in the application of mass timber sections, such as glued-laminated timber (glulam) and cross-laminated timber (CLT).Timber floors in such tall mass timber buildings can be even more robust and span longer by adding a top concrete layer to allow the formation of timber-concrete composite (TCC) floor systems when adequate shear connectors are utilized.In TCC systems, notch connections can allow a gap between the timber and the concrete top layer to have a better floor system in terms of cost saving and acoustic characteristics.This paper presents the experimental results of two full-size TCC floor slabs that have been examined under service load and standard fire exposure.Each test assembly had a 5300 mm total length with a 5000 mm clear span and 900 mm width.The composite sections were formed of a 5-ply CLT panel of 143 mm thickness and a 65-mm thick concrete layer on a 25-mm thick insulation layer.In those two fire tests, the performance of CLT-concrete composite slabs was achieved due to the nearly full composite action between the CLT panel and the concrete top layer, resulting from the utilization of 150-mm wide rectangular strip notch shear connections.Test results show that the failure of the composite slabs under standard fire exposure depends mainly on the reduction of the CLT section due to charring.However, due to the activation of the composite action, both composite floor slabs sustained the applied service loads for the 90 min duration of standard fire exposure.However, a whole-wide strip notch connection needs to be designed carefully to achieve acceptable fire resistance based on available design equations.In addition, having a whole-wide notch connection would influence the initial deflections of the supporting CLT panels while poured concrete is still wet if no shoring is provided in the middle of the slabs.Thus, to avoid excessive initial mid-span deflections and to meet the design serviceability requirements, intermediate support needs to be provided until concrete is hardened.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".