Performance of Timber-Concrete Composite (TCC) Systems Connected with Inclined Screws: A Literature Review
Bibliographic record
Abstract
Timber–concrete composite (TCC) systems present a viable alternative to conventional timber or reinforced concrete systems. TCC leverages the advantages of both materials, resulting in an enhanced composite structure. Historically, traditional mechanical connectors such as nails, bolts, and dowels have been used in TCC systems to join timber and concrete components. However, these connectors often fall short in providing sufficient load transfer efficiency. Therefore, the use of screws and, more recently, inclined screws in TCC systems has increased due to their enhanced load transfer efficiency and greater stiffness compared to traditional connections. This review paper consolidates findings from contemporary experimental studies and analytical models, examining the influence of factors such as screw type and inclination angle on the performance of TCC systems for both connection and beam specimens in ultimate and serviceability limit states. Key issues addressed include the shear strength, stiffness, and long-term behaviour of the connection type. By offering a comprehensive synthesis of existing knowledge, this paper aims to inform design practices and contribute to the development of more resilient and efficient TCC systems, supporting their increased adoption in sustainable construction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".