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Record W4405999839 · doi:10.3390/jcs9010013

Performance of Timber-Concrete Composite (TCC) Systems Connected with Inclined Screws: A Literature Review

2025· review· en· W4405999839 on OpenAlexafffund
Shadi Esmaeildoust, Douglas Tomlinson, Ying Hei Chui

Bibliographic record

VenueJournal of Composites Science · 2025
Typereview
Languageen
FieldEngineering
TopicStructural Engineering and Vibration Analysis
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComposite numberStructural engineeringMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.164
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.258
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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