Influence of Loading Direction on the Behavior of Steel–Grout Hybrid Joints for Mass Timber Panels
Bibliographic record
Abstract
This paper presents a blended experimental, numerical, and statistical approach to characterize the influence of loading direction on the behavior of hybrid connectors used in mass timber panels. These connectors consist of a thick layer of epoxy-based grout surrounding steel rods, all embedded within the cross-laminated timber (CLT) panels. A series of 42 monotonic pushout tests were conducted to evaluate their structural performance parameters, including initial stiffness, yield resistance, and ultimate resistance. The specimens were tested in two orientations: half were loaded parallel to the face layers; the other half were loaded perpendicular to these layers. Following the experimental tests, a high-fidelity finite-elements model was developed using ANSYS commercial software to simulate the structural behavior of the hybrid connectors under various loading angles. This model was calibrated using the experimental results. Simulations were performed for different angles between the loading direction and the CLT face layers, including 0, π/6, π/4, π/3, and π/2 radians. Experimental and simulation results indicated that the connectors exhibited better structural performance as the loading angle approached 0°. The finite-elements model accurately captured the slip modulus, yield resistance, and ultimate resistance, achieving an accuracy of over 95%. Additionally, the model provided insights into the structural performance, yield mechanisms, and failure propagation of connectors at various orientations relative to the CLT face layers. Based on the finite-elements model’s outputs, trigonometric equations derived from Hankinson’s formula were developed to predict the structural performance parameters of the connectors as a function of the CLT layer orientations relative to the loading direction. These predictive equations demonstrated excellent performance, with coefficients of determination approaching 1.0 in several cases.
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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 | 0.000 | 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".