Overstrength and variance analysis of angle brackets for timber structures considering the uncertainty in the nail–timber interaction
Bibliographic record
Abstract
Engineered wood products, along with advanced processing and fabrication, are pushing the limits of modern wood construction. However, the capacities of such systems are often determined by the capabilities of the connections. Typical connections for timber beams consist of angle brackets with annular ring nails. The load-bearing capacity of these connections is usually provided by their producers and is based on experimental testing, where the connection is typically subjected to short-term monotonic loading. However, the variability of angle bracket capacity is generally governed by uncertainties on the timber side, specifically the interaction between nails and wood. This paper presents the experimental results and a parametric numerical element model for predicting the load-bearing capacity of connections with nailed angle brackets, considering the effects of uncertainties in the nail–timber constitutive behaviour. Three metal brackets for beam–column connections are analysed, and their performance in various loading arrangements is examined. Detailed finite element models have been developed for each connection, where the response of each nail is defined by a semi-physical constitutive relationship calibrated on separate axial and transversal tests on isolated nails. The authors propagate the uncertainty in the nail–timber interaction on the capacity of the angle brackets following a Monte Carlo-based approach in the three considered loading scenarios. The generated dataset is used to estimate the overstrength factors of the connections and explain the variance of the estimated capacity values based on a multilinear regression surrogate model. • Experimental tests on off-the-shelf angle brackets. • Three angle brackets under three loading scenarios. • Stochastic nail–timber constitutive law. • Uncertainty propagation from the nail–timber to the capacity. • Variance explanation and overstrength factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".