Investigation of installation characteristics of ASTM-F1852 twist-off type tension control fasteners under various temperature levels
Bibliographic record
Abstract
Twist-off tension control method is one of the available and recognized methods for bold pretensioning in the steel construction industry. Twist-off tension control bolts are commonly used in steel construction because they are fast, easy to utilize and convenient to inspect. Twist-off tension control is characterized by a spline at the end which can shear off at a circular notch (torque control groove) when the required preload is achieved. Preload in twist-off assemblies are based on torque control that can be affected by friction between components of the bolt assembly. This friction can be affected by any environmental condition or material property such as bolt material strength, geometric properties of the assembly, thread and lubrication conditions of the nut and bolt and installation temperature. Earlier studies have shown that twist-off bolt assemblies used in the field and left exposed to weather for a period of time prior to applying the pretensioning force, demonstrate degradation in the achieved tensile force. This phenomenon formed a series of research concerning effects of moisture, delay installation and temperature levels on the achieved tensile force in the bolt. The effect of temperature levels in degradation of achieved tensile force forms the principle parameter of concern in the current research. Results showed that the twist-off bolt assemblies failed to achieve the minimum specified pretension for installation temperatures less than 0°C. Empirical equations were developed to provide engineers and fabricators of an estimate of the reduction in the achieved pretensioning force in twist-off bolt assemblies with the change in temperature levels at time of installation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".