Experimental Study On The Slip Resistance, Bolt Relaxation And Fatigue Behaviour Of ASTM A1010 Stainless Steel For Bridge Applications
Bibliographic record
Abstract
<div>Unpainted weathering steel has been used extensively in Canada for bridge construction primarily to reduce the maintenance requirements and increase durability in comparison to regular structural steel. However, weathering steel has not performed well in bridges where chlorides from de-icing salts get onto the steel and prevent the development of a proper protective patina on its surface.</div><div>An alternative solution to this problem is the use of ASTM A1010 stainless steel with a chemical composition that enhances corrosion resistance compared to the standard weathering steel. Most bridge splice connections are made up of gusset plates and high strength bolts (namely; A325 and A490) and are designed using the slip-critical method. The Canadian Highway Bridge Design Code (CSA S6-14) specifies an equation for the calculations of slip resistance of bolted joints using either the A325 or A490 high strength bolts with regular structural steel, at varying surface conditions. However, this equation is not applicable to stainless steel ASTM A1010 due to lack of research data. In addition, the fatigue category of A1010 steel is as of yet unavailable. Therefore, this research seeks to determine: (i) the slip resistance coefficient of slip-critical joints made of</div><div>A1010 steel plates; (ii) bolt relaxation of three bolt types, namely: A325 plain, A325 galvanized, A193 B8 stainless steel; and (iii) fatigue behaviour of A1010 stainless steel plates. Identical specimens made of Canadian structural steel plates (CSA G40.21 350W grade) were fabricated</div><div>and tested to represent the baseline performance for A1010 stainless steel. The experimental findings led to recommendations for design of slip-critical connections and further research on different fatigue detail categories.</div>
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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".