Dynamic Hysteretic Characteristics of High-Strength Steels (POSTEN60, POSTEN80) and Application of a Dynamic Hysteresis Model to FE Analysis
Bibliographic record
Abstract
As steel structures become larger, taller, and longer, the demand for high-strength steel increases. High-strength steels exhibit different mechanical characteristics and hysteretic behavior for dynamic deformation than for quasi-static deformation. This is attributable to the strain rate and temperature dependence of steel materials when nonuniformly deformed in the plastic region. Therefore, to analyze and design structures using high-strength steels under dynamic cyclic loading, such as earthquake loading, it is necessary to consider the special dynamic hysteresis model of high-strength steels. In particular, when using finite-element (FE) analysis programs one should use the proper material characteristics for those steels. In this paper, dynamic hysteresis models for standard high-strength steels, with tensile strengths of 600 and 800 MPa, are formulated based on results of tensile tests and low-cycle fatigue tests over a range of strain rates from 10−4−10 s−1. A three-dimensional elastic-plastic finite-element analysis program using a newly formulated dynamic hysteresis model is developed by the writers. Accuracy and validity of the developed finite-element analysis program is verified by correlation of the analytical and experimental results.
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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 | 0.000 |
| 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) | 0.001 | 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".