Nonlinear Stress–Strain Characterization of Cast Iron Used to Manufacture Pipes for Water Supply
Bibliographic record
Abstract
The stress–strain response of cast iron under tension or compression is nonlinear. This paper examines how the hyperbolic constitutive law can be applied to characterize nonlinear stress–strain behavior of cast iron used in water supply networks. Procedures are described to obtain parameters of the hyperbolic constitutive law from either the response (data) obtained from simple uniaxial tensile and compressive tests or from bending tests. To demonstrate its applicability, this hyperbolic constitutive law is first applied to data obtained from uniaxial tensile and compressive tests conducted by Schlick and Moore (1936, “Strength and Elastic Properties of Cast Iron in Tension, Compression, Flexure, and Combined Tension and Flexure,” Bulletin 127, Iowa Engineering Experiment Station, Ames, IA). In addition, an approach to extract parameters for the hyperbolic constitutive law from bending (beam and pipe rings) tests is proposed and subsequently applied to tests conducted by Talbot (1908, “Tests of Cast-Iron and Reinforced Concrete Culvert Pipe,” Bulletin No. 22, University of Illinois, Urbana, IL). This latter approach is attractive for practical purposes because the test set up is simple and the test coupons are very easy to prepare. The hyperbolic constitutive law in conjunction with maximum normal strain theory as proposed by St. Venant (Collins, J. A., 1993, Failure of Materials in Mechanical Design: Analysis, Prediction, Prevention, John Wiley, New York, NY) was also used to predict failure loads.
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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 | 0.001 | 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".