Bibliographic record
Abstract
This paper presents a more realistic and comprehensive static analysis technique for structures having non-prismatic members. In the proposed method a general stiffness matrix for non-prismatic members that is applicable to Timoshenko beam theory has been derived. The stiffness coefficients have been determined for constant, linear, and parabolic height variations of members, employing analytical and (or) numerical integration techniques. Uniform, triangular, and trapezoidal distributed loads over the entire member or along any part of it, concentrated loads, moments at points on the member, and any of these load combinations are taken into consideration to determine the fixed-end forces. A computer program has been coded in Fortran which analyses two-dimensional frames using the proposed stiffness matrix and fixed-end forces for a wide range of external loads. The fixed-end forces may include the effect of shear deformations. The importance of the shear deformations in non-prismatic members with high depth-to-span ratios is shown using numerical examples. The accuracy of the proposed analysis technique is verified by comparing the results of the numerical examples with those obtained from the general analysis program SAP90 using a large number of subelements. Key words: computer programs, fixed-end forces, loads (forces), non-prismatic (tapered), shear deformations, stiffness, structural analysis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".