Modelling Reinforced and Prestressed Concrete Structures Subjected to Shear and Torsion
Bibliographic record
Abstract
In the design and analysis of reinforced and prestressed concrete structures, engineers are often faced with complex loading conditions that require the assessment of members subjected to shear and torsion. To develop a better understanding of the behaviour of such structures, twelve shell element tests were conducted to investigate the influence of combined loads, reinforcement ratios and concrete strength on shear and torsion performance. Ten of the tests were subjected to combinations of in-plane shear and out-of-plane shear in addition to torsion, flexure and biaxial loads. The remaining two tests, cast from high-strength concrete, were subjected to combinations of in-plane shear and biaxial stresses. This thesis then presents five simplified analysis methods, based on the Modified Compression Field Theory, that can be used to model the behaviour of reinforced and prestressed concrete structures. Shell II-S, a three-layered sectional model capable of predicting the response of shells subjected to the eight stress resultants, is presented. Based on Shell II-S, a simplified three-layered finite element model for shells is presented, the method is called Shell II. These techniques are then used to inform the development of simplified design and analysis equations in the context of the Canadian shear design provisions. To assess the nonlinear response of beams subjected to the six stress resultants, a companion method to Shell II is presented, it is called VAST II. The finite element program is based on the variable angle space truss model for beams and can be used to rapidly model the full nonlinear response of structures in three-dimensions. Finally, a simplified calculation process, called the Single Element Method, is presented where a single membrane element is used along with simplified calculations to model the shear behaviour of slender and deep beams.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".