An innovative formulation for predicting the punching shear behavior in two-way reinforced concrete slabs
Bibliographic record
Abstract
Punching shear failure represents one of the most critical and perilous challenges that slabs may encounter under load-bearing conditions. Numerous studies have delved into the mechanics of punching shear and the methods for assessing the strength of slabs against punching shear failures. However, owing to the inherent complexity of the punching shear phenomenon, a universally applicable relationship has remained elusive. This article introduces a mathematical framework for analyzing the punching shear strength of two-way reinforced concrete slabs. The framework leverages a dataset of 218 laboratory test results compiled from various literature sources. To achieve the objective, the authors preprocessed the database, optimized the computational architecture, established the computational structure, and extracted mathematical relationships from the resulting system, respectively. The punching shear values generated by the computational model presented in this article were also compared with those determined using existing relationships. The framework surpasses existing methods by achieving a demonstrably lower error rate in predicting punching shear strength. This translates into a significant advantage for engineers, enabling them to design two-way reinforced concrete slabs with greater confidence and accuracy. Furthermore, it can be a valuable tool for assessing the viability of strengthening strategies for existing slabs or guiding rehabilitation efforts to ensure structural integrity. By facilitating these applications, the proposed framework holds immense promise for enhancing the safety, reliability, and lifespan of two-way RC slabs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".