An innovative formulation for predicting the punching shear behavior in two-way reinforced concrete slabs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it