Development and Validation of a Python-Based Trilinear Analysis Software for RC Beams with CFRP Strengthening
Bibliographic record
Abstract
A trilinear analysis software with a user-friendly interface was developed using Python, based on the trilinear analytical model proposed by Hayder Rasheed, for analyzing reinforced concrete (RC) beams with or without carbon fiberreinforced polymer (CFRP) strengthening.The software offers engineers and researchers a robust tool to simulate RC beam behavior under three loading types: concentrated, two-point, and uniform loads.It provides detailed outputs, including moment-curvature relationships, load-deflection responses, failure modes, and ultimate load capacities.Advanced features include the ability to evaluate various failure scenarios such as concrete crushing, debonding, and rupture, depending on strain limits, as well as comprehensive parameter control for material and geometric properties.The software accuracy was verified against Rasheed's analytical model, and its reliability was validated using experimental data.The tool not only predicts the behavior of RC beams with high precision but also aids in optimizing their design and assessing strengthening requirements.This software represents a significant contribution to engineering practices, research, and education, enabling efficient and accurate analysis of RC beams with or without CFRP strengthening.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".