Predicting Load–Deflection Response of Enclosed Composite Concrete Beams Using the Mansur Nonlinear Constitutive Model
Bibliographic record
Abstract
This study delivers a study to assess the performance of Mansur's nonlinear constitutive model to predict the load-deflection relationship of encased composite concrete beam experiencing monotonic flexural loading.The model is equipped with post-peak softening and fibre-induced ductility effect.The model was implemented in a hybrid modelling approach of ACI 318-19, considering a full plastic factor of 0.90 and AISC 360-16 provisions.Two full-scale experimental beams from previous literature studies were selected as sample beams for which the Mansur model described their behaviours.The obtained results were benchmarked by using statistical indicators of RMSE, NRMSE, MAPE, R², and Pearson's R to the experimental data and the code-predicted results.The Mansur model could capture the nonlinear stiffness degradation more accurately compared to other code-predicted results, and it is more significantly accurate beyond the cracking stage.The most significant outcome from the study is the accurate prediction of the post-cracking behaviour of Beam Cb. 2 using the Mansur model, yielding RMSE = 6.41 kN and R² = 0.9812.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".