Calibration of ABAQUS Concrete Damage Plasticity (CDP) Model for UHPC Material
Bibliographic record
Abstract
Ultra-high performance fiber reinforced concrete (UHPFRC) is an advanced cementitious material with exceptional mechanical properties including, a compressive strength in the range of 120 to 200 MPa, a tensile strength up to 15 MPa with a hardening post-peak behavior, significant durability, and ductility. Due to their speed, affordability, and versatility in providing numerous results options, finite element (FE) simulations can be used to analyze various structural systems under different loads (e.g., mechanical, thermal, coupled field). Of the commercially available software, ABAQUS has been widely used to simulate the behavior of concrete members. The concrete damage plasticity (CDP) model is the flagship and only constitutive model in ABAQUS applicable for representing the brittle nature, cracking, and crushing failure in concrete-like materials. As the model inputs have been exclusively developed and calibrated for conventional concrete, they might not be applicable to UHPC. Particularly the model inputs related to shear and tension behaviors might differ between conventional concrete and UHPC, where aggregates present in the former provide shear mechanical interlock, lacking in the latter, while fibers in the latter provide tensile bridging effects, lacking in the former. This study aims to calibrate the various parameters of CDP model, including the dilation angle, eccentricity, stress ratio, stress-strain curve for tension and compression, for UHPC, using a large database of experimental results. Recommended values and ranges are provided in the manuscript to enable accurate analysis of UHPC members in ABAQUS.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".