Development of a computational multi-physical framework for the use of nonlinear explicit approach in the assessment of concrete structures affected by alkali-aggregate reaction
Bibliographic record
Abstract
This paper proposes an innovative methodology for the use of the explicit approach in the assessment of concrete structures affected by alkali-aggregate reaction (AAR).Efficiency of the explicit approach has been proven in previous works for the case of large concrete structural models with high degree of nonlinearity.In the proposed methodology, the strain is decomposed into mechanical, thermal, creep, shrinkage and AAR strain components.The AAR component is computed according to Saouma and Perotti model [1] for the anisotropic distribution of the volumetric expansion and according to Larive model [2] for the AAR kinetics.One advantage of the approach is that it can be used with any existing concrete model that has undergone a rigorous verification and validation (V&V) process for the mechanical part.In this work the EPM3D concrete model [2] implemented as a user-subroutine in Abaqus-Explicit is used.The general methodology is based on three different finite element analyses: thermal implicit, hygral implicit and the final nonlinear multiphysical explicit analysis.An innovative formulation to address the problem of time scale difference between the implicit and explicit approaches is presented.A new incremental numerical formulation is presented to correctly handle the dependency of the AAR kinetics on the temperature field in case of cyclic temperature variation.A verification example is presented at the material level, along with qualitative assessment of the cracking pattern of an existing hydraulic structure affected by AAR.This last application at structural level demonstrates the efficiency of the suggested methodology and the feasibility within an industrial context.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".