Thermal effects on the tensile behavior of geosynthetic reinforcements
Bibliographic record
Abstract
Investigating the influence of temperature on the mechanical performance of geosynthetic reinforcement has become increasingly important for the structural analysis and design of reinforced earth structures, especially in the context of intensifying climate change.Understanding these effects is crucial for enhancing infrastructure resilience by accurately evaluating how temperature variations affect the tensile strength and stiffness of geosynthetics.Although numerous studies have examined the impact of temperature on geosynthetic tensile properties, the limited availability of temperature-controlled test data remains a challenge.Furthermore, the absence of engineering tools to predict temperature-dependent behavior highlights the need for reliable predictive models.To address this gap, this research employs both regression analysis and numerical simulations to evaluate the thermal response of commonly used geosynthetics.The study incorporates a thorough review of the tensile behavior of various geosynthetic reinforcements across a wide temperature range.Drawing on nine datasets from previous investigations, two regression models, the hyperbolic model and the modified power law model, were adopted.The relationship between temperature and the model coefficients was then analyzed to approximate the behavior of geosynthetic reinforcements under varying thermal conditions.An illustrative prediction based on these findings is provided.Finally, recognizing the limited availability of experimental datasets, this research employed finite element analysis (FEA) with the elastic-plastic model and the DSGZ constitutive model in ABAQUS to generate additional, reliable data for further regression model development.The sensitivity analysis of model coefficients revealed that developing a predictive model for general polymeric reinforcements faced challenges, due to the inherent limitations of the available datasets.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".