Reliability-Based Analysis of Internal Limit States for MSE Walls Using Steel-Strip Reinforcement
Bibliographic record
Abstract
This paper demonstrates reliability-based analysis of tensile strength and pullout limit states for mechanically stabilized earth (MSE) walls constructed with steel-strip reinforcement. Five different reinforcement tensile load models, three different pullout models, and one tensile strength model were examined. The accuracy of each model was assessed probabilistically using bias statistics in which bias was the ratio of the measured value to the predicted value. The tensile limit state included uncertainty in the tensile strength due to variability in original strength of the steel and variability in potential loss of strength due to corrosion. Reliability-based analyses were carried out considering the accuracy of the load and resistance models that appear in each limit state equation plus uncertainty due to the confidence (level of understanding) of the engineer at the time of design. The reliability index was computed using Monte Carlo simulation of the tensile strength limit state and a convenient closed-form solution that is easily implemented in a spreadsheet for the pullout limit state. A MSE wall example was used to demonstrate the general approach and to compare margins of safety using different load and resistance model combinations and reinforcement strips of different initial thickness.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".