A hybrid GMDH neural network and logistic regression framework for state parameter–based liquefaction evaluation
Bibliographic record
Abstract
The cyclic stress or liquefaction behavior of granular materials is strongly affected by the relative density and confining pressure of the soil. In this study, the state parameter accounting for both relative density and effective stress was used to evaluate soil liquefaction potential. Based on case histories along with the cone penetration test (CPT) database, models for calculating the state parameter using a group method of data handling (GMDH) neural network were developed and recommended according to their performance. The state parameter was then used to develop a state parameter–based probabilistic liquefaction evaluation method using a logistic regression model. From a conservative point of view, the boundary curve of 20% probability of liquefaction was suggested as a deterministic criterion for state parameter–based liquefaction evaluation. Subsequently, a mapping function relating the calculated factor of safety (F S ) to the probability of liquefaction (P L ) was proposed based on the compiled CPT database. Based on the developed P L –F S function, a new risk criterion associated with the state parameter–based design chart was proposed. Finally, a flowchart of state-based probabilistic liquefaction evaluation and quality control for ground-improvement projects was presented for the benefit of practitioners.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".