PARAMETER IDENTIFICATION OF CONSTITUTIVE MODEL FOR HARD ROCK UNDER HIGH IN-SITU STRESS CONDITION USING PARTICLE SWARM OPTIMIZATION ALGORITHM
Bibliographic record
Abstract
Particle swarm optimization(PSO) algorithm is a stochastic global optimization technique and has become the hotspot of evolutionary computation because of its excellent performance and simplicity for implementation.In light of the fact that it is hard to determine the parameters of a constitutive model—cohesion weakening and frictional strengthening(CWFS) model,which performs excellently in modeling the extent and depth of brittle failure zone for hard rock under high in-situ stress condition,a new method is presented to identify parameters of CWFS model using PSO.At first,the stochastic values of parameters are initialized and the difference in failure zone between the value computed and the datum measured is regarded as fitness value to evaluate quality of the parameters.Then the parameters are updated continually using PSO until the optimal parameters are found.Thus parameters are identified adaptively during computation.The results of applications to two real tunnels,i.e.,Mine-by tunnel in Canada and Taipingyi tunnel in China,show that the method is feasible and efficient for identifying constitutive parameters and predicting the extent and depth of brittle failure of hard rock under high in-situ stress condition with high precision.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".