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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".