Quantitative estimation of TBM disc cutter wear from in-situ parameters by optimization algorithm improved back-propagation neural network: A case study of a metro tunnel in Guangzhou, China
Bibliographic record
Abstract
Unplanned worn disc cutters maintenance can cause casualty and financial loss. Good knowledge of cutter wear will facilitate the design of the excavation plan and elongate cutter life. Cutter wear is subject to multiple influential factors, which can be regarded as a nonlinear multivariate question. Back propagation neural network (BPNN), a robust machine learning method in this field, can shed light on it. A shield tunneling section from Metro Line 18 in Guangzhou, China, only encounters hard rock strata. There are 49 manually measured cutter wear. The tunnel boring machine records over 250 types of parameters per second with a real-time logging system. According to 28 types of influential parameters from previous studies, 14 input parameters are selected to reflect the effect of machine, geology, and operation on the output, cutter wear, which is quantified as the average radial reduction of cutter ring. By extrapolation and interpolation, a dataset with 1434 samples is established from the Pan-nan section. Cutter wear is distributed to each ring within the inspection section with a published model. To overcome the inherent weakness of BPNN, we apply SMBO (Sequential Model-based Optimization) and GA (Genetic Algorithm) and compare their effectiveness. SMBO and GA returns model with R2 of 0.968 and 0.971. Error tracing reveals GA model tends to overestimate records with slight wear.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".