Predictive and Diagnostic Analysis of Shield Cutter Head Failures in Tunnel Construction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Shield cutter head failures exist in almost every tunnel project. A systematic approach that incorporates dynamic fault tree (DFT) and discrete-time Bayesian network (DTBN) is developed in this research, in order to facilitate the analysis for shield cutter head failures in tunnel construction. A causal network model is built to simulate the behaviors of shield cutter head failures over time during the tunnel boring machine (TBM) operation. One of the tunneling projects recently completed in the Wuhan metro system in China is selected as a case study to demonstrate the applicability of the developed approach. Results indicate that the developed approach is capable of predictive analysis for the estimation of the TBM performance, and diagnostic analysis in case a low performance or a failure is observed. This approach provides a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions in a complex project environment.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it