Machine learning model for dynamical response of nano-composite pipe conveying fluid under seismic loading
Bibliographic record
Abstract
Machine learning approaches including support vector regression (SVR) and multi-layer feedforward backpropagation neural network (FFBNN) were used in the present study along with classic theory for predicting maximum displacement of nanocomposite pipe conveying fluid under seismic load. The FFBNN consisted of three layers: 1) three neurons in input layer including length-to-radius ratio (L/R), fluid velocity (V) and volume percent of carbon nanotube; 2) hidden layer with 11 neurons obtained via trial and error; 3) maximum displacement-based seismic load. SVR model was obtained via three-input data with maximum likelihood estimator. Model predicted results were compared using three metrics, including Nash-Sutcliffe efficiency, root mean squared error and coefficient of correlation for 100 testing and 255 training data points. Results indicated that SVR achieved best predictions in the training phase, while FFBNN provided superior prediction in the testing phase. Increasing L/R, V and decreasing VCNT, increased maximum displacements under seismic load.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".