Support-vector-machine-regression assisted methodology for the design-for-reliability of tapered composite tubes
Bibliographic record
Abstract
The structural performance of load-carrying composite structures is significantly influenced by uncertainties in material properties, necessitating reliability quantification and design-for-reliability approaches. Traditional structural reliability evaluation methods, however, often involve high computational costs, limiting their practical use. To overcome this challenge, the present work presents a novel methodology that integrates Support Vector Machine Regression (SVMR), Monte Carlo Simulation (MCS), and the Finite Element Method (FEM) to efficiently assess the structural reliability of tapered composite tubes under axial loading, explicitly accounting for uncertainties in material properties and ply thickness. An approximate-analytical solution based on the Donnell-Mushtari-Vlasov shell theory is developed to predict axial deformation and is used to validate the finite element model. Additionally, the finite element model and approximation-analytical solution are validated against a closed-form analytical solution and experimental results available in the literature, ensuring the accuracy and reliability of the approach. The proposed structural reliability evaluation methodology demonstrates accuracy and computational efficiency I reliability evaluation through comparisons with the direct Monte Carlo Simulation method. Reliability analysis quantifies the influence of random variables on structural response, revealing that designs based solely on mean material properties result in approximately 50% reliability, indicating a 50% probability of failure. Moreover, the taper angle exerts a negligible influence on structural reliability indices, highlighting a key design consideration. This integrated framework provides a computationally efficient and validated tool for the design-for-reliability of tapered composite tubes, enabling broader applications in composite structural engineering.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".