An Asymptotic Solution for Evaluation of Stresses in Balanced and Unbalanced Adhesively Bonded Joints
Bibliographic record
Abstract
Adhesively bonding is one of the most commonly and widely used joining methods in various engineering applications. Many fiber-reinforced plastic (FRP) structural components nowadays are joined by adhesives. As a result many researchers have expended considerable effort in developing analytical solutions and computational procedures to assess the stress distribution in such joints. Most of the works however have considered joints that are balanced, formed with a thin layer of adhesive, mainly useful in characterizing joints in aerospace structural applications. However, in many applications, especially in marine and civil infrastructure applications, the adhesive layers are relatively thick, and the joints are usually unbalanced. Therefore seeking an accurate and robust analytical solution for characterizing such adhesively bonded joints is desirable. In this paper, an analytical closed-form solution is developed based on the asymptotic method, using the assumptions laid out by earlier researchers (e.g., Goland and Reissner and others). The solution is capable of characterizing the stress distribution in balanced and unbalanced joints with a thin or thick layer of adhesive. The integrity of the solution is verified by the finite element method.
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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".