Paving Integrated Photovoltaic Technology: Numerical Investigation of Fatigue Performance and Design Strategy
Bibliographic record
Abstract
To elucidate the fatigue damage evolution of solar road panels under long-term loading and enhance their structural durability, this study develops a particle-based discrete element model and simulates fatigue responses under different structural configurations and loading rates. A strength degradation index was established by introducing peak stress and terminal stress, enabling quantitative evaluation of strength deterioration. Combined with fracture evolution, the dominant mesoscopic damage mechanisms were revealed. The results indicate that structural configuration strongly influences fatigue performance, with square panels showing the best resistance due to geometric symmetry and stable boundary constraints. Loading rate regulates damage evolution: lower rates promote structural coordination but may delay cumulative failure, while higher rates suppress overall deformation yet increase localized fracture risk. Based on these findings, a nonlinear predictive model of the strength degradation rate was constructed (R2 = 0.935), offering reliable support for structural life prediction and design optimization. Finally, fatigue-resistant design strategies are proposed, including optimal structural configuration, controlled loading rates, bonding enhancement, and integration of online monitoring—providing both theoretical and technical guidance for high-performance, long-lifespan solar road systems.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".