Energy conversion of NPR protective net under high-energy impacts
Bibliographic record
Abstract
The rapid expansion of infrastructure and transportation networks in the mountainous regions of Southwest China imposes significant challenges in mitigating rockfall-related geological hazards, posing threats to human life and engineering safety. In recent years, the frequency and intensity of high-energy rockfall hazards have escalated. To address these challenges, the design and failure mechanisms of protective nets under extreme impact must be tested and explored; however, many commercially available materials are insufficient to meet these demands. This study investigates the performance of Negative-Poisson’s-Ratio (NPR) cables made of high-strength, high-ductility steel as a new material for protective nets under repeated impact experiments for ultra-high-energy rockfall protection in Sichuan Province, China. This study aims to understand the energy transformation characteristics of protective nets under ultra-high-energy impacts. The experiment uses high-speed cameras, infrared thermography, and dynamic axial force sensors to monitor the deformation process. A comprehensive analysis is conducted on the ability of the net to absorb and dissipate energy, focusing on material deformation, thermal effects, and internal energy. Building on this, a multi-degree-of-freedom system is modeled from the perspective of structural mechanics to describe the interaction between the impactor and the protective net. A physical equation for energy transformation under repeated impacts is derived, offering theoretical support and insights for optimizing protective net structures and their applications in extreme conditions.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".