Bibliographic record
Abstract
Vehicle barriers are high security infrastructure systems commonly used for protection of buildings and infrastructure.The global increase in terrorism as well as the need to effectively protect humans and infrastructure has led to various research on improving security infrastructure systems.One of the several alternatives for providing security to infrastructure is the use of anti-ram vehicle barriers such as bollards.A vehicular bollard is a barrier system used to restrict movement of vehicles around buildings.An important factor to consider in the design of a bollard is the impact resistance of the bollard which is dependent on its foundation.Previous studies conducted on foundation systems for bollards mostly focuses on the use of shallow concrete foundation systems.However, a simple deep foundation technology can be employed for bollards with minimum cost.This thesis investigates the use of such deep foundation technology for bollards.An extensive experimental study into innovative foundation systems for vehicle bollards using soil was conducted in the laboratory to evaluate the performance of different pile systems under both static and impact lateral loads.The results demonstrated that part of the bollard can be used as a pile to design a simple deep foundation system which is capable of resisting the vehicular load acting on the bollard.The implementation of Fins on the pile significantly increased the resistance capacity of the foundation under lateral impact loads.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".