Oversize-Overweight Vehicles: An Investigation into the Safety and Space Requirements for Alternative Energy Products
Bibliographic record
Abstract
The use of oversize/overweight vehicles for the transportation of components and equipment for alternative energy projects is on the increase in North America. A system of High Load corridors has been developed in response to requirements to move over-dimensional equipment machinery, and preassembled components form manufacturing centers in central and southern Alberta to the Oil sands plants in northern Alberta. The High Load corridor allows loads up to 9m high and widths up to 9m. For the wind energy sector as an example, wind turbine component transportation planning is carried out in a reactive manner where adjustments to road furniture are made during the transportation of the heavy or abnormal load. There is a need to develop a proactive approach which allows planners and designers to effectively design road infrastructure with the manoeuvrability of specialized vehicles in mind. This paper examines key safety and road geometry aspects (for interchanges and intersections) of the current wind turbine component transportation planning process e.g. initial route identification, identification of physical obstacles, headroom restrictions, horizontal and vertical curves on existing roads and infrastructure. It also describes other considerations for wind turbine transportation planning which would typically impact the normal usage of the road e.g. road closures and temporary relocation of signage or static objects. The paper suggests a framework for future research initiatives on oversize vehicles e.g. the gathering information on the typical oversize vehicle for wind turbine component transportation and studies on the vehicle's turning radius and swept path. For the covering abstract of this conference see record control number 201111RT334E.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".