Auralisation: A Valuable Consultation and Engagement Tool for Infrastructure Projects – Case Study of Airspace Change for a Regional Airport
Bibliographic record
Abstract
Since the late 1990s, Arup has developed and used auralisation capability to inform the design of some of the world’s best arts and culture venues. Through Arup SoundLabs around the world, clients, designers, major stakeholders and the general public have been able to take informed decisions by experiencing the acoustic implications of designs as they are developed. More recently, auralisation technology has been developed to simulate sound generation and propagation during planning and design for a broad range of infrastructure projects, such as High Speed 2 railway, A66 highway and Heathrow airspace change and expansion in UK; Texas Central High Speed railway and LADoT Advanced Air Mobility (AAM) policy in US; and a wind farm development in Tasmania. The aviation industry is currently introducing new disruptive technologies principally to improve its sustainability performance. The introduction of electric aircraft and delivery drones are likely to revolutionize regional airspace, creating new opportunities for regional airports. Although it is possible to achieve lower noise levels for these new vehicles compared to traditional light aircraft, the sound characteristics (for example tonality and high pitch due to electric motors) and their potential for higher traffic, could give rise to concerns about noise being more noticeable and disturbing to local communities than the current situation. This paper will present the auralisation methodology, successfully applied to a regional UK airport, to address public concerns and assist in the local authority planning process for an airport development masterplan. Through a series of sound demonstrations, members of the public and stakeholders could experience and judge for themselves, the impacts of the proposed airspace and infrastructure changes (such as new types of aircraft, modification of flight paths and increased air traffic).
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".