LIVEdataset aec-bench@releasetasks 552models 18last submission … · built …
mechanicalwith-tool

Distance Attenuation

Calculates the free-field sound pressure level at a target distance from a known reference level and distance. The template uses inverse-square geometric spreading, expressed as L2 = L1 - 20 log10(r2/r1), for deterministic first-pass acoustic propagation checks.

with-tool: The model is given an executable Python calculator script.

Standards

ISO 9613-2

Tags

acousticsdeterministicdistance-attenuationmechanicalsound-pressure-level

How this task is generated

One template produces many comparable benchmark tasks while keeping the scoring contract fixed.

  1. 01

    Template

    The reusable contract shown on this page.

  2. 02

    Scenario

    An archetype and site context are sampled.

  3. 03

    Difficulty tier

    Inputs may be hidden at harder tiers.

  4. 04

    Task prompt

    The model responds with the declared outputs.

Parameters

Inputs the model receives, and the outputs it is scored on.

Inputs

3 inputs

Always given

Included directly in every task prompt.

3

  • Reference spl

    reference_spl_db

    Sound pressure level at the reference distance

    40 – 130 dB

  • Reference distance

    reference_distance_m

    Reference distance from the point source

    0.5 – 50 m

  • Target distance

    target_distance_m

    Target distance from the point source

    1 – 500 m

Scored outputs

3 outputs

Distance ratio

distance_ratio

Distance ratio r2/r1

Scores if within ±3% of the reference value.

Attenuation db

attenuation_db

Geometric spreading attenuation

Scores if within ±3% of the reference value.

Target spl db

target_spl_db

Sound pressure level at the target distance

Scores if within ±3% of the reference value.

Difficulty

Each template is sampled at three tiers. Harder tiers may hide inputs, forcing the model to infer them from the scenario description.

All inputs remain visible at every tier

For this template, difficulty scales through parameter and scenario ranges rather than hidden information.

easy:
All parameters given for nearby plant equipment
medium:
All parameters given across plant and industrial noise scenarios
hard:
All parameters given for longer-distance environmental noise checks

Task bundle

The exact instruction and parameter contract used to generate this task, pinned to the published library source.

/workspace

  • instruction.md
  • distance-attenuation_calc.py

Teal lines show Jinja input conditions, not task visibility policy. A line renders only when that input or tool is visible.

1You are a senior mechanical engineer specializing in building services acoustics.2 3## Problem4 5Calculate the sound pressure level at a target distance from a point source using inverse-square distance attenuation.6 7## Given8 9| Parameter | Value | Unit |10|-----------|-------|------|11| Reference SPL L1 | {{ reference_spl_db }} | dB |12| Reference distance r1 | {{ reference_distance_m }} | m |13| Target distance r2 | {{ target_distance_m }} | m |14 15{% if archetype_description is defined %}16### Acoustic Context17 18{{ archetype_description }}19{% endif %}20 21{% if tool_available %}22## Available Tool23 24A distance attenuation calculation tool is available at `/workspace/{{ meta.name }}_calc.py`. Run it with:25 26```bash27python3 /workspace/{{ meta.name }}_calc.py --help28```29 30You may use this tool to verify your calculations or compute values directly.31{% endif %}32 33## Required34 35Calculate the following:36 371. Distance ratio r2/r1382. Geometric spreading attenuation (dB)393. Sound pressure level at the target distance (dB)40 41## Constraints42 43- No internet access is available. Work from engineering knowledge and the provided tool.44- Treat the source as a point source in free-field conditions.45- Use attenuation = 20 log10(r2/r1).46- Use L2 = L1 - attenuation.47- Do not add ground absorption, barrier, meteorological, facade, or directivity corrections.48 49## Output Format50 51Show your step-by-step working in Markdown. At the end of your solution, include a JSON block with your final answers in exactly this format:52 53```json54{55 "distance_ratio": <numeric_value>,56 "attenuation_db": <numeric_value>,57 "target_spl_db": <numeric_value>58}59```60 61Write your complete solution to `/workspace/output.md`.62

Scenario archetypes

Each generated task is drawn from one of these realistic scenario bands.

Site contexts ground each scenario in a real locale the model can use to infer hidden values.

Plant room equipment

plant_room_equipment

Mechanical plant item assessed at occupied-area setback

hospital-plant-roomcommercial-rooftop-plant
Parameter ranges
reference_spl_db
75 – 100
reference_distance_m
1 – 3
target_distance_m
5 – 50

Industrial fan

industrial_fan

Industrial fan or blower assessed at site boundary

water-treatment-plantindustrial-process-building
Parameter ranges
reference_spl_db
85 – 115
reference_distance_m
1 – 10
target_distance_m
25 – 250

Construction noise

construction_noise

Temporary construction equipment assessed at a receiver

urban-construction-siterail-corridor-works
Parameter ranges
reference_spl_db
80 – 120
reference_distance_m
5 – 15
target_distance_m
20 – 500

Example task

water-treatment-plant-industrial-fan-preview — hard difficulty, all inputs given.

Industrial fan or blower assessed at site boundary. water-treatment-plant. Required outputs: distance_ratio, attenuation_db, target_spl_db

The model sees

Scenario context and visible inputs.

reference_spl_db
85 to 115 dB
reference_distance_m
1 to 10 m
target_distance_m
25 to 250 m

Executable tool: distance-attenuation_calc.py

The model must infer

Inputs withheld at this difficulty.

Nothing. All inputs are supplied.

The model must produce

The scored JSON answer schema.

{
  "distance_ratio": <number>,
  "attenuation_db": <number>,
  "target_spl_db": <number>
}
  • distance_ratio · scored within ±3%
  • attenuation_db · scored within ±3%
  • target_spl_db · scored within ±3%