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

Handling Capacity

Calculates the five-minute handling capacity of a lift group from building population, round-trip time, car capacity, number of lifts, and car loading factor. The reduced method reports passengers carried in five minutes and capacity as a percentage of population.

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

Standards

CIBSE Guide D

Tags

deterministicelectricalhandling-capacityliftsvertical-transportation

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

5 inputs

Always given

Included directly in every task prompt.

4

  • Building population

    building_population

    Population served by the lift group

    10 – 20000

  • Round trip time

    round_trip_time_s

    Lift round-trip time

    20 – 600 s

  • Car capacity persons

    car_capacity_persons

    Rated lift car capacity in persons

    2 – 40

  • Lift count

    lift_count

    Number of lifts in the group

    1 – 16

Hidden at higher difficulty

Visible in easier tasks and withheld in one or more harder tiers.

1

  • Car loading factor pct

    car_loading_factor_pct

    Effective car loading factor

    Hidden at hard difficulty.

    30 – 100 %

Scored outputs

2 outputs

Passengers per 5min

passengers_per_5min

Passengers transported in five minutes

Scores if within ±3% of the reference value.

Handling capacity pct

handling_capacity_pct

Five-minute handling capacity as population percentage

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.

easy

All inputs given

Residential group with all inputs visible

medium

All inputs given

Residential or office lift group

hard

Some inputs hidden

Loading factor hidden in traffic design context

Hidden inputs

  • Car loading factor pctcar_loading_factor_pct

Prompt replacement text

Use the car loading factor implied by {{ archetype.description }}.

Car loading factor pct restricted to: 80

Task bundle

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

/workspace

  • instruction.md
  • handling-capacity_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 vertical transportation engineer calculating lift handling capacity.2 3## Given4 5| Parameter | Value | Unit |6|-----------|-------|------|7| Building population | {{ building_population }} | persons |8| Round-trip time | {{ round_trip_time_s }} | s |9| Car capacity | {{ car_capacity_persons }} | persons |10| Number of lifts | {{ lift_count }} | count |11{% if car_loading_factor_pct is defined %}12| Car loading factor | {{ car_loading_factor_pct }} | % |13{% endif %}14 15## Constraints16 17- Loaded car capacity equals rated car capacity times loading factor.18- Passengers per five minutes equals `300 x lift_count x loaded_capacity / RTT`.19- Handling capacity percentage equals passengers per five minutes divided by population.20 21## Output Format22 23Include a JSON block with exactly these keys:24 25```json26{27 "passengers_per_5min": <numeric_value>,28 "handling_capacity_pct": <numeric_value>29}30```31 32Write your complete solution to `/workspace/output.md`.33

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.

Residential group

residential_group

Residential lift group

apartment-coremixed-use-tower
Parameter ranges
building_population
100 – 1500
round_trip_time_s
60 – 220
car_capacity_persons
6 – 18
lift_count
1 – 6
car_loading_factor_pct
50 – 80

Office group

office_group

Commercial office lift group

office-towercampus-office
Parameter ranges
building_population
500 – 10000
round_trip_time_s
90 – 300
car_capacity_persons
12 – 28
lift_count
4 – 12
car_loading_factor_pct
70 – 90

Example task

office-tower-office-group-preview — hard difficulty, some inputs hidden.

Commercial office lift group. office-tower. Required outputs: passengers_per_5min, handling_capacity_pct

The model sees

Scenario context and visible inputs.

building_population
500 to 10000
round_trip_time_s
90 to 300 s
car_capacity_persons
12 to 28
lift_count
4 to 12

Executable tool: handling-capacity_calc.py

The model must infer

Inputs withheld at this difficulty.

  • Car loading factor pct

    car_loading_factor_pct

Stand-in text in the prompt

Use the car loading factor implied by {{ archetype.description }}.

The model must produce

The scored JSON answer schema.

{
  "passengers_per_5min": <number>,
  "handling_capacity_pct": <number>
}
  • passengers_per_5min · scored within ±3%
  • handling_capacity_pct · scored within ±3%