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Getting BIM Right for Carbon

Practical Guidance for ORIS open BIM Module

The accuracy of your carbon assessments is only as good as the BIM data behind it.
ORIS open BIM Module is designed to automate Material Assessments directly from BIM models, but it does not interpret intent.
It reads explicit quantities and material information exactly as they exist in the file.

If one of core pillars (categorisation of elements, element quantities, quantity units and element materials) is missing or inconsistent, the workflow breaks down.
This guide explains how to structure BIM data so ORIS open BIM Module can extract and classify element quantities reliably and map them to materials with minimal manual intervention.


Categorisation: Structuring the Bill of Quantities
Quantities: The Foundation of Carbon Assessments
Materials: Enabling Accurate Carbon Mapping
Bonus Tip: Keep Models Lean

Categorisation: Structuring the Bill of Quantities

To map quantities to carbon data, ORIS requires model elements to be grouped into structured categories. These categories form the basis of the Bill of Quantities (BoQ) and are essential for the Material Assessment. Without them, models with thousands of objects become a manual mapping bottleneck, making efficient and accurate analysis impossible.

Property-based Categorisation

ORIS open BIM Module utilizes a property-based categorisation schema, supporting a hierarchy of up to two levels. It is critical to note that ORIS does not infer structure from physical geometry or visual groupings; hierarchy exists only where it is explicitly defined within the property sets.

Best practices
  • Use properties that are populated across the entire model, with consistent naming across elements
  • Keep categorisation lean, over-classification increases noise and mapping effort

The Goal:
High-quality categorization collapses thousands of individual objects into manageable, functional groups. This dramatically reduces redundant data rows and accelerates the assessment timeline.

Quantities: The Foundation of Carbon Assessments

Material Assessments in ORIS start and end with quantities.
Geometry alone is not enough: each element must expose at least one usable quantity as an object property.

What ORIS requires
  • A consistent quantity per element category (e.g. all the same concrete element types measured in volume)
  • Quantities embedded as readable properties, not implicit geometry
  • Clear and consistent units

Common issues observed

  • Volumes exist in authoring software but are not retrievable in ORIS
  • Quantities generated by scripts or add-ons but not attached to elements
  • Mixed quantity logic within the same element type

ORIS provides visual indicators next to the mapped quantities of a particular group of elements:

  • Green: quantity present and consistent
  • Yellow / orange: partial or inconsistent quantity data

Materials: Enabling Accurate Carbon Mapping

Materials are what ultimately drive carbon results. ORIS can map materials to the quantities manually or using AI-assisted mapping, but both approaches depend on how clearly material intent is expressed in the BIM file.
Minimum requirement:
At least one property that indicates material type, consistent across same element types, shall be used to categorise the elements. 
Strongly recommended
  • Standardize material naming across elements to minimise the number of mapped rows.
  • Introduce additional granularity if desired, e.g., distinguishing surface layers, sub-base materials, or reinforcement types, depending on the level of detail needed for the carbon assessment.
  • Alignment between BIM material naming and ORIS material libraries.

The richer and more consistent the material metadata, the more effective AI mapping becomes, and the less manual intervention is required.

Bonus Tip: Keep Models Lean

Model for carbon, not coordination.
Keep only elements that directly drive quantities and materials used for the carbon assessment, and remove surplus detail that adds weight without value.
Manage different disciplines separately rather than relying on a federated model: this reduces file size, limits irrelevant data, and makes quantity and material mapping faster, clearer, and more robust in ORIS.