aecdata — An Open Source Python Library to do more with Construction Data

There’s so much data. There’s so much technology. Can’t we use data to reach out climate targets?

The construction industry is not the most data-mature industry. There’s multiple reasons for that:

• Concerns around data sharing
• Analogue processes (more here)
• Informal decision making
• Long timelines of projects
• Start-from-scratch mentality (…and a solution here)
• Lack of interoperability (although Speckle is making leaps there)
• Lack of structured data (2050 Materials is fixing this)

The real reason this is a problem

At the same time, the AEC industry is under the magnifying glass due to it’s inordinate impact on global emissions, and overall impact on climate change. Designers, engineers, consultants and contractors have to suddenly answer their client how they’re reducing the impact of their new project.

And everyone (almost) is trying.

Currently, a big focus (justifiably) is on carbon data. There’s 2 types of carbon data:

1. Energy-related carbon — how much energy is used to keep the building warm/cool and how much CO2 is emitted to create that energy
2. Material-related carbon — how much CO2 is in each brick/plasterboard/window/door/chair which goes in the building?

The energy related carbon has been around for a while. There’s countless solutions to model and optimise the building’s energy performance (see Cove.Tool).

However, when it comes to material-related carbon, 99.99% of the industry uses sub-par software to manually input their BoM (bill-of-material) and calculate a carbon footprint of their building.

The problem with this approach (as we’ve written about before here and here and here and here) is that a Life Cycle Assessment (LCA) is usually generated after the biggest decisions have already been made around the structural materials, shape, height and basic architectural materials (e.g. the envelop), which by definition makes them a retrospective analysis, and not an exercise meant to drive design decisions.

There is a future out there however, where material and product data is used for analysis, insights, optimisation, decision making and more.

In fact, we’re building that future at 2050 Materials.

Introducing our open-source Python Library aecdata.

Besides structuring and offering product and material data through our APIs at 2050 Materials, we have now launched an open source Python library named aecdata .

That’s all we’re saying for now.

Stay tuned, or pip install aecdata