Tag: precedents

Posted on

Crowd-sourced building-performance data

Here’s an idea that I would like to throw out into the solar systems and see if anyone can do something with it. 

I was writing yesterday about post-occupancy amnesia — how little attention we, as an industry, pay to how buildings actually perform once they’ve been built. And this got me thinking: what if we could crowdsource that data?

Think about how Google Maps works. It aggregates large amounts of data provided my millions of users to understand traffic flows and levels of occupancy of different location. All from data that individuals give Google permission to aggregate. 

What if we could do something similar for building performance?

Many of our devices already capture data on location, movement and temperature. I imagine they can also collect data on noise and light levels. If enough people opted in it might be possible to gather data on how buildings are actually performing, eg: 

  • How many people are in a building, in what areas and when
  • How they move through spaces
  • What temperatures they experience
  • Light, sound and air quality. 

Triangulated with health data (with the right safeguards) we might see new patterns emerge. Patterns of how the complex systems of people in buildings actually behave. What we learn from these lag indicators can become lead indicators for the buildings we propose for the future might perform. 

Of course, there a big questions. What’s in it for the user? Why would people opt in?

And there are precedents. The Zoe Health Study in the UK gathered huge amounts of data from volunteers who signed up because there was a clear, public health need. Energy use and building performance might not feel as immediate, but as the energy crisis deepens, and we become more concerned about whether our buildings make us healthier or not, this might change. 

And maybe it can start with a smaller group. Maybe a community of building nerds using such an app would give us much more insight than we have now. 

Every building is an experiment. It’s up to us whether we pay attention to the results.

Posted on

Designers tell the future (part 2)

Yesterday, we looked at how the Gothic cathedral architects of northern France used precedent to guide what could be built next.

But what happens when there’s no precedent?

When Antoni Gaudí was designing the Sagrada Família in Barcelona, there was no precedent for the complex geometries he wanted to build. So, he created a model: using hanging chains and sandbags to mimic the geometry and loading of the cathedral’s roof.

This physical model acted as a lead indicator, giving Gaudí insight into whether his structure would stand up. When there’s no precedent, you can’t ask “does that look right?”—because you’ve never seen it before.

The reliability of this kind of lead indicator depends on the accuracy and appropriateness of the model. Selecting or creating the right model improves with training and experience.

Engineers build models all the time. In fact, every engineering calculation is a model of the future. A structural stability calculation gives us a lead indicator about whether a structure is going to stand up. Engineers work very hard to make sure these models are as accurate as possible.

But they are still just models. The truth comes after the fact: did the building actually stand up? That’s the lag indicator. And in those rare cases where something goes wrong, this new knowledge gets fed back into better models for the future.

Thankfully, very few buildings in the UK fall down due to bad modelling. That’s because this feedback loop—between model, reality, and revised model—is quite advanced.

But what about the other areas of engineering where we don’t close the loop?

That’s a question for tomorrow.