This is a presentation I gave at last year’s AIA TAP at CIFE. It was a short presentation on some of the potential benefits of mining BIM data and studying it to help emerge some truths, patterns, etc. about our projects, processes and our teams. I showed some specific examples on how we are using some of this data today to make more informed decisions.
Archiving and search
One of the lowest hanging fruit of all this is the idea that if we can access all the information that is stored in these models, we can literally search through our past projects. Imagine being able to search for what doors or what floor to floor height were used on a building you finished 5 years ago, without having to run down to the archives.
It is common for us as industry to use precedents, but most of the time we are limited by relatively obvious attributes in our selection. We use things like typology, project size, client, client type and region as good meta organizers. What if we wanted to take that further? What if we wanted to identify projects by their floor to floor heights? or, what if we wanted to identify projects by the use of some particular piece of mechanical equipment? By that same measure, what could we learn if we could take successful projects and compare them against those considered to be less successful?
When working with large models and large teams, knowing ‘what is in the model’ can be almost impossible to determine. The rate at which people are adding elements is faster than the speed at which we can check them. Our qa/qc tools are not in line with our speed of production. Running model checks against a set performance standard is another big opportunity here. The information is there, we just need to be able to see it in the right form, and it needs to come to us at the right speed. It needs to keep up with production.
Good thinking, Fed! There’s a lot of data in these models which we can’t leverage effectively if we just stick to the usual metadata and other conventional techniques. The user-guided feature recognition methods I’ve been working on can help this through geometric-content-based search and classification of model objects, and can also be extended to include non-geometric data. Are the CASE apps also addressing some of these issues?
Andre, good hearing from you! I remember your research well, even though it’s been a while since I last saw it. I think it was at a coffee shop in the West Village 🙂 Would love to see it in action again. Our approach is mainly focusing on leveraging what is already there more so than making stuff smarter for now, though there is a big market for that out there. My main near term interest is to help disprove a lot of those toxic anecdotal urban myths that keep adding friction to the general progression of a practice. If we can do that, I’m happy.
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