Recently I’ve tutored a Processing workshop for the Hyperbody department at the TU Delft. I’ve decided to share the sketches with the open world – who knows, maybe someone will find them useful. There’s quite a few variations included – clustering, attractors, collision avoidance, etc. Read below for the download link and some screens of some of the sketches.

2d and 3d clustering:

Swarms interacting:

Attractors and structures:

Collision avoidance:

Here’s a .zip file containing everything – including some Grasshopper sketches which allow for some basic interchange between Rhino and Processing via standard text files.

A nifty little tool for creating connectors between pretty much any kind of planar surfaces which intersect themselves at the edges.

What it basically does is to output in a ordered structure the intersecting faces (as tree branches) and their intersection line. From there on you can continue to build up your own connectors however you like.

The example file contains the Grasshopper definition, Connector-Notch Script and an example Rhino file. Read below for more.

The workflow is as follows:

1. Select the faces you want to make the connectors for and input them into the Grasshopper definition.
2. Bake on separate layers the offset faces and the connectors.
3. Run the script provided and provide the necessary inputs in the correct order (connectors, faces and material thickness).
4. Enjoy.

Troubleshooting:

There will be errors. They always are. Here’s how and where to look for them:

• Faces might actually not intersect, even if they seem so. To check, manually select the faces causing problems and use Rhino to detect their intersection (type intersect). Sometimes things are not as it seems.
• If the radius of a connector exceeds the faces’s size, the script will go berserk. Try reducing the connector radius and things should start looking up. You can also improve the script to use adaptive connector radii in respect with the face size.
• If the material thickness is too big or too small, geometry will start crashing.
• Cull faces which are too small – you might have a little face somewhere which is 0.0004 m^2 and causing problems.
• Check the units you’re working on. The definition’s parameters are set for meters.
• Otherwise, you can email me. Take note that this is a free, spare-time, no-guarantee, as-is service – don’t send me three emails just in case i didn’t get the first one

Download: 110615 SuperConnect.zip (2.21 mb)

SuperConnect working in real-life (spoiler for the future)

A symbolically critical pamphlet

For the hip architectural public, there surely isn’t any need of introducing the (in)famous Voronoi diagram. If there is, then you probably shouldn’t be reading this text and you’re better off doing something else.  Nevertheless, I find myself under increasing pressure to express my thoughts regarding what I find to be a shallow, often completely mis-interpreted and un-justified use of what started out to be a mathematical “toy”. The practical applications of the Voronoi diagram are quite numerous highly fascinating. However, they are beyond the scope of this article – I want to focus mainly on the (mis)use of the aforementioned algorithm in architecture and urbanism.

I think it is quite safe to state that voronoi diagrams have now probably become the “golden mean” of computational architecture. However, I am quite surprised that it took this long for people to notice this – and, what’s even more surprising, there seems to be a severe lack of constructive criticism regarding this quite common and recurrent space-partitioning algorithm. Before moving forward, I would like to clarify the fact that I am not against using voronoi in architecture or urbanism whatsoever – there are clearly numerous meaningful uses, both in generating actual geometry and, probably more, in analyzing and visualizing data on an urban scale. What I am trying to criticize and draw attention to is the mental lock that this catchy algorithm has imposed, and, even worse, the common and frequent misconceptions induced by its strong affiliation with natural phenomena.

There are many reasons for the constant abuse of Voronoi cells (be they two-dimensional or three-dimensional) in architectural and urbanistic projects (the majority of which, by a lucky turn of events, are yet to be built). Crucial to this point is the association of voronoi patterns with organic structures found throughout Nature (living and non-living as well). The unmistakable silhouette can be found in numerous instances: you can see it under a microscope in almost any compact tissue like skin, you can see it in the way cells are distributed in a tree trunk, you can see it in the wings of a dragonfly; the list can carry on for quite a bit more. Taking into account the respective system’s constraints, voronoi cells can provide the most efficient structure or spatial routing paths for matter to organize itself into. This frequent recurrence in nature elevated the voronoi algorithm to the same status as that of the Fibonacci series and the golden mean  was enjoying before. On top of this, its organic and apparently random appearance made it the perfect candidate for a wide range of good-looking geometric experiments. Furthermore, its close ties with nature somehow transcend the barriers of reason and magically attach organic, eco-friendly, pro-environment qualities to any product designed by using this technique.

For example, one common misconception is the fact that generating structure via a three dimensional voronoi diagram would automatically create a super-efficient, really optimized and, on top of this, organic looking structural system. This quite big confusion is probably caused by the numerous natural structures that resemble the output of a voronoi algorithm. There is however a quite obvious missing link in the association which should pop up instantly to any attentive observer. The structures generated by the voronoi algorithm are to be found at microscopic scales, starting off from somewhere near 1*10^-5 m and continuing to decrease. Let’s say, for the sake of argument, that architecture begins somewhere around 10¹ m. There’s quite a big difference in scale, and due to symmetry breaking, physical laws (which, as any scientist worth his pay would tell you, are not universal truths, but the best approximations humankind has found for the way things work) rarely transcend through big scale jumps[1]. In the present case it’s quite obvious – the predominant force in a living tissue at 10^-5 m is a uniform pressure exerted on a cell by surrounding cells – and nature’s elegant response is a complex three dimensional voronoi structure which can be said is roughly indifferent to the main constraint which has shaped structural systems in architecture – namely Gravity. If you do a simple FEA analysis on a voronoi cell grid, you will see you’ll probably need more steel than a simple orthogonal grid to support the same loads, you will double production and building costs[2], besides getting less flexibility in terms of interior organization (spaces restricted to unique, bulky but flexible-looking cells). On the other hand, when used in straight-forward metaphorical approaches, and when this status is recognized and clearly expressed and not masked by a multitude of seemingly objective attributes, the approach can be considered to be “fair use”[3].

Another type of misuse of the Voronoi algorithm can be found throughout large scale urban projects – masterplans, local developments, etc. Cities are not composed of living “cells” in the literal sense – that’s where voronoi works. Cities are living organisms, but the rules behind the dynamics of city growth and crystallization are something completely different from a two-dimensional petri dish[4]. You can use the voronoi diagram to compute the shortest possible paths around a set of point-like obstacles, but this argument is insufficient for justifying its direct transformation in a street network[5]. Actually, street networks never had anything to do with the forces found generating voronoi cells. What you can often see is actually the same dangerous attitude and way of thinking behind modern urbanism clothed and presented as the exact opposite – naturally grown, organic urban lattices etc. – while in the end, if you start to rationally question and compare both approaches you can find dangerous similarities: both are lacking the same links with reality and are somehow strictly imposing their vision. This discrepancy noted here is actually, I believe, part of a bigger and much more comprehensive issue relating to digital and computational architecture[6].

What I find most distressing is the fact that there is a lot of cover-up work being done – voronoi diagrams, be they in three dimensions or two, always stand for some deep underlying natural phenomena whose efficiency and environmental-friendly qualities are automatically transferred to the respective project through a few rhetorical loops empowered by sophisticated jargon. The Voronoi algorithm does generate beautiful patterns and structures – which, when carefully used in the right places, are completely justifiable, sometimes even by aesthetic principles only. To conclude, I strongly believe that a certain level of sincerity should be (self)enforced when employing voronoi diagrams in architecture. While the manner in which this article is written might seem to some to be a bit too vehement, I am deeply concerned about the ease and nonchalance with which the voronoi algorithm is used – in the manner of an architectural recipe which can be applied anytime and anywhere, regardless of any other considerations. That’s why I have tried to raise awareness about the creative abuse taking place and its philosophical idiosyncrasies which, on a broader scale, do not restrict themselves to just this algorithm.

Dimitrie Stefanescu, 28 Oct 2010, Delft

Notes:

[1] The most straightforward example of this is probably the duality of gravity and quantum forces. While at a large enough scale, space is dominated by gravitational fields. The smaller the space gets, gravity loses influence in the favor of electrostatic forces, in the end becoming a negligible factor. The analogy is quite relevant – voronoi-like patterns are found mainly at microscopic scales, whilst architecture operates on a completely different level which can be said to be under the strong influence of gravity.

[2] I am acutely aware of the advances in fabrication technologies and related sciences which might render this argument useless in the possible future. I am trying to argue that, given the sensible ecological context of our current world, we should look for more sensible uses and applications for the tools and techniques that science makes available.

[3] As any ego-centric person would do, I can’t help not to throw in a reference to one of my early projects: http://dimitrie.wordpress.com/2007/12/03/77/

[4] For more in-depth knowledge of this, I strongly recommend both Manuel DeLanda’s much praised  A Thousand Years of Non-Linear History, as well as his interview with Neil Leach in the Digital Cities issue of AD (June 2009, p.50).

[5] I am not afraid to admit that I know this from personal, first-hand experience of the mentioned trap: http://improved.ro/blog/2010/01/urban-developement-proposal/

[6] To be more specific, an overall observed trend is that of employing computational geometry algorithms, often with spectacular visual results followed up by an active effort of fitting architectural qualities in the resultant shapes which usually ends in projects which are, for lack of a better word, fake.

Here’s a movie of the thing in action (realtime):

Importing geometry (lines) from Rhino+GH into Processing, then switching back. Springs are rendered via a nifty hack, ie inverse proportional to the tension inside them. This corresponds to the most cluttered areas where particles get together more. The attraction force is good ‘ol newton’s law.

Of course, you can use it in 2d as well for any number of geometrical network relaxation uses you may find it useful for (street networks, for one, infrastructure at a more abstract scale, city planning, fancy urbanism projects, the like).

For the more architecturally inclined, here’s what happens when gravity enters the mix:

Nice, isn’t it? A nifty form-finding algorithm for a fancy support system of, well, any surface you please (I used a rectangle in example, a bit dull, but aren’t you fed up of double curvatures?). Here’s some screen grabs:

The structures presented here are self-normalizing, ie they reach an equilibrium at some point in the system. However, this is achieved via a delicate balance of quite a few parameters in the processing sketch: path resolution, spring strength and damping, world physics damping & min distance for the attraction force. I strongly recommend taking a look at traer’s physics lib documentation before starting to juggle the values (and to know where to look to juggle the values).

Enjoy & share alike!

Initial explorations: Culture Cracking.

Bucharest never had a coherent image in any point in history. Its only true comon denominator would be the highly despised disorder generated by the struggle between
western rigor and local chaotic impulses. It is one city where conflict is strikingly out in the open, little existing in maters of interface between old and new, poor and rich, highrise and low-rise. This project taps into these conflicting energies, drawing strength and geometry from them while at the same time exposing them and becoming an icon of their formative power.

Does what it says on the tin.

What’s nice is that there are a lot of embedded parametrics inside: the canopy structural system, terrain modding (site), shape cracking of the canopy. Sometime ago there even was a def for controlling room sizes in respect with planar geometries.

Floor plan:

Second and third floor plans:

UPDATE:

Here are some pictures of the final model i built:

Hope you’ll like and love.

We parametrically explored possible future configurations for the study area (450ha large, at the intersection of two main planned road-infrastructure extensions) and tried to push for a solution that would maximize performance (density, height, shading, access to natural elements and connectivity) of the whole region while leaving ample potential that allows for unplanned emergent evolution.

Our goal was to envision an urban tissue that could flexibly respond to all local input factors as well as accommodate desired (planned) goals. An universal 130m by 130m grid was proposed and then deformed to differentiate and create a unique lattice that allows for surprise and yet is easily mapped due to its inner space-partitioning algorithm.

We iteratively cycled through several circulation analysis (using Depthmap) and continuously changed the main axial map of the area to maximize integration.

The text on the boards is in romanian, sorry about that – hopefully the explanations given around the post should be enough. Here are the final boards, reduced:

It was great to work on this project, and also great to find recognition for an approach that is considered risky by our faculty’s standards   We used Rhino and its parametric plugin Grasshopper for the main work and Depthmap for the integration analysis.

Project teachers: Tiberiu Florescu, Sebastian Guta.

Having some more haha with the previous grashopper sketch, this time working on curves.

This is intended in some sort of terrain manipulation for the ongoing project – too bad things started shaping like some sort of electrical field/strange attractor (it has nothing to do with the above, the similarity is purely formal).

Download the .ghx file here. (right click, save target as)

Also there’s a bug in the code – something’s not working right. Initial attractors seem to have much more power than the latter. Maybe someone that takes a fresh look over the code could point out my mistakes.

– LATER EDIT –

There seems to be also a little glitch in provding the code for curve_gen scripting node, so here it is (copy and paste it):