SanderO wrote:Very cool! You really can't consider the facade columns as individuals.
I could see the deficiency there.
First the trusses were connected to every other column... but more importantly they acted as triple because the the spandrels. If you run the sim again combine 3 facade columns and treat them as one.
Yes, that can be done. Even gradations more true to the real thing, where the triplets are very strongly coupled but still distinct and capable of some diffential action, all controlled by variable settings or logical functions.
The failure was surely less random... beginning with the taken out columns from the plane impacts.
This was the first full run of the new program so I opted for random but regular input. Scared up some bugs along the way. Regular input, with variations, is good for that. Think of the trial above as a test pattern.
A lot of things need to be incorporated, starting with simple local load sharing, then hybrid complex local/global sharing via the hat truss. Then more sophisticated and meaningful methods of applying damage and affecting capacity like fire maps, lateral support loss, and so on. Then, explicit introduction of floors in some way, either as elements or nested within their own distinct, parallel sub-simulation. List is long.
The whatever fires were in play would be localized and spread... but not across the shafts... which is why I supplied the last layout.
Excellent. The development of heating effect routines will be a multiphase effort (everything here is iteratively evolving). It would be nice to be able to program fire behavior in a variety of ways. I'm thinking about capturing the NIST fire/temp maps and using them as input.
Fire patterns are (more than) one step removed from the ultimate goal, which is capacity changes over time. Fire drives heating, but the relation between fire conditions over time and the resulting temperature distribution over time is very complex and has high uncertainty. The ideal relationships between temperature and capacity are only a rough approximation, too. Then, other things besides heat reduce capacities. There are many paths to one capacity value at a specific time.
Right now, the model supports setting capacities in any arbitrary way at any step, but there's no logic feeding that process yet, hence the random capacity reductions of the previous run; very easy. I have to program some additional stuff to do more interesting damage patterns. I can easily specify column loss manually as part of the initial conditions or target certain columns on particular steps. So the plane damage will be easy. Targeted capacity loss by region, with or without a random component, is a little more work but you could see I had the beginnings of it in the first program.
The important thing at this stage was to complete a solid and flexible framework for plugging in custom behavior as it's later developed. Everything feeds the capacity values, and the capacity values drive the evolution of the system. Now the engine is in place to orchestrate and execute the process, almost anything can be plugged in. All takes time of course.
Interesting how the rapid onset occurs.
Isn't it? It is by its nature always an accelerating process. When it gets towards the end, the self-reinforcing feedback (fail -> capacity loss -> redistribution -> fail) explodes.
I run it a bit slower with some sort of clock.
Ah, there's a sticky part. Right now, it is nearly atemporal. All it has concerning time is strict ordering of the steps; that is, the order of steps follow the arrow of time, but whatever time interval occurs between steps is unspecified and is virtually guaranteed to vary from step to step, perhaps by many orders of magnitude. Depends on the thing being modeled and the properties assigned to it. More in a later post.
In order to "slow this down" natively, without introducing the dimension of time at all, I can slow the amount of capacity reduction at steps approaching the critical threshold. This will produce smaller avalanches of failures at each step and draw out the process somewhat. Still can't call it time. Yet.
But it's very instructive of how a tube in tube structure can go from standing to global failure in an instant. Calling Richard Gage, calling Richard Gage... rapid onset of collapse is not ONLY seen in controlled demolitions.
It is very instructive and this is one of the premiere features of cascading failure. One or more quantities grow dynamically. Mass/momentum with a snowball effect, size and scope with network failure and domino chains, or rapidity of failure about a critical point like in this type of model.