This experiment use generative components as a parametric modeling software and microsoft excel to evaluate and optimize a steel truss. The objective consist in reduce the total steel weight in the truss.
The problem is simple. It is necessary to modify the position of some truss joints (nodes) in order to explore lightweight solutions (less material) but always satisfying the structural load.
The truss is composed by 2 chords, 5 vertical webs and 6 diagonal webs. The top chord (green) is fixed to the wall and the last joint receives a vertical load ( f ). The bottom chord (red) is composed by 6 joints and these nodes are the ones to change the position in order to explore light weight design solutions. The first joint, represented by a red triangle can only move in one axis parallel to the wall. In the other hand, the square red nodes has the flexibility to explore the space in different directions, but constrained to defined areas.
If no restriction is applied to red nodes the solution will be a shape similar to a triangle, a well known structural efficient form. The idea is to let the system generate efficient forms under complex constraints.
The color areas represent the constraints where the node can be positioned. The black arrows represent the trajectory of the points to the optimized location. The tube radius, react according to the structural load
Planar trusses are typically hosted by a single plane and their most common use are for roofs and bridges. for this experiment, the truss is fixed supported in the joint 1 and the load is applied in the further distance of the cantilever. The red color in the structure represents that these segments are supporting tension and the blue color slenderness.
The item 1, explain in diagrams how generative components send the information to excel and how this information is managed. The item 2, is a brief description of microsoft solver. The excel plugin to develop the optimization. The item 3, is a comparative of 2 truss. On the left, before the optimization and on the right, after the optimization
The model before the optimization weight: 16.795 Tonnes and the model after the optimization weight: 11,607 Tonnes. Represents a reduction of 5,188 Tonnes
This experiment was conducted in 3 days during the smartgeometry conference and workshops, under Steve Downing supervision. San Francisco, March 2009.