Optimization Of Trusses Utilising Genetic Algorithms

I wish to look into Multi objective Optimization of trusses; such as weight-cost Optimisation (fabrication cost included weld lengths, assembly cost, transportation cost) because due to the fact that less material = less material cost but could increase fabrication and assembly cost, require more time to assemble etc).

After I apply it to all flat truss typologies and pitched truss typologies, I wish to conduct studies on cost benefit analysis of trusses with material as a variable.

In small spans I wish conduct Material - Structural Optimization

This was a Trial test of the first definition....

Mono Objective problem

Objective function: Minimisation of weight

Layout optimisation : Variables; Depth, purlin distribution, no of panels.

Restriction: Max purlin spacing; 2.2meters. Deflection and Utilization limitation to EC

Buckling limitation by Karamba - Cross section Optimizer

Cross Section availability: Standard CHS range

Analysis Engine : Karamba

Trial Test 1

Span; 18m

Loading regime; 2 load cases: 1xDead + 1.5xWind , 1.35xDead + 1.5xSnow

Dead weight (not factored) 3.74KN/m.sq, Wind load (not factored) 5.25KN/m.sq, Snow Laod 4.45/m.sq.

Results Posted ...

Attachments:

Trail 1 Resulting Truss.png, 70 KB
Trail 1 Results.png, 49 KB
Penalty assignment definition.png, 207 KB

Replies to This Discussion

Permalink Reply by Kane Borg on October 23, 2012 at 6:18am

Trial Test 2

Span; 20m

Loading regime; Same as before

Results Posted ...

Attachments:

Trial 2 Resulting Truss_20m span.png, 58 KB
Trial 2 Results.png, 47 KB

Permalink Reply by Diego Gil on October 31, 2012 at 10:50am

Hello Kane. It has been a long since my last post. These past weeks I have been quite busy with the thesis and therefore unable to participate in the forum.

Your project is quite interesting.

It would be very interesting to see how you calculate the different penalties and how they balance each other. It reminds me a bit of old simulation games like Sim City which had to take into account a great deal of variables and yet create a balanced solution of them all.

Also I believe that it would very interesting to see the contrast between a non-optimized and an optimized solution. For example: after optimizing the structure we achieved a reduction of 10% in cost.

Regards

Diego

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