1. Generative Design in Civil Engineering Using Cellular Automata Rafal Kicinger June 16, 2006

2. NKS 2006, June 16-18, 2006, Washington, DC 2 Outline Generative Design Cellular Automata as Design Generators –Steel Structures in Tall Buildings –Traffic Control Systems in Urban Areas Emergent Designer Design Experiments Experimental Results Conclusions

3. NKS 2006, June 16-18, 2006, Washington, DC 3 Generative Design: Representation Design representations –One of the key aspects of any computational design activity –Describe designs form, its components, etc. –Incorporate domain-specific knowledge –Determine the space in which solutions are sought Need to address important engineering objectives –Novelty –Optimization

4. NKS 2006, June 16-18, 2006, Washington, DC 4 Traditional Design Representations

5. NKS 2006, June 16-18, 2006, Washington, DC 5 Generative Design

6. NKS 2006, June 16-18, 2006, Washington, DC 6 Generative Design Cellular automata generating designs –Steel structural systems in tall buildings –Traffic control system in urban areas Evolutionary algorithms searching the spaces of generative representations (design embryos + design rules)

7. NKS 2006, June 16-18, 2006, Washington, DC 7 Cellular Automata as Design Generators Steel Structural Systems in Tall Buildings

8. NKS 2006, June 16-18, 2006, Washington, DC 8 Cellular Automata as Design Generators Traffic Control Systems in Urban Areas

9. NKS 2006, June 16-18, 2006, Washington, DC 9 Cellular Automata as Design Generators Traffic Control Systems in Urban Areas

10. NKS 2006, June 16-18, 2006, Washington, DC 10 Emergent Designer

11. NKS 2006, June 16-18, 2006, Washington, DC 11 Emergent Designer System architecture

12. NKS 2006, June 16-18, 2006, Washington, DC 12 Design Experiments Extensive Computational Experiments Conducted –Steel Structural Systems in Tall Buildings Exhaustive search of all elementary CAs started from arbitrary and randomly generated design embryos Generative representations based on 1D CAs evolved using evolutionary algorithms –Traffic Control Systems in Urban Areas Generative representations based on 2D CAs evolved using evolutionary algorithms

13. NKS 2006, June 16-18, 2006, Washington, DC 13 Design Experiments Steel structural systems: –number of bays - 5 –number of stories - 30 –bay width - 20 feet –story height - 14 feet Arbitrary design embryos used:

14. NKS 2006, June 16-18, 2006, Washington, DC 14 Design Experiments Traffic Control Systems –Number of network nodes- 65 –Number of network links -80 –Number of traffic signals - 25

15. NKS 2006, June 16-18, 2006, Washington, DC 15 Design Experiments CA representation parameters: –CA dimension: 1D and 2D –CA neighborhood radius: 1 and 2 –number of cell state values: 2 and 7 –CA neighborhood shape (2D CAs):Moore –CA iteration steps (2D CAs): 14 Evolutionary computation parameters: –evolutionary algorithm: ES –population sizes (parent, offspring): (1,5), (5,25),(5,125) –mutation rate: 0.025, 0.05, 0.1, 0.3 –crossover (type, rate): uniform, 0.2 –fitness: weight of the steel skeleton structure, or the total vehicle time

16. NKS 2006, June 16-18, 2006, Washington, DC 16 Experimental Results Exhaustive Search: Arbitrary Design Embryos Best designs:Total weight: Max. displacement:

17. NKS 2006, June 16-18, 2006, Washington, DC 17 Experimental Results Distributions plotted with respect to two objectives:

18. NKS 2006, June 16-18, 2006, Washington, DC 18 Experimental Results Exhaustive Search: Random Design Embryos Simple X bracings K bracings

19. NKS 2006, June 16-18, 2006, Washington, DC 19 Experimental Results Evolutionary search of generative representations: steel structures

20. NKS 2006, June 16-18, 2006, Washington, DC 20 Experimental Results Evolutionary search of generative representations: traffic control systems

21. NKS 2006, June 16-18, 2006, Washington, DC 21 Conclusions Generative representations based on cellular automata proved to perform well for civil engineering problems where some regularity/patterns are expected, or desired They produced quantitatively better solutions (6-20% average performance improvement) than traditional design representations

22. NKS 2006, June 16-18, 2006, Washington, DC 22 Conclusions CA representations produced qualitatively different patterns than patterns obtained using traditional representations They can be efficiently optimized by evolutionary algorithms, particularly in the case of 1D CA representations

23. NKS 2006, June 16-18, 2006, Washington, DC 23 Credits The work on generative design of steel structural systems in tall buildings was conducted together with Drs. Tomasz Arciszewski and Kenneth De Jong The work on generative design of traffic control systems in urban areas was conducted with Dr. Michael Bronzini

24. NKS 2006, June 16-18, 2006, Washington, DC 24 Backup Slides Evolutionary search of elementary CAs: K bracings

25. NKS 2006, June 16-18, 2006, Washington, DC 25 Backup Slides Evolutionary search of elementary CAs: Simple X bracings