1. Chia Y. Han ECECS Department University of Cincinnati Kai Liao College of DAAP University of Cincinnati Collective Pavilions A Generative Architectural Modelling for Traditional Chinese Pagoda CAAD futures 2005

2. Introduction Complex Adaptive Systems (CAS) Computational approaches based on CAS Inspired by ‘bio-logic’ Categorized into non-classic, connectionists and natural computation Applications for shape computation, architecture and urban morphology formalization of natural form through fractal geometry modelling of animal behaviour patterns, e.g., the Boids algorithm biological growth processes, e.g., the L-systems evolution & adaptation phenomena, evolutionary computation, etc.

3. Background Current avant-garde architectural practice the works of Greg Lynn, ETHZ, etc. A Sequence of Similar Modules with Iteration and Interaction

4. Background ( cont. ) Design Computation research Recursive algorithms for shape computation, e.g., shape grammar Fractals in architecture and urban structure Evolutionary design for architecture Artificial life for architectural design and 2D/3D Cellular Automata for building plan and mass/volume composition

5. Background (cont.) Problems with current CAS approaches Unclear association to the architectural form & space concepts, and architectural space theories, Lack of an in-depth, systematic analysis of design manners that provides a holistic and connectionist view, Insufficient development of aesthetic theory and historical perspective of the new paradigm.

6. Background (cont.) Can current CAS approaches do this? Iteration: Shape grammar? Structure?

7. Needs and Proposed Solutions To upgrade the concepts of architectural form and space based on ‘bio-logic’, self-organization and non-linear order – To develop a framework of generative architectural modelling that is applicable to design analysis & criticism, and formal & spatial design. To study how the shape patterns/components are used as the basic entities for architectural design/modelling – To integrate basic shape with architectural space concept and spatial patterns in architectural settings. To discover how past architectural works can enrich future design using generative methodologies in architectural modelling – To study traditional Chinese architectural structures, in particular, pagoda, to help us gain new aesthetic knowledge and develop historical research methods for enriching design manners & architectural vocabulary for current design practices.

8. Western vs. Eastern philosophy

9. Evolution of Chinese Pagoda

10. Embedded into terrain

11. Pagoda Forest

12. Array Formation

13. Our Approach 1. Study both the architectural form-making and space design analysis based on CAS viewpoints 2. Incorporate generative design and evolutionary computation in implementation 3. Provide both global and local considerations through multi-agent modeling and simulation

14. Our Method Two level abstraction (space and form) for descriptive & generative model, combining: - graph-based space description with - recursive shape computation

15. c Basic pavilion parts Roof Bracket Wall/column Podium/banister

16. Variants of composition

17. Generative Model – Profile characteristics

18. Sample global spatial structures

19. 1-5-5-11-5

20. Pavilion units 4-sided 68 round

21. Variation by Eave Feature

22. Adjacent pavilions interacting as agents with living behaviors Moving Producing Extending eave Degenerating (roof)

23. Sample composition patterns (Rhythm/ emergent social structure)

24. Algorithm

25. Start Phase II Phase I Phase III Phase V End Phase IV Flow Chart Phase I – Generate pavilions (local features) Phase II – Generate spatial patterns (global features) Phase III – Generate pavilion assemblies (integration) Phase IV – Generate pagoda (adaptive refinement) Phase V – Select final configuration (explore design space)

26. Phase I - Generate Pavilions (GA) 1. Initialize design space for pavilion units 2. Select a formal unit pattern and record its topological graph 3. Seed a set of pavilion units genotype (initial population) 4. Decode genotypes into phenotypes for subjective evaluation of the fitness, accept or continue 5. Add the genes of selected pavilions into the pool, evolve, and go to step 4.

27. Phase II – Generate spatial structure 1. Initialize design space – topologies of pavilion layouts 2. Randomly seed a set of graphs for topologies of pavilion assemblies 3. Decode the genotypes into phenotypes for subjective evaluation, accept or continue 4. Put the genes of selected pavilion layouts into the pool, evolve, and go to step 3

28. Phase III – Generate assemblies 1. Initialize design space for combining phases I & II (forming a collection of pavilion genes and layout genes) 2. Generate an assembly from the above pool, using a selected pavilion unit as axiom and applying recursively operational rules according to the selected layout

29. Phase IV – Generate pagoda 1. Consider a pagoda as a collection of living pavilions, explicitly encode the following: interactions between pavilions, local and global constraints, and geometric and form parameters of the pavilions. 2. Do local refinement by letting individual pavilion move, grow, shrink, produce, die, and interact with others to generate a candidate version of the pagoda

30. Phase V – Exploring design space 1. Specify aesthetic standards for selection 2. Invoke phase IV to generate a set of pagoda candidates, and select a pair with desirable characteristics. 3. Evaluate them subjectively, and let them evolve further in the design space to generate newer versions

31. Exploring design space

32. Compositional rules for plan layout recursive

33. Shanghai Jin Mao Tower By Skidmore, Owings & Merrill

34. Genes for Shanghai Jin Mao Tower ∑

35. Sample result - glPagoda

37. Conclusions Investigated generative architectural modeling for Pagoda and traditional Chinese architecture Explored and extended the potential of adaptive computing for architectural design methods

38. Contributions 1. Provide a study of both the architectural form- making and space design analysis from the CAS viewpoints 2. Incorporate generative design and evolutionary computation in implementation 3. Provide both global and local considerations through multi-agent modeling and simulation

39. Thank you !

40. Temple of Heaven

41. Twin Pagodas

42. Pagoda Triplet