Computational Approaches to Space Layout Planning

1. Computational Approaches to Space Layout Planning Presented By Hoda Homayouni Final project for ARCH 588

2. Introduction“What is Space Layout Planning” Space layout planning is the process of allocating a set of space elements according to certain design criteria. It usually results in topological and/or geometrical relationships between elements.

3. Introduction“Why Computers?” We humans are easily board, distracted, and tend to make mistakes when confronted with large and complex problems. Our memory is vast, but our ability to recall these memories at will is limited.

4. Motivation“Why this is important?” Kalay: ”If we could find a way to take advantage of the abilities of computers where ours fall short, and use our own abilities where computers’ fall short, we would create a very powerful symbiotic design system: computers will contribute their superb rational and search abilities, and we humans will contribute all the creativity and intuition needed to solve design problems.”

5. Introduction“Challenges” • Solving ill defined problems. • Addressing qualitative constraints. • Evaluating Nonquantifiable Qualities. • Having Creativity or using humans creative abilities. • Convincing the architects to rely on the program.

6. Computerized Space Layout Planning“Solution Approaches” Kalay (2004) categorizes computational design synthesis methods as: 1- Procedural Methods 2- Heuristic Methods 3- Evolutionary Methods

7. 1- Procedural Methods Leverage our ability , as human designers, to specify local conditions and the ability of computer to apply or test these relationships over much larger sets of variables.

8. 1-Procedural Methods“Types” • Complete Enumeration • Space Allocation • Additive Space Allocation • Permutational Space Allocation

9. 1- Procedural Methods“Additive Space Allocation” GRAMPA (GRAph Manipulating PAckage ) is an example of a program that uses additive space allocation. The current GRAMPA was presented in 1971.

10. GRAMPA“Structure of the program” The methods of solution depend on a special linear graph representation for floor plans called the ‘dual graph’ representation.

11. GRAMPA“Structure of the program” • a “space” is defined to be either a room or one of the four outside spaces.

12. GRAMPA“Floor Plan Graph with Dual Graph”

13. GRAMPA “Planar Realizations of a Graph”

14. GRAMPA “Final level” • Fill the region with edges representing those adjacencies not specifically requested by the design requirements. • Satisfying the physical dimension requirements.

15. GRAMPA“Output”

16. GRAMPA“Pros and Cons” • The adjacency constraints are defined as either true or false. • Long run time (23 min. for the example) • The program could handle only adjacency objectives. • Finding all the realizable planar graphs not necessarily would lead to a floor plan. • Exhaustive generation of the floor plan could be replaced with a heuristic method.

17. 1. Procedural Methods“Permutational Space Allocation” • ACTLOC is an example of a prototype that uses Permutational Space Allocation • The first version was written in 1965 • Current program has been developed in 1992

18. ACTLOC “Procedure of the Program” The Program is based on compatibility between any pairs of activities.

19. ACTLOC“Structure of the Program” ACTLOC finds the optimum configuration by shifting activities around, keeping the arrangement which results in an improvement in the overall compatibility, then shifting activities again, starting from the current best.

20. ACTLOC “The Output”

21. ACTLOC “Pros and Cons” • The program does not consider size of the activities. • The program may be trapped in a local optimum • There are many other factors that could not be simulated with the ACTLOC

22. 2. Heuristic Methods Computational design methods that are inspired by analogies, just like the design synthesis methods that are typically inspired by analogies and guided by the architect’s own or another designer’s previous experiences.

23. 2. Heuristic Methods“Types” • Analogical Methods • Case-Based Methods • Expert Systems • Shape Grammars

24. 2. Heuristic Methods“Analogical Methods” • Borrow the idea of simulating space arrangements in layouts from the rules that has derived from other sciences • Scott Arvin and Donald House (1999) were the first one who used physically based techniques to manipulate space layout planning.

25. Analogical Methods“Structure of the Program” • The designer specifies and modifies the design objectives instead of directly manipulating primitive geometry. • The plan adapts to the changing state of objectives by applying the physics of motion to its elements.

26. Analogical Methods“Structure of the Program” • The spaces and walls are modeled as point masses . • Adjacencies between spaces are modeled as springs that connects the masses • Objectives are translated into forces applied to the Masses.

27. Analogical Methods“Other Objectives” • Orientation Objectives • Interior Objectives • Exterior Objectives • Separation Objectives

28. Analogical Methods“Geometric Design Objectives” • Alignment Objectives • Offset Objectives • Area Objectives

29. “Analogical Methods”Geometric Design Objectives • Gravity Objectives • Proportion Objectives

30. Analogical Methods“The Output”

31. Analogical Methods“Pros and Cons” • It help architects to feel the nature of design problem • The behavior of spring May not be the same as what we need for manipulating the design in all situations • There are other design criteria that the program does not address • Unable to handle multistory buildings

32. 3. Evolutionary Methods Potential to Produce Novel Design Solutions. Superior to other search algorithms for problem consisting of large unstructured search spaces. • Genetic Algorithm • Neural Networks

33. 3.Evolutionary Methods “Genetic Algorithm” • Based on Survival of the fittest • Genetic codes: Genotype • Population of solutions: Phenotypes • Fittest phenotypes are chosen by the means of Fitness Function. • Three operators for manipulating the representation of genotypes: Selection, Crossover, and Mutation

34. 3.Evolutionary Methods “Genetic Algorithm” • By Rosenman and Gero in 1999 • two examples of work for evolving designs by generating useful complex gene structures • The first example uses a genetic engineering approach whereas the other uses a growth hierarchical model

35. Genetic Algorithm “Genetic Engineering Approach” • Making the complex genes from basic genes and using them in next generations as evolved genes • The individuals are evolved through a number of generations • An additional operation identifies particularly successful combinations of genes

36. Genetic Engineering Approach “Evolving Representation”

37. Genetic Engineering Approach Design of Architectural Floor Plan • The Fitness Function:

38. Design of Architectural Floor Plan“Evolved Representation”

39. Design of Architectural Floor Plan“New Requirements” 1- Minimal overall wall length 2- No walls with open ends, that is, no walls that do not build a closed room; 3- 6 rooms; 4- Room sizes 300, 300, 200, 200, 100 and 100 units.

40. Genetic Engineering Approach “The Output”

41. Genetic AlgorithmHierarchical Growth Approach • A multi-level approach. • Each level has its own definition and requirements. • at each level, a component is generated from a combination of components from the level immediately below.

42. Hierarchical Growth Approach“The Fitness Functions” • At room level: Minimizing the perimeter to area ratio and the number of angles. • At zone level: Minimizing a sum of adjacency requirements between rooms. • At house level: Minimizing a sum of adjacency requirements between rooms in one zone and rooms in other zones.

43. Hierarchical Growth Approach“The output- Living Room Generation”

44. Hierarchical Growth Approach“The output- Living Zone Generation”

45. Hierarchical Growth Approach“The output- House Generation”

46. Hierarchical Growth Approach“Pros and Cons” • Organizing the generating process • Making the design process more meaningful and manageable to the architects. • Having multiple fitness function for addressing the same issue may make a system biased toward the one that is more likely to happen.

47. Hierarchical Growth Approach“Pros and Cons” • The fitness function can not adapt itself to the design process. • The program is not capable to recognize the novel solutions by itself. • The program can not learn from its mistakes.

48. Conclusion“Comparison Chart”

49. Conclusion • Despite the increasing need of architects to have a computational assistant in the design tasks, still they don’t show much enthusiasm to use the available programs • Most of the programs are still at a research prototype stage • The runtime of these programs increases exponentially by increasing the objectives so that the program can not handle calculating complexities.

50. Conclusion • Architects want to design themselves and let the computer do the redundant jobs. • They don’t trust these programs. • They do not want to loose their job to a software.