Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | School of Design | en_US |
| dc.creator | Janssen, Patrick Hubert Theodoor | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/4100 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | A design method and computational architecture for generating and evolving building designs | en_US |
| dcterms.abstract | The aim of this thesis is to contribute to the development of a practical evolutionary design approach - incorporating both design methods and software systems - that would allow a design team to evolve designs that they find surprising and challenging. This thesis has developed an overall framework that supports such an evolutionary design approach. Genetic and evolutionary algorithms and software systems attempt to harness the power displayed by natural evolution. These algorithms and systems have been successfully employed during the design process in a number of different design fields. However, they have been limited to tackling a very narrow range of well-defined engineering problems. Typically, the evolutionary system is used to optimise certain parameters within a predefined parametric design. Due to a number of fundamental problems, the evolutionary approach has had limited success in evolving the overall configuration and organization of complex designs. This thesis investigates and proposes how these problems can be overcome for building design. The primary problem to be overcome is generating designs that incorporate an appropriate level of variability, which is referred to as the variability problem. This affects both the performance of the evolutionary system and predictability of the types of designs that are produced. In an ideal system, performance is high and predictability is low. However, due to the variability problem, this is difficult to achieve. If design variability is very restricted, then performance may be high but predictability will also be high. If design variability is very unrestricted, then predictability may be low but performance will also be low. In order to evolve surprising and challenging designs, a system is required that both performs well and evolves unpredictable designs. The proposed generative evolutionary design framework allows the design team to restrict design variability by specifying the character of designs to be evolved. This approach is based on the notion of a design entity that captures the essential and identifiable character of a family of designs. This design entity is called a design schema. The design team encodes the design schema as a set of rules and representations that can be used by the evolutionary system. The system can then be used to evolve designs that embody the encoded character. The framework consists of two parts: a design method and a computational architecture. The design method consists of two phases: a generalization phase to develop and encode the design schema, and a specialization phase to evolve a specific design by using the encoded schema. In the first phase, the design team develops the schema with a type of design project in mind. However, the specific project does not yet need to be known. In the second phase, the schema is applied to a specific project and designs are evolved and adapted to the context and constraints of the project. One key advantage of this design method is that the encoded design scheme can be re-applied to many different projects. Two key requirements for the design method are that it should be conservative and synergetic. It should be conservative in that it should only deviate from existing design methods and processes where absolutely necessary. In practice, many designers follow a design process similar to the schema based process - a personal architectural character is cultivated during a lifetime of work and adapted for particular projects. This makes it easier for design teams to adopt the proposed method. The second key requirement is for a synergetic design method. It should be synergetic in that the contrasting abilities of the design team and the computational system should be exploited in a way that is mutually beneficial. The design team focuses on the creative and subjective task of developing and encoding the design schema, and the computational system is used for the repetitive and objective task of evolving alternative design models. The second part of the framework is the computational architecture. This architecture specifies a system that can be used to run the evolutionary process. Its two key requirements are scalability and customizability. The architecture should be scalable in that the performance of the evolutionary system should not degrade unacceptably when used to evolve large and complex designs. Scalability is achieved by using a parallel computational model that reduces execution time, in combination with a decentralised control structure that improves the robustness of the system. The architecture should be customisable in that it should allow the design team to change and replace the evolutionary rules and representations. Customizability is achieved by breaking the system down into two parts: a generic core and a set of specialised components. The generic core does not need any modification by the design team and can be reused within any project. The specialised components, on the other hand, have to be specified by the design team. These components include a set of routines that encapsulate the rules and representations that constitute the encoded schema. The feasibility of the proposed generative evolutionary design framework is supported by a demonstration of the process of encoding the design schema. A design schema is introduced that defines the character of a family of design. A crucial aspect of encoding such a schema is the creation of a set of rules and representations for generating alternative design models with an appropriate level of variability. The demonstration focuses on these generative rules and representations. A generative process is developed that can generate a variety of three-dimensional models of buildings that differ in overall organization and configuration but that share the schema character. This generative process is used to define generative rules and representations that are implemented as a set of Java programs. These programs are then used to generate a population of three-dimensional models of building design, thereby allowing the character and variability of the designs to be verified. The feasibility of the encoding process is successfully demonstrated. | en_US |
| dcterms.extent | xviii, 264 p. : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2005 | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.LCSH | Architectural design -- Computer simulation | en_US |
| dcterms.accessRights | open access | en_US |
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|---|---|---|---|---|
| b1809918x.pdf | For All Users | 4.22 MB | Adobe PDF | View/Open |
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