Artificial Intelligence Applied to Conceptual Design. A Review of Its Use in Architecture

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Conceptual architectural design is a complex process that draws on past experience and creativity to generate new designs. The application of artificial intelligence to this process should not be oriented toward finding a solution in a defined search space since the design requirements are not yet well defined in the conceptual stage. Instead, this process should be considered as an exploration of the requirements, as well as of possible solutions to meet those requirements. This work offers a tour of major research projects that apply artificial intelligence solutions to architectural conceptual design. We examine several approaches, but most of the work focuses on the use of evolutionary computing to perform these tasks. We note a marked increase in the number of papers in recent years, especially since 2015. Most employ evolutionary computing techniques, including cellular automata. Most initial approaches were oriented toward finding innovative and creative forms, while the latest research focuses on optimizing architectural form.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/1

Performance Assessment Strategies

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis

Performance Assessment Strategies:

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis

Performance Assessment Strategies:

Using engineering performance evaluations to explore design alternatives during the conceptual phase of architectural design helps to understand the relationships between form and performance; and is crucial for developing well-performing final designs. Computer aided conceptual design has the potential to aid the design team in discovering and highlighting these relationships; especially by means of procedural and parametric geometry to support the generation of geometric design, and building performance simulation tools to support performance assessments. However, current tools and methods for computer aided conceptual design in architecture do not explicitly reveal nor allow for backtracking the relationships between performance and geometry of the design. They currently support post-engineering, rather than the early design decisions and the design exploration process. Focusing on large roofs, this research aims at developing a computational design approach to support designers in performance driven explorations. The approach is meant to facilitate the multidisciplinary integration and the learning process of the designer; and not to constrain the process in precompiled procedures or in hard engineering formulations, nor to automatize it by delegating the design creativity to computational procedures. PAS (Performance Assessment Strategies) as a method is the main output of the research. It consists of a framework including guidelines and an extensible library of procedures for parametric modelling. It is structured on three parts. Pre-PAS provides guidelines for a design strategy-definition, toward the parameterization process. Model-PAS provides guidelines, procedures and scripts for building the parametric models. Explore-PAS supports the solutions-assessment based on numeric evaluations and performance simulations, until the identification of a suitable design solution. PAS has been developed based on action research. Several case studies have focused on each step of PAS and on their interrelationships. The relations between the knowledge available in pre-PAS and the challenges of the solution space exploration in explore-PAS have been highlighted. In order to facilitate the explore-PAS phase in case of large solution spaces, the support of genetic algorithms has been investigated and the exiting method ParaGen has been further implemented. Final case studies have focused on the potentials of ParaGen to identify well performing solutions; to extract knowledge during explore-PAS; and to allow interventions of the designer as an alternative to generations driven solely by coded criteria. Both the use of PAS and its recommended future developments are addressed in the thesis

Trade-offs in Design: A Theory Building Qualitative Study on the Role of Problem Formulation and Framing in Resolving Trade-offs in Design

Design projects are complex problem-solving endeavors that can involve many goals that are often conflicting. These trade-offs between goals have been primarily studied through the lens of optimization, attempting to create the best possible solution under the constraints of the conflicting goals. However, the broader design literature indicates that design problems are characterized by being ill-defined. As a result, designers need to interpret, formulate, and frame the problem they are attempting to solve, and they must do this without a well-defined set of constraints and requirements. To this end, designers use solutions concepts to explore their problem, and this causes the design problem and solution to coevolve. This research explores the ways that designers formulate and frame trade-offs, how they can manipulate their formulation and framings of the problem to resolve trade-offs, and the aspects of their design situations that influence how challenging these reformulation and reframing processes are. A theoretical framework was derived using set theory to model and describe a designer’s formulation and framing of their problem and solution, which is labeled the design space. The framework also utilizes the concept of Pareto optimality to formally define design trade-offs within a design space. An intensionally defined set of possible manipulations to this design space was identified using this theoretical framework, which informs how those manipulations can be used to resolve trade-offs. This framework also models how a designer’s perceptions and expectations of their design spaces can differ from the real performance of their solutions due to inherent cognitive limitations, information availability, and biases. A semi-structured interview approach was used to explore how practicing designers framed and formulated their initial trade-off situation, and how they manipulated those aspects in their resolution of the trade-off, if at all. Additionally, an echo interview process was used to investigate what influences the designers perceived as affecting how challenging their trade-off situations were to resolve. Seven different approaches to resolving trade-offs were identified in the dataset through a case study analysis, which were classified by how they manipulated the design space. Four of these approaches actively manipulated the designer’s perceived design space to resolve the trade-off, two altering the boundaries of the space and two altering the parameters that comprised the space. These manipulations allowed the designers to restructure their design space and the trade-offs therein to make them easier to resolve. In some of the cases studied, the manipulations also allowed the derivation of solutions that dominated the Pareto frontier of the original design space. In addition to the case study analysis, a thematic analysis was used to identify the aspects of the situation that made manipulating design spaces and resolving trade-offs either easier or more challenging. From this nine codes were identified, sorted into three themes. The three themes were how the design space was initially structured, how well a designer’s expectations aligned with the real outcomes of decisions, and how previous decisions impacted the options available to a designer. The results showed that designers can and do manipulate their problem formulation and framing to resolve trade-offs. This indicates that optimization approaches in design need to account for the dynamic structure of the problem, and that designers should be aware that results of an optimization approach reflect the structure they impose on their design problems. Overall, this research contributes to understanding how designers perceive and frame trade-offs, what tools they have at their disposal to resolve them, and what challenges they encounter while resolving them

Reliability and realizability risk evaluation of concept designs

This thesis addresses the improvement in quality of decision making in design through the use of decomposed design evaluation. The research reported in this thesis is supported by the Design Research Methodology. To perform decomposed decision making, it is necessary to identify criteria that are deemed important for this activity. Questionnaire surveys, literature review and interviews with industry helped to identify these criteria. Reliability and realizability are two criteria that are selected for research in this thesis. The questionnaire surveys are discussed in chapter 2. A review of literature on decision making, reliability and realizability is reported in chapters 3 and 4. Methodologies for evaluating reliability and physical realizability are discussed in chapter 5. Relative reliability risk assessment methodology is applied to various examples consisting of university and industry projects in chapter 6. The application helps to reveal the strengths of the methodology and is termed ‘Verification of the methodology’. Validation issues of both the methodologies are dealt with in chapter 7 using the controlled experimental design. It is found that both the methodologies help to improve the quality of decision making during design evaluation. Relative reliability risk evaluation methodology helps to improve the quality of decision making to a substantial extent but physical realizability evaluation methodology shows only a little improvement in quality of decision making. Finally, it is suggested that the decomposed design evaluation methodology helps to improve the quality of decision making and is therefore proposed to be used by both novice and experienced designers.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adaptive enlargement of state spaces in evolutionary designing

In designing a state space of possible designs is implied by the representation used and the computational processes that operate on that representation. GAs are a means of effectively searching that state space which is defined by the length of the genotype’s bit string. Of particular interest in design computing are processes that enlarge that state space to change the set of possible designs. This paper presents one such process based on the generalization of the genetic crossover operation. A crossover operation of genetic algorithms is reinterpreted as a random sampling of interpolating phenotypes, produced by a particular case of phenotypic interpolation. Its generalization is constructed by using a more general version of interpolation and/or by adding extrapolation to interpolation. This generalized crossover has a potential to move the current population outside of the original state space. An adaptive strategy for state space enlargement, which is based on this generalization, is designed. This strategy can be used for computational support of creative designing. An example is given