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Generative design, which integrates multidisciplinary types of expertise in unconventional ways, was reserved just until recently to experienced and highly autodidactic designers. However, growing recognition of the importance of generative design methodologies have resulted in a need to introduce theories and applications of generative design to u...
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... order to illustrate the potential of generative calculus, there are two basic areas available: the natural and the artificial . Illustrations of generative mathematics are strangely attracted to make use of natural examples like clouds, mountains, snowflakes, galaxies, plants and so on. Moreover, basic paradigms for generative strategies are inspired by or borrowed from nature: DNA, evolution, breeding, growing. There are of course good reasons for choosing natural examples for the application of generative mathematics. One is that these are very well known examples and thus good vehicles for explanation of complex mathematical concepts. Another reason for generating naturalistically is the application in the field of virtual reality production, which spends great effort to advance to more and more naturalistic outputs. However, when it comes to educational material such as student textbooks, the distinction between explanatory model, chaotic surprise and intentional design goals becomes imprecise. The inevitable ‘fractal landscapes images’ (see figure 3) which can nowadays be mass- generated from specialized stand-alone programs for example, are typical examples of this confusion. In these generated landscapes, parameters and algorithms, geometries and colour schemes are intentionally tweaked to generate even more realistic landscapes including rock textures, trees, reflections on water surfaces and snowy mountain ...
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... Discussed above is the rational approach to generative design, which assumes that the same inputs will always lead to the same result. However, in addition to it, there is also an irrational approach, which assumes the existence of random factors (Herr and Fischer 2021). This approach is more oriented towards discovering new solutions by using pseudo-random numbers. ...
The design processes based on parametric design and generative
design allows multiple alternatives to be generated, analysed and
corrected. The aim of the developed program was to automate the urban design concept development process for selected land units with a local development plan. This document, ordinances and other sources were the guidelines for the program to be developed. The solution-generating program starts by taking in the data, and then geometrical objects are created that have the right relationships to each other. Finally, results are evaluated on the basis of calculated indices and further generations are created taking into account the best options. This process must be overseen by a human, but with the development of machine learning and artificial intelligence, automation will progress, reducing human effort. The results of the study were collated and compared with other experiments in an in-depth literature review.
... In the 1980s, intelligent design methods were proposed based on expert system algorithms [62]. In the following years, a series of intelligent design methods based on biologically inspired algorithms emerged together with the concept of generative design [80,97,102]. Advancements in computer technology drove the digitization and automation of building structural designs forward at an unprecedented pace. ...
Designing building structures presents various challenges, including inefficient design processes, limited data reuse, and the underutilization of previous design experience. Generative artificial intelligence (AI) has emerged as a powerful tool for learning and creatively using existing data to generate new design ideas. Learning from past experiences, this technique can analyze complex structural drawings, combine requirement texts, integrate mechanical and empirical knowledge, and create fresh designs. In this paper, a comprehensive review of recent research and applications of generative AI in building structural design is provided. The focus is on how data is represented, how intelligent generation algorithms are constructed, methods for evaluating designs, and the integration of generation and optimization. This review reveals the significant progress generative AI has made in building structural design, while also highlighting the key challenges and prospects. The goal is to provide a reference that can help guide the transition towards more intelligent design processes.
... In the 1970s, Michell described generative design systems as a tool that could provide various possible solutions for a specific question [5]. He regards it as a design method based on algorithmic or ruled-based procedures; however, some other researchers consider it an evolutionary design process used in developing and producing design solutions [6,7]. Caldas introduces a generative system in her thesis project, where she investigated methods such as Genetic Algorithms and Simulated Annealing in order to generate efficient architectural solutions [8]. ...
Efficiently generating appealing and realistic architectural space configurations has been a significant challenge for designers. This paper presents a deep-learning approach, providing architects with increased control over the final design outcomes. Employing deep learning algorithms to analyze the graph structure of input bubble diagrams facilitates the generation of node-based space layouts confined within predefined borders, ensuring a balance between creative freedom and practical constraints. The findings reveal the effectiveness of the graph-constrained data-driven method in automating the space layout design process. Automating space arrangement accelerates the building design workflow, yielding more efficient and productive results for architects.
... The concept of "generative" is defined as the capacity of revealing anything or showing the source of creativity [Fischer & Herr, 2001]. Furthermore, the concept of generative designing is explained as the method where the designer is interested in the process rather than the output, and generative designing system can be considered as the system supporting the users in the operations. ...
... Furthermore, the concept of generative designing is explained as the method where the designer is interested in the process rather than the output, and generative designing system can be considered as the system supporting the users in the operations. The generativity level of the system is determined based on whether the designer can generate creative products or improve their perception toward designing [Fischer & Herr, 2001]. ...
Genetic algorithm (GA) are based on the continuation of fitter ones’ lives considering the natural evolution. Data are coded as genes in the genetic algorithms. Optimal solutions can be achieved through the methods of crossing and mutation performed on these coded genes. Facade elements of the buildings with an architectural design in this study are independent of sustainability-related concerns, suggesting a great issue for the new buildings to be constructed in the traditional pattern. Accordingly, using the genetic algorithm method, proposals were presented for the new door and window typologies with genetic fitness for the architectural designing process of the buildings to be constructed in Suriçi Region, Diyarbakır, Turkey. Shape grammar, fractal and genetic algorithm, three generative designing systems, were used as the methods. Utilizing the genetic algorithm method, a field study was performed for the proposal of new door and window typologies with the fitness value. The field study was assessed through the plans and facade analyses regarding six Diyarbakır traditional houses with U plan type in Suriçi region of Diyarbakır. An identity card was created for the plan and facade data of the buildings and transferred to the table. Then, the door and window typologies of the exterior facade elements of each examined building were crossed within themselves with the GA method. As a result of the crossover, alternative joinery typologies with a total of 31 windows and 53 different door typologies with compatibility values were produced. Thus, the sustainability of the data of traditional joinery typologies for use in contemporary houses has been ensured. In conclusion, optimal alternative typologies were presented in regard to every chopping typology assessed with the genetic algorithm method. It is thought that this study should be a method that can be used in the production of exterior joinery typologies of contemporary houses to be built in many different cities of our country, especially in the historical texture. Thus, by using the GA method for the production of exterior joinery typologies of contemporary houses to be built in the region, different designers will be able to obtain various designs compatible with the traditional architectural texture while preserving their originality.
... Nevertheless, through instruction, the existing comprehension barriers can be suppressed, and AD can be integrated in the common architectural practice as only another means of representation. In fact, successful introductions of AD in the architectural curriculum can be pointed out, be it via visual or textual programming languages e such as, the studies of Howe (2011), Celani and Vaz (2012), Aish and Hanna (2017), Sousa (2020), Romaniak and Filipowski (2018), and Lima (2018) e along with several pedagogical frameworks for teaching digital design (Çolakoglu and Yazar, 2007;Fischer and Herr, 2001; Frontiers of Architectural Research 11 (2022) 527e541 Iordanova, 2007;Kotnik, 2010;Leitão, 2013;Oxman, 2008Oxman, , 2010. ...
Architectural representation encompasses the means used to describe architectural entities. This discipline has long been under constant change due to architects' ever-present desire for innovation. Algorithmic design (AD) is currently making its way into the plethora of representation methods that integrate the architect's day-to-day work tools. However, it provides its fair share of controversy and hardship as it goes. This paper assesses whether AD is suitable as a representation method for architectural design by making a systematic analysis of this medium as a contemporary representation method. Specifically, we investigate (1) its birth and evolution as a means of representation, (2) the characteristics that make it simultaneously appealing and off-putting to the architectural community, (3) the influence of technological evolution and education on its proliferation, and (4) its capacity to represent design problems in comparison to the currently predominant means of digital architectural representation, that is, computer-aided drafting and building information modeling.
... Following the incorporation of black-box methods, which enabled the solution of general design problems with custom objectives and constraints, optimization became the dominant category for generative systems. For example, genetic algorithms became a widespread technique in the recent debate on generative systems [6][7][8][9][10][11][12] and designers today have access to many black-box optimization algorithms encapsulated in plug-ins for CAD software. In this context, the combination of parametric modeling and optimization to explore design alternatives became a mainstream generative workflow [13]. ...
Over the last decades, a vast repertoire of computational methods has been employed for the synthesis of spatial configuration. Many of these techniques, such as the long-standing black box optimization or the recent generative adversarial networks, enable a quick exploration of the design solutions based on destructive operations, but encapsulate the generative process, inducing turn-taking between computer and designer. In contrast, techniques based on agents naturally provide partial design information and enable fine-grained interaction. However, existing agent-based models originate from non-architectural problems, so it is not straightforward to adapt them for spatial design. To address this gap, we present a method to create custom spatial agents that can satisfy architectural requirements. While the method can be adapted to a diversity of representations and goals, we focus on a proof of concept where agents control spatial partitions (represented as polyominoes with no holes) and interact in an environment represented as a grid. The agents learn how to satisfy its individual (shape, area, etc.) and collective goals (adjacency) using multi-agent deep reinforcement learning. In this paper, we focus on the formulation of the environment, agents, and goals, and present simulations of trained agents to illustrate possible variations.
... Isso permite otimizar fluxos de trabalho e dinamizar a análise de alternativas. Com as possibilidades de design generativo, o computador emerge como um gerador de soluções, em função de parâmetros inseridos e manipulados pelo projetista, que atua de modo indireto sobre os resultados (FISCHER;HERR, 2001). O design computation é intensificado pelos recursos de simulação e otimização, extrapolando a noção do contexto digital como um mero instrumento de representação (CELANI, 2016;BREDELLA, 2017). ...
... Isso permite otimizar fluxos de trabalho e dinamizar a análise de alternativas. Com as possibilidades de design generativo, o computador emerge como um gerador de soluções, em função de parâmetros inseridos e manipulados pelo projetista, que atua de modo indireto sobre os resultados (FISCHER;HERR, 2001). O design computation é intensificado pelos recursos de simulação e otimização, extrapolando a noção do contexto digital como um mero instrumento de representação (CELANI, 2016;BREDELLA, 2017). ...
This paper addresses the adoption of digital technologies in the Architecture, Engineering and Construction (AEC) design process, including Building Information Modeling (BIM), parametric modeling and digital fabrication. The purpose of the study is to understand the implications of adopting digital technologies in the contemporary design process, based on the context of companies considered early adopters of these resources, in contrast to the prevailing practices of the sector. Questionnaires were applied and interviews were conducted with 30 professionals, as well as with 7 researchers, from different countries. As a result, the study presents a synthesis of the main potentials and challenges pointed out by the respondents regarding the adoption of digital technologies, as well as their relations with the design process. Lessons from the early adopters and measures that can be taken to achieve the potential of digital technologies in the AEC industry were also identified. The essential measures to be taken include the development of requirements and regulations by authorities and institutions aiming at the promotion of standards, the integration of professional practice with academic research and changes in the education of future professionals.
... In the following two decades, the literature poorly addressed GD (see Fig. 6). At the beginning of the 21st century, Fischer and Herr (2001) defined GD as a design approach where "during the design process the designer does not interact with materials and products in a direct way, but via a generative system of some sort." For Herr (2002), a generative system refers to computer-aided generative systems that are typically developed by architects, thus reflecting the uniqueness of architectural design problems. ...
... The Cambridge Dictionary defines generative as the "capacity to produce or create something." Some authors define GD as a design process that mainly refers to evolutionary techniques in both the creation and production processes of design solutions (Fischer and Herr, 2001;Frazer et al., 2002;Zhang and Xu, 2018), whereas others do not restrict GD to evolutionary processes, considering it a design approach based on algorithmic or ruled-based processes that generate multiple and, possibly, complex solutions (Bernal et al., 2015;Bukhari, 2011;Chase, 2005;Fasoulaki, 2008;Humppi and Österlund, 2016;Leitão et al., 2014;McCormack et al., 2004;Mitchell, 1975;Oxman, 2008;Shea et al., 2005). Moreover, several authors consider approaches, such as algorithmic generation, cellular automata, evolutionary methods, generative and shape grammars, L-systems, selforganization, agent-based models, and swarm systems, as part of GD (Abdelmohsen, 2013;Caldas, 2008;Chase, 2005;Fasoulaki, 2008;McCormack et al., 2004;Oxman, 2008;Puusepp, 2011;Zee and Vrie, 2008). ...
... Several authors associated GD with the use of evolutionary-based processes (Fischer and Herr, 2001;Frazer et al., 2002;Zhang and Xu, 2018), which is extremely narrow in our opinion. GD includes such processes and other algorithmic approaches, as mentioned by (Bernal et al., 2015;Bukhari, 2011;Chase, 2005;Fasoulaki, 2008;Humppi and Österlund, 2016;Leitão et al., 2014;McCormack et al., 2004;Mitchell, 1975;Oxman, 2008;Shea et al., 2005). ...
Computation-based approaches in design have emerged in the last decades and rapidly became popular among architects and other designers. Design professionals and researchers adopted different terminologies to address these approaches. However, some terms are used ambiguously and inconsistently, and different terms are commonly used to express the same concept. This paper discusses computational design (CD) and proposes an improved and sound taxonomy for a set of key CD terms, namely, parametric, generative, and algorithmic design, based on an extensive literature review from which different definitions by various authors were collected, analyzed, and compared.
... O que diferencia sistemas generativos do processo tradicional é a substituição da interação direta com o produto pela indireta, com mecanismos que o gerem (FISCHER;HERR, 2001). No urbanismo, aplicações auxiliadas por capacidade computacional são crescentes, embora não tão populares ou desenvolvidas como na arquitetura (LIMA, 2017, p. 26). ...
A inclusão da integração com o uso do solo acrescenta complexidade aos já difíceis problemas de desenho de rede de transportes. Então, a limitação da cognição humana reivindica o uso de recursos computacionais. Ao mesmo tempo, esses são problemas complexos de se modelar. Esse trabalho se refere a uma exploração metodológica inicial no curso de doutorado em Engenharia de Transportes. O objetivo desse ensaio é estabelecer etapas metodológicas, e suas relações, para compor um método generativo e algorítmico que reconheça a interação entre transportes e uso do solo. Para tanto, foram explorados conceitos da morfologia urbana e da pesquisa operacional, de maneira teórico-conceitual. Como resultado, foram estabelecidas as bases ontológicas de um sistema dinâmico que será desenvolvido ao longo da confecção da tese. Dessa forma, o trabalho desenvolve um método generativo orientado aos objetivos de planejamento em vez das heurísticas restritas por gramáticas, presentes na literatura.
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Disponível em: https://www.proceedings.blucher.com.br/article-details/proposio-para-abordagem-algortmica-de-planejamento-integrado-de-transportes-e-uso-do-solo-34073
... Since there is a confusion in the terminology between parametric modelling [21] [22], generative design, computational design and other terms used to express the techniques used in architectural parametric design, we had also introduced an open-ended question where we asked the participants to define in one sentence their understanding of the "parametric design approach." Our content analysis of the responses to the answers to open-ended question highlighted "design" and "flexibility" as essential qualities. ...
