Generative Design | Genesis Lab

Augmented Computational Design; Methodical Application of Artificial Intelligence in Generative Design

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Pirouz Nourian, Shervin Azadi, Roy Uijtendaal, Nan Bai

Abstract:

This chapter presents methodological reflections on the necessity and utility of artificial intelligence in generative design. Specifically, the chapter discusses how generative design processes can be augmented by AI to deliver in terms of a few outcomes of interest or performance indicators while dealing with hundreds or thousands of small decisions. The core of the performance-based generative design paradigm is about making statistical or simulation-driven associations between these choices and consequences for mapping and navigating such a complex decision space. This chapter will discuss promising directions in Artificial Intelligence for augmenting decision-making processes in architectural design for mapping and navigating complex design spaces.

Collective Intelligence in Generative Design; A Human-Centric Approach Towards Scientific Design

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Shervin Azadi, Pirouz Nourian

Abstract:

Mathematical formalization of knowledge within a scientific paradigm unifies sporadic efforts through converging glossary and notation, thus enabling scientists to identify knowledge gaps and discrepancies easier. Furthermore, comprehensive formalization reveals potential bridges to various domain sciences and facilitates the utilization of methods that have proven effective in scientific problem-solving. In the case of Architecture and Built Environment, there is a long history of scattered efforts for identifying and formalizing design problems and design methodologies, but the big picture is yet missing. In this short piece, we name and frame some of these efforts to identify their parallels with Mathematics, Computer Science, and Systems Theory, as well as to illustrate new opportunities that methodical design unlocks.

DeciGenArch; A generative design methodology for participatory architectural configuration via multi-criteria decision analysis

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Aditya Soman, Shervin Azadi, Pirouz Nourian

Abstract:

Our approach to Generative Design converts the problems of design from the geometrical drawing of shapes in a continuous setting to topological decision making about spatial configurations in a discrete setting. The paper presents a comprehensive formulation of the zoning problem as a sub-problem of architectural 3D layout configurations. This formulation focuses on the problem of zoning as a location-allocation problem in the context of Operations Research. Specifically, we propose a methodology for solving this problem by combining a well-known Multi-Criteria Decision-Analysis (MCDA) method called ‘Technique for Order of Preference by Similarity to Ideal Solution’ (TOPSIS) with a Multi-Agent System (MAS) operating in a discrete design space

EARTHY; Computational Generative Design for Earth and Masonry Architecture

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Pirouz Nourian, Shervin Azadi

Abstract:

EARTHY is a master’s level design studio offered by the chair of Design Informatics in collaboration with the chair of Structural Design and Mechanics. As the name suggests, the course is about designing and engineering earthy buildings, in particular adobe buildings, intended for mid-term accommodation of displaced communities. Our goal is to design buildings that can be ideally built by their prospective inhabitants. Earthy buildings are virtually 100% recyclable, and, compared to tents, they offer much more comfort. The use of earthen materials necessitates the knowledge of complex geometry, e.g., in designing and technical drawing of vaults, domes, and arches in optimal shapes. The focus of the course is on the relations of materials, forms, and structures, explored computationally. Automated construction design and generation of assembly instructions are extra challenges to be tackled via computation.

EquiCity game; a mathematical serious game for participatory design of spatial configurations

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Pirouz Nourian, Shervin Azadi, Nan Bai, Bruno de Andrade, Nour Abu Zaid, Samaneh Rezvani & Ana Pereira Roders

Abstract:

We propose a mathematical framework for developing social-choice games that are designed to mediate decision-making processes for city planning, urban area redevelopment, and architectural configuration of urban housing complexes. The proposed framework features a digital serious gaming approach for participatory design to support transparency and inclusion in the process of decision-making and ensure an equitable balance of sustainable development goals in spatial design outcomes. The mathematical process consists of a Markovian design machine for balancing the design decisions of actors, a massing configurator equipped with fuzzy logic and multi-criteria decision analysis, algebraic graph-theoretical accessibility evaluators, and automated solar-climatic evaluators using geospatial computational geometry. We demonstrate the effectiveness of the framework by implementing a multi-player online game that facilitates a participatory decision-making workshop for forming multi-functional building complexes by providing a generative configurator equipped with automated appraisal/scoring mechanisms for revealing the aggregate impact of alternatives. The EquiCity game empowers a group of decision-makers to reach a fair consensual spatial design by mathematically simulating many rounds of reasonable trade-offs between their decisions, with different levels of interest or control over various types of investments. The novelty of the framework is in its capability to encompass decision-making about the most idiosyncratic aspects of a site related to its heritage status and cultural significance to the most generic aspects such as balancing access to sunlight for the site while respecting ‘the right to sunlight’ of the neighbours of the site, ensuring coherence of the entire configuration with regards to a network of desired closeness ratings, the satisfaction of a programme of requirements, and intricately balancing individual development goals in conjunction with communal goals and environmental design codes.

Generative Design in Architecture; From Mathematical Optimization to Grammatical Customization

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Pirouz Nourian, Shervin Azadi, Robin Oval

Abstract: This chapter provides a methodological overview of generative design in architecture, especially highlighting the commonalities between three separate lineages of generative approaches in architectural design, namely the mathematical optimization methods for topology optimization and shape optimization, generative grammars (shape grammars and graph grammars), and [agent-based] design games. A comprehensive definition of generative design is provided as an umbrella term referring to the mathematical, grammatical, or gamified methodologies for systematic synthesis, i.e. derivation, itemization, or exploration of configurations. Among other points, it is shown that generative design methods are not necessarily meant to automate design; but rather provide structured mechanisms to facilitate participatory design or creative mass customization. Effectively, the chapter provides the theoretical minimum for understanding generative design as a paradigm in computational design; demystifies the term generative design as a technological hype; shows a precis of the history of the generative approaches in architectural design; provides a minimalist methodological framework summarising lessons from the three lineages of generative design; and deepens the technological discourse on generative design methods by reflecting on the topological constructs and techniques required for devising generative systems or design machines, including those equipped with Artificial Intelligence. Moreover, the notions of discrete design and design for discrete assembly are discussed as precursors to the core concept of design as decision-making in generative design, thus hinting to avenues of future research in manufacturing-informed combinatorial masscustomization and discrete architecture in tandem with generative design methods.

GoDesign; A modular generative design framework for mass-customization and optimization in architectural design

Mon, 01 Jan 0001 00:00:00 +0000

Authors: Shervin Azadi, Pirouz Nourian

Abstract:

In this paper, we present a modular generative design framework for design processes in the built environment that provides for the unification of participatory design and optimization to achieve mass-customization and evidence-based design. The paper articulates this framework mathematically as three meta procedures framing the typical design problems as multi-dimensional, multi-criteria, multi-actor, and multi-value decision-making problems: 1) space-planning, 2) configuring, and 3) shaping; structured as to the abstraction hierarchy of the chain of decisions in design processes. These formulations allow for applying various problem-solving approaches ranging from mathematical derivation & artificial intelligence to gamified play & score mechanisms and grammatical exploration. The paper presents a general schema of the framework; elaborates on the mathematical formulation of its meta procedures; presents a spectrum of approaches for navigating solution spaces; discusses the specifics of spatial simulations for ex-ante evaluation of design alternatives. The ultimate contribution of this paper is laying the foundation of comprehensive Spatial Decision Support Systems (SDSS) for built environment design processes. INTRODUCTION This paper presents a ‘participatory generative design framework’ emblematically called ‘Go Design’ after the game of Go. This framework is designed to enable Mass-Customization and application of Multi-Criteria Decision Analysis for supporting multi-actor decision-making processes such as those aimed at reaching consensus among stakeholders on goals and design requirements, objective decision-making processes such as finding the best configuration respective to environmental factors (e.g. light, energy), and finally subjective processes such as choosing styles, materials, and colors of the final structure. The focus of this paper is on the mathematical formulation of the spatial configuration problem, given exemplary inputs for user preferences to establish the generality of the framework as to different optimization/decision-making approaches and vari-Computational design-Volume 1-eCAADe 39 | 285 ous participatory processes. Thus, the details of implementation and the participatory processes are beyond the scope of this paper. Effectively, the proposed framework reformulates architectural design as a chain of systematic decision-making problems in terms of given inputs and desired outputs rather than ad-hoc drawing and representation challenges. We present a mathematical categorization and formulation of archetypical design problems, that provides for adequate utilization of a variety of computational methodologies. This categorization sets out a spectrum of decision-making problems ranging from the most abstract to the most concrete: 1) [space] planning in the context of Graph Theory, 2) configuring in the context of Algebraic Topology, and 3) shaping in the context of Computational Geometry. This categorization distinguishes the priorities of decision-making and specifies the widely-spoken notion of early-stage design decisions. By revisit-ing such typical architectural design problems from ‘drawing’ problems to ‘decision’ problems, they fall naturally within the purview of “The Sciences of the Artificial” (Simon, 2008), as defined by Herbert A. Si-mon. As such, this framework is a tribute to the initiative of several pioneers of computational design, namely the eloquent quest of Yona Fridman’s “To-wards a Scientific Architecture” (Friedman, 1980).