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

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).