Designing in complexity: Simulation, integration, and multidisciplinary design optimization for architecture

Abstract

While the overall performance of buildings has been established to be heavily impacted by design decisions made during the early stages of the design process, design professionals are typically unable to explore design alternatives, or their impact on energy profiles, in a sufficient manner during this phase. The research presents a new design simulation methodology based on incorporating a prototype tool (H.D.S. Beagle) that combines parametric modeling with multi-objective optimization through an integrated platform for enabling rapid iteration and trade-off analysis across the domains of design, energy use intensity, and finance. The research evaluates how the proposed method impacts design simulation processes, by either enabling and/or disrupting the early stages of design decision making. This simulation technology is presented through two major experiment sets: (1) a series of hypothetical cases emulating the architecture, engineering, and construction (AEC) design modeling and simulation process using our integrated simulation framework and technology; and (2) a pedagogically based experiment used for establishing benchmarks. Through these experiment data sets, both quantitative and qualitative data are collected, including human designer and computational analysis speeds, quantity of generated design alternatives, and quality of resulting solution space as defined by the evaluation metric of this research. The affordances for incorporation of real world design complexity into our computational design prototype and simulation methodology are discussed through both the enabling and the disruptive impact on the early stages of the design process.

Multi-agent Team Formation for Design Problems

“…In computational design, automated methods that can provide a high number of optimal alternatives are highly desirable, as it is hard for the human designers to manually find optimal solutions, and they need a large solution pool in order to pick one that fits their aesthetic/subjective evaluation and/or to make a complex trade-off among different objectives that cannot be formalized into a single function [6,24]. One common method for generating alternatives is to use genetic algorithms [27].…”

“…Genetic algorithms are widely used in order to estimate it. The only knowledge available for the system to evaluate the optimality is in comparison with the other solutions that are also being evaluated during the optimization process [6]. Many apparently "optimal" solutions are actually discovered to be sub-optimal as we find more solutions.…”

“…We study a real system for architectural building design. This is a fundamental domain, since the design of a building impacts its energy usage during its whole life-span [6,3]. We use Beagle [6], a multi-objective design optimization software that assists users in the early stage design of buildings.…”

“…This is a fundamental domain, since the design of a building impacts its energy usage during its whole life-span [6,3]. We use Beagle [6], a multi-objective design optimization software that assists users in the early stage design of buildings. First, the designer creates a parametric design, containing (as discussed in Section 3) a set of parameters that can be modified within a specified range, allowing the creation of many variations.…”

Cyclic overlapping block coordinate search for optimizing building design

“…The technical barriers impeding the objective of bringing dynamic properties to models are being removed and that objective is being pursued through parametric scripting (Nembrini et al, 2014), integration with BIM software (Gerber et al, 2013;Welle et al, 2011) and the recourse to shape grammars (Granadeiro et al, 2013).…”