1 DESIGN SCENARIOS: METHODOLOGY FOR REQUIREMENTS DRIVEN PARAMETRIC MODELING OF HIGH-RISES Victor Gane, PhD Candidate, CIFE, Stanford University; [email protected]; http://stanford.edu/~vgane John Haymaker, PhD, AIA, LEED AP, Assistant Professor CIFE, Stanford University; [email protected]; http://stanford.edu/~haymaker ABSTRACT: This paper introduces a collaborative, parametric performance-based design methodology that enables teams to systematically generate and analyze high-rise building design spaces based on multi-stakeholder requirements. Building design involves investigating multidisciplinary design spaces with a high number of project-specific variables and constraints. In practice at leading architecture firms today, conceptual design methods support generating very few options that respond to a limited number of design requirements. As a result, potentially better performing design solutions are overlooked. Our research synthesizes a novel, collaborative design methodology called Design Scenarios (DS). The methodology consists of five process steps: (1) Requirements Model used by a multidisciplinary team to collect, weigh and prioritize multi-stakeholder requirements, (2) Design Strategy used to formally transform into parametric models the identified requirements by proposing potential enabling design parameters and identifying conflicting and enabling relationships amongst requirements and design parameters, (3) Parametric Process Model used to generate, manage and communicate the complex structure of a resultant parametric product model from these relationships; (4) Parametric Model used to generate design spaces responsive to identified requirements, (5) Decisions Model used to support the consensus-building and documentation of the best decision by visually reporting the design options' performance back to the designers and stakeholders. We applied DS on a case study presented in this paper. The research is unique in its development of a method to formally generate parametric models from requirements, and for its industrial-scale, practice-based integration and testing of formal design and decision making methodologies for high-rise building design. Improvements are anticipated both in the quality of the design process by reducing uncertainty and inefficiency, and in the resulting product by enabling more options to be considered from more perspectives. KEYWORDS: design space, parametric modelling, process modelling, requirements engineering. 1. OBSERVED PROBLEM The market economy requires project teams to design quickly and cheaply; however, research shows that successful design is largely a function of clear definition of end-user requirements (Rolland, 2005) and the generation and multidisciplinary analyses of a large quantity of options (Kelley 2006). Every project comes up against an inevitable tension between design exploration and process efficiency. Take high-rise design for example. We recently conducted a benchmarking survey of existing conceptual high-rise design practice to determine the performance of leading design teams. We found that on average a multidisciplinary team averaging 12 people can normally produce only 3 design options during a design process that lasts on average 5 weeks. Most of this time is spent by architects on generating and presenting a small number of design options. Little time is dedicated to establishing / understanding project goals and running multidisciplinary analysis. These analyses are inconsistent and primarily governed by architectural rather than multidisciplinary criteria (Gane & Haymaker 2008). Better performing designs are likely left undiscovered. How can high rise building project teams improve design and critical thinking? Understanding and efficiently managing multidisciplinary requirements early in the design process is a major challenge. So is translating these requirements into a wide range of design options that designers can quickly analyze and systematically choose from. Several points of departure partially address these issues. Design Theory helps us understand the general process of design and define strategies to search the design space. Process modeling can help represent and measure goal-driven design processes. Requirements engineering can help design teams define and manage their building design2 criteria in terms of formally structured goals and constraints. Parametric modeling can help efficiently generate geometric options. High-rise Design Methods help categorize the types of high-rises and elicit a list of design constraints, criteria and performance metrics that each category entails (we summarize these in Gane & Haymaker, 2008). Even with these theories and methods, our benchmarking study shows that the Architecture Engineering Construction (AEC) industry still lacks a methodology that enables project teams to efficiently integrate them into practice. They lack a methodology to define and prioritize requirements, translate these requirements into geometrically flexible parametric models, to analyze these models efficiently from multiple perspectives, and to understand the multidisciplinary tradeoffs of individual options and spaces of options. In another paper we describe how the lack of such a method substantially reduces the effectiveness of parametric methods and stalls multidisciplinary design and decision making processes (Gane & Haymaker 2007). This research establishes such a methodology and begins to test its impact in practice. 2. THEORETICAL POINTS OF DEPARTURE In this section we describe the fundamental points of departure for this research. 2.1 Design theory Design is a creative process, where part of the task is to formulate the problem itself (Simon 1969). Design teams are aided by multi-stakeholder value-based design and decision making methodologies (Lewis et al 2007, Jin & Danesh 2007, Keeney & von Winterfeldt 2007). AEC focused researchers are developing related theory and methodologies, describing the design as (1) identifying a set of requirements; (2) prioritizing among these requirements; (3) developing preliminary solutions; (4) evaluating solutions; (5) establishing final design requirements, preferences and evaluation criteria (Akin 2001). Others are applying these concepts in formal design and decision making methodologies (Ellis et al 2006, Haymaker & Chachere 2007). While designing, teams construct a design space, formulated as the sum of the