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Title

MULTI-DISCIPLINARY PERFORMANCE-BASED FORM GENERATION PROCESS: DEVELOPING AN OPTIMIZATION APPROACH FOR LONG SPAN ROOFS

Creator

Nicknam, Mahsa

Date

2013, 2013-05

Description

This research is intended to incorporate multiple performances into the architectural form generation process of long span roofs. To this end,...
Show moreThis research is intended to incorporate multiple performances into the architectural form generation process of long span roofs. To this end, it proposes a multidisciplinary performance-based form generation process (MPGP) using Genetic Algorithm (GA) for the exploration of form based on performance criteria. This process leads us to a new integrated design approach in architecture. Conceptual design decisions have the greatest impact on building performance. However in conventional linear approaches, energy and structural issues are typically dealt with after these program, massing, and enclosure decisions are well articulated. This locks in life-cycle performance, and leads to costly redesigns when results fail to satisfy requirements. Research has shown how successful buildings emerge from the rapid and systematic generation and multidisciplinary analysis of many alternatives. However, until recently Architecture, Engineering and Construction (AEC) design teams were constrained by tools and schedule and only be able to generate a few alternatives, and analyze these from just a few perspectives. The rapid emergence of parametric and generative design, building simulation, and design space exploration and optimization tools now make it possible for a design team to construct and analyze far larger design spaces more quickly, and better understand the importance of design variables on the overall building performance. The proposed process, moves beyond the current form generation approaches by using the dynamic potential possibilities of simulation tools in which form generation is based on their performance feedback. The simultaneous integration of multiple xvi performances at the early stage of design minimizes the need to move back and forth later on the design development phase, therefore reducing the overall design circle. MPGP uses the potential of parametric algorithm to generate the form and uses an optimization algorithm, Genetic Algorithms (GAs), as a search algorithm to explore the proper design satisfying required performances. This method will demonstrate how a flexible 3D model can be parametrically altered toward targeted solutions with the help of near real-time feedback generated by performance-based analysis tools within an optimization framework. Hence, in this approach, design is considered to be a process of a repeated loop of generation, evaluation, and modification until the targeted objectives are satisfied. The integration of generative tools and performance analytical tools in the early stage of design provides great opportunities for the designers to enhance the design space and select the proper design among different design solutions based on their preferences. As a result, designers develop architectural forms based on informed decisions by observing the impact of the varying parameters on the structural and energy efficiency performances. Consequently, this process will greatly benefit engineering by achieving a more collaborative and information-based design environment. Increasing the number of efficient design alternatives, dealing with different levels of complexity in the architectural design process, promoting multi-disciplinary collaboration, and improving overall design understanding are the main benefits of the proposed process.
PH.D in Architecture, May 2013
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