The story goes that Philip II of Spain ran a competition for the design of a monastery in Madrid in the 16th century and hired Italian architect Giacomo Barozzi da Vignola to organize and facilitate the competition. In total, 22 architects responded to the challenge and submitted designs. However, Vignola did not rank the entries and select a winning scheme as planned. Instead, he composed a new design by collaging bits and pieces from competition entries he deemed strong. Vignola then presented his composition to the King, who was impressed and gave him the commission. 1 Of course, such a procedure would certainly break all kinds of ethical norms of today's profession. What if, however, the most ideal solution lies in a collective design approach, that is, in composing high performing parts of separate design entries into a new whole? In this article, we present research on a deep neural network (DNN) or deep learning application that extracts design into essential building blocks-based on functional performance criteria-and recombines them into new designs. Over the last 5 years, research in machine learning has exploded thanks to fast developments in deep learning. DNNs used on a wide range of practical applications, from voice recognition systems, such as Siri
This paper discusses the use of crowdsourcing as a new approach for architectural design acquisition. We will give an overview of the concept of crowdsourcing, and elaborate on its particular application in architecture via concrete projects executed on Arcbazar, a firstof-its-kind crowdsourcing platform for architectural design services. We argue that online crowdsourcing platforms can have an immense impact on smaller-scale design challenges, e.g., home remodeling projects and landscape and interior design challenges, and can potentially carry these often neglected projects into the architectural design sphere. In this paper we will discuss the methods and techniques of architectural crowdsourcing and illustrate the processes and outcomes through a series of projects: a remodeling project for a closet; an interior design challenge for a dining space; and a layout problem for an apartment complex. We will then evaluate the protocol and outcome of architectural crowdsourcing, and convey the professional and popular media response to this new method of architectural design acquisition.
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