Architecture

About: Architecture is a research topic. Over the lifetime, 25849 publications have been published within this topic receiving 225266 citations.

The Architect's Role in Community Shepherding

Abstract: Software architects don't just design architecture components or champion architecture qualities; they often must guide and harmonize the entire community of project stakeholders The community-shepherding aspects of the architect's role have been gaining attention, given the increasing importance of complex "organizational rewiring" scenarios such as DevOps, open source strategies, transitions to agile development, and corporate acquisitions In these scenarios, architects would benefit by having effective models to align communities with architectures This article discusses the "smells" indicating that a community isn't functioning efficiently, offers a set of mitigations for those smells, and provides an overview of community types

An Anthropology of Architecture

Abstract: Preface Chapter 1: The Long Nineteenth Century: Collecting Primitive Huts and Thinking Through Origins Chapter 2: Architecture and Archaeology Chapter 3: Social Anthropology and the House Societies of Levi-Strauss Chapter 4: Institutions and Community Chapter 5: Consumption Studies and the Home Chapter 6: Embodiment and Architectural Form Chapter 7: Anthropology, Representation and Architecture Chapter 8: Iconoclasm, Decay and the Destruction of Architectural Forms Postscript Bibliography Index

The Fashion of Architecture

Abstract: Fashioning Architecture and Art Intimate Architecture: Fashion and the Interior Fashioning Photography Urban Decay Heroin Chic Memory and Tragedy 2Mapping Fashion Space Shopping for Architecture Virtual Re[tail]ity 3Shrinking Space Urban Radar Place and Non-place 4Mining the Void Reconstruction Deconstruction Unconstruction 5Urban Nomads Archigram Yeohlee Final Home Hussein Chalayan C P Company 6Refuge Refuge Wear Body Architecture Nexus Architecture Mobile Villages Modular Architecture Intervention Fluid Architecture 7Fluid Form The Blob The Fold Twisting Blurring Masking and Revealing Notes List of Credits Select Bibliography Index

An evolution-based generative design system : using adaptation to shape architectural form

Abstract: This dissertation dwells in the interstitial spaces between the fields of architecture, environmental design and computation. It introduces a Generative Design System that draws on evolutionary concepts to incorporate adaptation paradigms into the architectural design process. The initial aim of the project focused on helping architects improving the environmental performance of buildings, but the final conclusions of the thesis transcend this realm to question the process of incorporating computational generative systems in the broader context of architectural design. The Generative System [GS] uses a Genetic Algorithm as the search and optimization engine. The evaluation of solutions in terms of environmental performance is done using DOE2.1E. The GS is first tested within a restricted domain, where the optimal solution is previously known, to allow for the evaluation of the system's performance in locating high quality solutions. Results are very satisfactory and provide confidence to extend the GS to complex building layouts. Comparative studies using other heuristic search procedures like Simulated Annealing are also performed. The GS is then applied to an existing building by Alvaro Siza, to study the system's behavior in a complex architectural domain, and to assess its capability for encoding language constraints, so that solutions generated may be within certain design intentions. An extension to multicriteria problems is presented, using a Pareto-based method. The GS successfully finds well-defined Pareto fronts providing information on best trade-offs between conflicting objectives. The method is open-ended, as it leaves the final decision-making to the architect. Examples include finding best trade-offs between costs of construction materials, annual energy consumption in buildings, and greenhouse gas emissions embedded in materials. The GS is then used to generate whole building geometries, departing from abstract relationships between design elements and using adaptation to evolve architectural form. The shape-generation experiments are performed for distinct geographic locations, testing the algorithm's ability to adapt buildings shape to different environments. Pareto methods are used to investigate what forms respond better to conflicting objectives. New directions of research are suggested, like combining the GS with a parametric solid modeler, or extending the investigation to the study of complex adaptive systems in architecture. Thesis Supervisor: Leslie K. Norford Title: Associate Professor of Building Technology, Department of Architecture

NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search

Abstract: Neural architecture search (NAS) is a promising method for automatically design neural architectures. NAS adopts a search strategy to explore the predefined search space to find outstanding performance architecture with the minimum searching costs. Bayesian optimization and evolutionary algorithms are two commonly used search strategies, but they suffer from computationally expensive, challenge to implement or inefficient exploration ability. In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is defined from Bayesian optimization and we propose a graph-based uncertainty estimation network as a surrogate model that is easy to implement and computationally efficient. The second predictor is a graph-based neural network that directly outputs the performance prediction of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Extensive experiments demonstrate the superiority of NPENAS. Quantitative results on three NAS search spaces indicate that both NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-BO achieving state-of-the-art performance on NASBench-201 and NPENAS-NP on NASBench-101 and DARTS, respectively.