ARTICLE ANALYSIS

Analysis of article using Artificial Intelligence tools





Id 864
Author Bonev I., Shimura S., Fricke H.H., Tsukamoto Y.
Title Design and construction methodology for de-institutional architecture: A case study on “Lakeside Dancers Club” at Horst Festival
Reference

Bonev I., Shimura S., Fricke H.H., Tsukamoto Y.; Design and construction methodology for de-institutional architecture: A case study on “Lakeside Dancers Club” at Horst Festival ;AIJ Journal of Technology and Design vol:25.0 issue: 61.0 page:1351.0

Keywords Clubs; Commonality; Conviviality; De-institution; Resourceful Design
Link to article https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076959854&doi=10.3130%2faijt.25.1351&partnerID=40&md5=0362ebfc934bcf836fbdd6caba7693b7
Abstract De-institution refers to social and architectural practices which provide alternatives to institution- and industry-centered models. It engages with the potential of communities by recognizing common behaviors, local resources, and their spatial manifestation. The concept of de-institution practically interprets the ideas of the philosopher Ivan Illich, and is incidentally discovered in the work of architecture collectives around the world. This paper reports on the social framework, design and construction process of Lakeside Dancers Club as a case of a de-institutionalized club. It aims to elaborate on the architectural challenges and their creative solutions to building a dancing space with social impact. © 2019 Architectural Institute of Japan. All rights reserved.

Metodology

Technique

Keyword Find research methods used
Tentative Keyword Show Candidate Transition Variables for article (AI method)
Summary Summary for article (AI method)
Categories Find category for article (AI method)
Crossover theme Find social impact for article (AI method)
Wordcloud Show WordCloud from article (AI method)
Article semantic search Article semantic search (AI method)
Find semantically similar articles Find semantically similar articles (Semantic search)
Similar articles Knowledge graph for article