Knowledge-rich optimisation of prefabricated facades to support conceptual design

被引:15
|
作者
Montali, Jacopo [1 ]
Sauchelli, Michele [2 ]
Jin, Qian [3 ]
Overend, Mauro [1 ]
机构
[1] Univ Cambridge, Dept Engn, Glass & Facade Technol Res Grp, Trumpington St, Cambridge CB2 1PZ, England
[2] Laing ORourke Plc, Engn Excellence Grp, Bridge Pl,1-2 Anchor Blvd, Crossways DA2 6SN, Dartford, England
[3] Tongji Univ, Coll Architecture & Urban Planning, 1239 Siping Rd, Shanghai 200092, Peoples R China
基金
英国工程与自然科学研究理事会;
关键词
Facade design; Design optimisation; Knowledge-based approaches; Design for manufacture and assembly; ADAPTIVE INSULATION SYSTEMS; PART; PERFORMANCE;
D O I
10.1016/j.autcon.2018.11.002
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
One of the principal challenges in facade design is to support the architectural intent by devising technically viable (i.e. standard-compliant and manufacturable) solutions from as early as possible in the design stage. This is increasingly relevant as prefabricated facades increase in complexity and bespokedness in response to current societal, financial and environmental challenges. In this paper a process that addresses this challenge is presented. The process consists of two sub-processes 1) to build product-oriented knowledge bases and digital tools for supporting design on a project-by-project basis and 2) to help designers identify a set of optimal solutions that consider the facade-specific trade-off between architectural intent and performance requirements, while meeting the largest number of production-related constraints. This process was applied to a case study and the results were compared with those obtained from a recently-developed facade. It was found that, although the proposed process produces optimal solutions that are approximated, designers can benefit from more control over the product's manufacturability, performance and architectural intent in less time.
引用
收藏
页码:192 / 204
页数:13
相关论文
共 50 条
  • [41] Visual knowledge specification for conceptual design: Definition and tool support
    Kraft, Bodo
    Nagl, Manfred
    [J]. ADVANCED ENGINEERING INFORMATICS, 2007, 21 (01) : 67 - 83
  • [42] Constrained functional knowledge modelling and clustering to support conceptual design
    Hu, X.
    Hu, J.
    Peng, Y.
    Cao, Z.
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2012, 226 (C5) : 1326 - 1337
  • [43] Evaluating Knowledge-poor and Knowledge-rich Features in Automatic Classification: A Case Study in WSD
    Zampieri, Marcos
    [J]. 13TH IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND INFORMATICS (CINTI 2012), 2012, : 359 - 363
  • [44] Sequence effects in solving knowledge-rich problems: The ambiguous role of surface similarities
    Scheiter, K
    Gerjets, P
    [J]. PROCEEDINGS OF THE TWENTY-FIFTH ANNUAL CONFERENCE OF THE COGNITIVE SCIENCE SOCIETY, PTS 1 AND 2, 2003, : 1035 - 1040
  • [45] Learning to Mine Definitions from Slovene Structured and Unstructured Knowledge-Rich Resources
    Fiser, Darja
    Pollak, Senja
    Vintar, Spela
    [J]. LREC 2010 - SEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2010, : 2932 - 2936
  • [46] Hunting for a linguistic phantom A corpus-linguistic study of knowledge-rich contexts
    Schumann, Anne-Kathrin
    [J]. TERMINOLOGY, 2014, 20 (02): : 198 - 224
  • [47] Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial Video
    Li, Pu
    Liu, Xiaobai
    [J]. 2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2021, : 10584 - 10591
  • [48] RETHINKING OF COMPUTATION FOR FUTURE-GENERATION, KNOWLEDGE-RICH SPEECH RECOGNITION AND UNDERSTANDING
    Deng, Li
    [J]. ICME: 2009 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-3, 2009, : 1797 - 1800
  • [49] An Efficient Many-Class Active Learning Framework for Knowledge-Rich Domains
    Shi, Weishi
    Yu, Qi
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), 2018, : 1230 - 1235
  • [50] Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods
    Chasin, Rachel
    Rumshisky, Anna
    Uzuner, Ozlem
    Szolovitsl, Peter
    [J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2014, 21 (05) : 842 - 849