Abstract The architectural design process is characterised by continuously comparing different design alternatives throughout the whole design process. Collaborators from different fields of expertise need to work closely together and they are expected to give quick but sufficient feedback on the available design alternatives in order to proceed in the collaborative process. However, for structural engineers making these comparisons on the structural behaviour and efficiency is hard to do. The lack of appropriate tools disables the structural engineer to gain quickly and qualitatively insight into a set of design alternatives. This paper discusses a novel mind-set for structural designers, that fills this gap by informed decision-making based on data visualisation. This kind of data exploration is only rarely used in the structural design field. Since visual displays of data allow for better decision-making, this opportunity should be leveraged by structural designers. Five different bow-string bridge geometries are compared as a case study with the help of data visualisation. As a consequence, this paper focuses on the comparing practice of structural parametric models of the same structural typology. This research illustrates that the use of data visualisations can help designers to quickly understand the structural behaviour and performance of different design alternatives. This way, informed decision-making through comparing is facilitated.
[1]
Robert Woodbury,et al.
Elements of Parametric Design
,
2010
.
[2]
Lennert Loos,et al.
How to re-open the black box in the structural design of complex geometries
,
2016
.
[3]
Edward R. Tufte,et al.
Visual and Statistical Thinking: Displays of Evidence for Making Decisions
,
1997
.
[4]
Caitlin Mueller,et al.
Computational exploration of the structural design space
,
2014
.
[5]
Paul Shepherd,et al.
Meta-Parametric Design
,
2017
.
[6]
Xiaoye Yu.
Improving the efficiency of structures using mechanics concepts
,
2012
.
[7]
Tianjian Ji,et al.
Concepts for designing stiffer structures
,
2003
.
[8]
Daniel A. Keim,et al.
Information Visualization and Visual Data Mining
,
2002,
IEEE Trans. Vis. Comput. Graph..
[9]
Robert F. Woodbury,et al.
Whither design space?
,
2006,
Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
[10]
John Harding,et al.
Thinking Topologically at Early Stage Parametric Design
,
2013,
AAG.
[11]
Amaresh Chakrabarti,et al.
Towards an ‘ideal’ approach for concept generation
,
2003
.
[12]
H. L. Cox,et al.
The design of structures of least weight
,
1965
.