AbstractThe challenges associated with the construction of large-scale, doubly-curved space-frame structure are significant. This research centres on the development of a novel framework for the reduction of their construction complexity of space-frames by reducing the geometrical variability in their joints and enhancing standardisation. Conway operators are applied to generate an extensive design-space of topologically uniform space-frame configurations, that enables the exploration of materially efficient, and innovative, modular layouts. A novel method for the comparison of the geometry of their joints is then developed, that is invariant under any rotation. This serves as the basis for the evaluation of geometrical variability in a structure and the assessment of its construction complexity, when overlaid with the properties of different fabrication processes. The geometry optimisation of complex, large-scale structures is therefore enabled to reduce the variability in their members and facilitate their construction. The parameters of the computational workflow established can be adjusted, depending on the stage of the project in which the optimisation is carried out, to improve its performance. This workflow therefore suggests an overall shift of the complexity from the construction to the design process, where it can be dealt with by the application of the advanced analysis tools developed. It facilitates the construction of complex structures, promoting an informed application of fabrication processes and thus generating better-engineered solutions.
|Date of Award||24 Jun 2020|
|Supervisor||Paul Shepherd (Supervisor) & Mark Evernden (Supervisor)|
- machine learning
Optimising space-frames for construction
Koronaki, A. (Author). 24 Jun 2020
Student thesis: Doctoral Thesis › PhD