Parametric modelling software often maintains an explicit history of design development in the form of a graph. However, as the graph increases in complexity it quickly becomes inflexible and unsuitable for exploring a wide design space. By contrast, implicit low-level rule systems can offer wide design exploration due to their lack of structure, but often act as black boxes to human observers with only initial conditions and final designs cognisable. In response to these two extremes, the authors propose a new approach called Meta-Parametric Design, combining graph-based parametric modelling with genetic programming. The advantages of this approach are demonstrated using two real case-study projects that widen design exploration whilst maintaining the benefits of a graph representation.

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This paper describes the behaviour of restrained steel columns in fire. It follows the introduction of extra load into the column through the axial restraint of the surrounding cooler structure and the consequential buckling. Key to this understanding is the post-failure behaviour and re-stabilisation of the column, which is discussed with reference to a finite element model and an analytical model.Through bi-directional control of the temperature, the finite element model allows the snap-back behaviour to be modelled in detail and the effects of varying slenderness and load ratio are investigated. The analytical model employs structural mechanics to describe the behaviour of a heated strut, and is capable of explaining both elastic and fully-plastic post-buckling behaviour.Through this detailed explanation of what happens when a heated column buckles, the consequences for steel framed building design are discussed. In particular, the need to provide robustness is highlighted, in order to ensure alternative load-paths are available once a column has buckled and re-stabilised. Without this robustness, the dynamic shedding of load onto surrounding structures may well spread failure from a fire's origin and lead to progressive collapse.

Additive manufacturing methods 1-4 using static and mobile robots are being developed for both on-site construction 5-8 and off-site prefabrication 9, 10 . Here we introduce a new method of additive manufacturing, referred to as Aerial Additive Manufacturing (Aerial-AM), that utilizes a team of aerial robots inspired by natural builders 11 such as wasps who use collective building methods 12, 13 . We present a scalable multi-robot 3D printing and path planning framework that enables robot tasks and population size to be adapted to variations in print geometry throughout a building mission. The multi-robot manufacturing framework allows for autonomous 3D printing under human supervision, real-time assessment of printed geometry and robot behavioural adaptation. To validate autonomous Aerial-AM based on the framework, we develop BuilDrones for depositing materials during flight and ScanDrones for measuring print quality, and integrate a generic real-time model-predictive-control scheme with the Aerial-AM robots. In addition, we integrate a dynamically self-aligning delta manipulator with the BuilDrone to further improve manufacturing accuracy to 5mm for printing geometry with precise trajectory requirements, and develop four cementitious-polymeric composite mixtures suitable for continuous material deposition. We demonstrate proof-of-concept prints including a cylinder of 2.05m with a rapid curing insulation foam material and a cylinder of 0.18m with structural pseudoplastic cementitious material, a light-trail virtual print of a dome-like geometry, and multi-robot simulations.