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We combined classroom training with observation of teaching in the clinical area, and by doing so were more able to translate classroom theory into authentic workplace practice.

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While structural optimisation is usually handled by iterative methods requiring repeated samples of a physics-based model, this process can be computationally demanding. Given a set of previously optimised structures of the same topology, this paper uses inductive learning to replace this optimisation process entirely by deriving a function that directly maps any given load to an optimal geometry. A support vector machine is trained to determine the optimal geometry of individual modules of a space frame structure given a specified load condition. Structures produced by learning are compared against those found by a standard gradient descent optimisation, both as individual modules and then as a composite structure. The primary motivation for this is speed, and results show the process is highly efficient for cases in which similar optimisations must be performed repeatedly. The function learned by the algorithm can approximate the result of optimisation very closely after sufficient training, and has also been found effective at generalising the underlying optima to produce structures that perform better than those found by standard iterative methods.

Style is a broad term that could potentially refer to any features of a work, as well as a fluid concept that is subject to change and disagreement, yet approaches to representing it too often seek either a pre-defined set of generative rules or list of measurable features. Instead, a general and flexible method of retrospectively and automatically representing style is proposed based on the idea of an archetype, to which real designs can be compared, and tested with examples of architectural plans. Unlike a fixed, symbolic representation, both the measurements of features that define a style and the selection of those features themselves can be performed by the machine, making it able to generalise a definition automatically from a set of examples.This process is implemented in analysis, and coupled with a generative algorithm to produce plans in a learned style.

The design methodology explained in this paper 1 takes a substantial shift from conventional methods where sizing is based on a single load case i.e. the maximum expected load. The difference from a conventional passive approach is that strategically located elements of the system provide controlled output energy (actuators) in order to manipulate actively the internal flow of forces and stresses. In this way stresses can be homogenized and deflections kept within desired limits. The alternative we are proposing offer a way to actively counteract loads when needed. Two dimensional pin-jointed trusses designed using this methodology show that substantial weight savings can be achieved respect to optimised "passive" structures (designed using Fully Utilised Design method). While the decrease in mass through actuation leads to reduction of embodied energy, it increases the operating energy that the active elements need to provide. Whole life energy analysis, implemented as coupled optimization between embodied and operating energy, reveals that an optimal trade-off exists. Results show that energy savings remain significant even considering the operating energy of the actuators for the entire life-cycle of the structure.

A framework for calculating a weighted random walk on an urban street segment network is described, and tested as a predictor of pedestrian and vehicle movement in London and the wider region. This paper has three aims. First, it proposes the simplest possible model of agency in that individuals have neither memory, goals nor knowledge of the network beyond street segments immediately visible at an intersection. Second, it attempts to reconcile two divergent approaches to urban analysis, graph centrality measures and agent simulation, by demonstrating properties of topological graphs emerge from the lowest level agent behaviour. Third, it aims for far faster computation of relevant features such as the foreground street network and prediction of movement than currently exists. The results show that the idealised random walk predicts observed movement as well as the best existing centrality measures, is several orders of magnitude faster to calculate, and may help to explain movement without perfect knowledge of the map, by demonstrating the street network is structured such that long range information on optimal paths correlates with geometrical features locally visible at each intersection.