Architects must deal with increasing amount of design constraints, which is the consequence of increasing demands on building’s performance in terms of sustainability and construction cost. On the other hand, complex geometries has become common part in architectural projects. Therefore, it is nowadays more true than before that the building’s qualities depend on architect’s ability to find the optimal solution for all, often contradicting constraints. This is a task for which due to the complexity necessitates the use of sophisticated solving algorithms integrated into the design workflow. The research proposes an integration of optimization apparatus called “ Cognitive Control System” (CCS) into a parametric design framework. Cognition or “ knowing ” is here defined in terms of the ability to respond to the performative criteria of a building by finding optimum solution. The CCS contains a set of global and local solvers. Its part is also an interface, the Interactive Graph Control (IGC) by which the user can steer and control the optimization process in a transparent fashion. This interactive platform presents the user not only the best optimal solution, but also the whole range of other possible solution, even if less optimal. The research examines several types of nonlinear solving algorithms, such as genetic algorithms, neural networks, and numerical mathematical solvers. The research reveals their pros and cons and demonstrates how these different types of algorithms can be integrated into parametric system to enhance the design process. The thesis presents how to set up an objective function for multiple objectives and how the function affects the optimization process. The functionality and usability of the solvers is demonstrated on several case studies. The case studies are performed on different scale projects with different solving complexity. The cases cover range of different geometrical and design topics, such as generating free-form roof structure with certain local height constraints, optimizing family house towards low energy consumption, daylight and cost or exploring the design options for museum building.

Cognitive Building Systems. Optimization Algorithms in Architecture from Design to Production

The main pedestrian entrance of the building site has a direct influence on the use of the buildings, so the selection of the main pedestrian entrance is very important in the process of architectural design. The correct selection of the main pedestrian entrance of building site depends on the experience of designers and environment data collected by designers, the process is time consuming and inefficient, especially when the building site located in complex urban environment. In order to improve the efficiency of design process, we used online map to collect museums information in China as training samples, and constructing artificial neural networks to predict the direction of the main pedestrian entrance. After the training, we get the prediction model with 79% prediction accuracy. Although the accuracy still need to be improved, it creates a new approach to analysis the main pedestrian entrance of the site and worth further researching.

Automatic Design of Main Pedestrian Entrance of Building Site Based on Machine Learning - A Case Study of Museums in China's Urban Environment

This research explores the possibilities of integrating environmental and human inputs to achieve precise architectural goals. Specifically, the aim is to create an adaptive facade, trained on historical data relating to human (an override capability) and environmental inputs to maintain optimal internal lighting conditions for inhabitants. The study was conducted using a physical louvreed shading system constructed in the Bartlett School of Architecture, University College London. The historical data was collected by the system over a one-week period and provided a sample data set to train the Artificial Neural Network (ANN) for which the system would operate. A multi-layer perceptron was the neural network used in the study and a series of experiments allowed for the optimal network architecture to be ascertained. Based on the trained network, further testing was carried out to assess the accuracy of the results with regards to the louvre angle suggested during system recall. It was found that the complexity derived from receiving both environmental and human data provided some confusion when recalling, however the system displayed a high level of accuracy, correctly recalling the desired blade angle over 70% of the time. Further testing found that the remaining recall error could be accounted for through environmental input data similarities. By physically building and testing the system this research suggests that a trained physical system based on computational principles can provide an adaptive architectural entity that considers building occupants behaviour and wants as well as the external environments natural imposition.

Neural network architectures for a user overridable dynamic shading system

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Neural network architectures for a user overridable dynamic shading system