Evolutionary building layout optimisation
2011-05-03T09:33:13Z (GMT) by
Space layout planning (SLP) is the organisation of functional/living spaces (spatial units-SUs) and corridors/access paths of a building satisfying requirements (e.g. accessibility, adjacency etc.) to achieve design goals (e.g. minimising unutilised space and travelling cost). Out of many ways of arranging SUs, a human designer may consider only a handful of alternatives due to resource limitations (e.g. time and effort). To facilitate this task, decision support for SLP design can be obtained using computer technology. Despite being highly combinatorial, many attempts have been made to automate SLP. However in the majority of these, the SUs are arranged in a fixed building footprint/boundary, which may limit exploration of the entire solution space. Thus, it is aimed to develop a space layout optimisation system that allows SUs to position themselves in a building site to satisfy design goals. The objectives of the research are to: understand architectural SLP and optimisation; assess the need for automation of SLP optimisation; explore methods to formulate the SLP optimisation problem; develop a prototype system to optimise SLP based on building design guidelines, and evaluate performance for its strengths and weaknesses using case studies. As early stages of building design are found to be most e ective in reducing the environmental impact and costs, it is also aimed to make provisions for integrating these aspects in SLP. To address the first three objectives, a literature review was conducted. The main finding of this was the current need for an optimisation tool for SLP. It also revealed that genetic algorithms-GA are widely used and show promise in optimisation. Then, a prototype space layout optimisation system (Sl-Opt) was developed using real-valued GA and was programed in JavaR. Constrained optimisation was employed where adjacency and accessibility needs were modelled as constraints, and the objective was to minimise the spread area of the layout. Following this, using an office layout with 8 SUs, Sl-Opt was evaluated for its performance. Results of the designed experiment and subsequent statistical tests showed that the selected parameters of GA operators influence optimisation collectively. Finally using the best parameter set, strengths and weaknesses of Sl-Opt were evaluated using two case studies: a hospital layout problem with 31 SUs and a problem with 10 non-rectangular SUs. Findings revealed that using the selected GA parameters Sl-Opt can successfully solve small scale problems of about less than 10 SUs. For larger prob- lems, the parameters need to be altered. Case studies also revealed that the system is capable of solving problems with non-rectangular SUs with varied 0rientations. Sl-Opt appear to have potential as a building layout decision support tool, and in addition, integration of other aspects such as energy efficiency and cost is possible.