A building spatial design (BSD) determines external and internal walls and ceilings of a building. The design space has a hierarchical structure, in which decisions on the existence or non... Show moreA building spatial design (BSD) determines external and internal walls and ceilings of a building. The design space has a hierarchical structure, in which decisions on the existence or non-existence of spatial components determine the existence of variables related to these spaces, such as sizing and angles. In the optimization of BSDs it is envisioned to optimize various performance indicators from multiple disciplines in concert, such as structural, functional, thermal, and daylight performance. Existing representations of design spaces suffer from severe limitations, such as only representing orthogonal designs or representing the structures in parametric superstructure, allowing only for limited design variations. This paper proposes prism nets - a new way of representing the search space of BSDs based on triangulations defining space filling collections of triangular prisms that can be combined via coloring parameters to spaces. Prism nets can accommodate for non-orthogonal designs and are flexible in terms of topological variations. We follow the guidelines for representation and operator design proposed in the framework of metric-based evolutionary algorithms. The main contribution of the paper is a detailed discussion of the search space representation and corresponding mutation operators. Moreover, a proof of concept example demonstrates the integration into multi-objective evolutionary algorithms and provides first results on a simple, but reproducible, benchmark problem. Show less
Boonstra, S.; Blom, K. van der; Hofmeyer, H.; Emmerich, M.T.M. 2021
Three methods for early-stage building spatial design optimization are presented, demonstrated, and compared for their qualities and limitations. The first, an evolutionary algorithm, can find well... Show moreThree methods for early-stage building spatial design optimization are presented, demonstrated, and compared for their qualities and limitations. The first, an evolutionary algorithm, can find well-distributed approximations of the Pareto front, but it uses many design evaluations and it can only explore a limited part of the entire design search space (i.e. the collection of all possible design solutions). The second, simulations of co-evolutionary design processes, can find improved design solutions relatively fast within an unrestricted design search space, however, they typically only find discretely distributed Pareto front approximations. For the third method, hybridization is proposed to combine the first two methods into two new hybrid methods, such that their advantages are combined and their disadvantages are diminished. The methods have been applied in an initial case study, which shows that hybridization can improve search efficiency and speed, and it can search larger design search spaces. Show less
Boonstra, S.; Blom, K. van der; Hofmeyer, H.; Emmerich, M.T.M. 2020
Multi-disciplinary optimisation of building spatial designs is characterised by large solution spaces. Here two approaches are introduced, one being super-structured and the other super-structure... Show moreMulti-disciplinary optimisation of building spatial designs is characterised by large solution spaces. Here two approaches are introduced, one being super-structured and the other super-structure free. Both are different in nature and perform differently for large solution spaces and each requires its own representation of a building spatial design, which are also presented here. A method to combine the two approaches is proposed, because the two are prospected to supplement each other. Accordingly a toolbox is presented, which can evaluate the structural and thermal performances of a building spatial design to provide a user with the means to define optimisation procedures. A demonstration of the toolbox is given where the toolbox has been used for an elementary implementation of a simulation of co-evolutionary design processes. The optimisation approaches and the toolbox that are presented in this paper will be used in future efforts for research into- and development of optimisation methods for multi-disciplinary building spatial design optimisation. Show less
Boonstra, S.; Blom, K. van der; Hofmeyer, H.; Amor, R.; Emmerich, M.T.M. 2016
In multi-disciplinary building optimisation, solutions depend on the representation of the design search space, the latter being a collection of all solutions. This paper presents two design... Show moreIn multi-disciplinary building optimisation, solutions depend on the representation of the design search space, the latter being a collection of all solutions. This paper presents two design search space representations and discusses their advantages and disadvantages: The first, a super-structure approach, requires all possible solutions to be prescribed in a so-called super-structure. The second approach, super-structure free, uses dynamic data structures that offer freedom in the range of possible solutions. It is concluded that both approaches may supplement each other, if applied in a combination of optimisation methods. A method for this combination of optimisation methods is proposed. The method includes the transformation of one representation into the other and vice versa. Finally, therefore in this paper these transformations are proposed, implemented, and verified as well. Show less
