S Sjonnie Boonstra

Multicriteria building spatial design with mixed integer evolutionary algorithms

This paper proposes a first step towards multidisciplinary design of building spatial designs. Two criteria, total surface area (i.e. energy performance) and compliance (i.e. structural performance), are combined in a multicriteria optimisation framework. A new way of representing building spatial designs in a mixed integer parameter space is used within this framework. Two state-of-the-art algorithms, namely NSGA-II and SMS-EMOA, are used and compared to compute Pareto front approximations for problems of different size. Moreover, the paper discusses domain specific search operators, which are compared to generic operators, and techniques to handle constraints within the mutation. The results give first insights into the trade-off between energy and structural performance and the scalability of the approach.

A super-structure based optimisation approach for building spatial designs

Building design can be supported effectively by computer-aided design exploration. This paper investigates optimisation based on a mixed-integer super-structure representation of the search space of building spatial designs. It can take into account parametric as well as topological variations. In the suggested super-structure – the so-called supercube representation – discrete and continuous variables determine the existence, respectively, dimensioning of spaces of the building spatial design. Constraints are formulated as closed form equations and can be used to numerically assess the feasibility of designs. A population-based constraint-handling evolutionary strategy is developed. In the constraint handling repair and penalty methods are combined in a domain specific way. The method is tested on different search space sizes and first promising results are reported.

Toolbox for super-structured and super-structure free multi-disciplinary building spatial design optimisation

Abstract 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 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.

The generation of hierarchic structures via robust 3D topology optimisation

Commonly used building structures often show a hierarchic layout of structural elements. It can be questioned whether such a layout originates from practical considerations, e.g. related to its construction, or that it is (relatively) optimal from a structural point of view. This paper investigates this question by using topology optimisation in an attempt to generate hierarchical structures. As an arbitrarily standard design case, the principle of a traditional timber floor that spans in one direction is used. The optimisation problem is first solved using classical sensitivity and density filtering. This leads indeed to solutions with a hierarchic layout, but they are practically unusable as the floor boarding is absent. A Heaviside projection is therefore considered next, but this does not solve the problem. Finally, a robust approach is followed, and this does result in a design similar to floor boarding supported by timber joists. The robust approach is then followed to study a floor with an opening, two floors that span in two directions, and an eight-level concrete building. It can be concluded that a hierarchic layout of structural elements likely originates from being optimal from a structural point of view. Also clear is that this conclusion cannot be obtained by means of standard topology optimisation based on sensitivity or density filtering (as often found in commercial finite element codes); robust 3D optimisation is required to obtain a usable, constructible (or in the future: 3D printable) structural design, with a crisp black-and-white density distribution.

Configuring advanced evolutionary algorithms for multicriteria building spatial design optimisation

In this paper solution approaches for solving the building spatial design optimisation problem for structural and energy performance are advanced on multiple fronts. A new initialisation operator is introduced to generate an unbiased initial population for a tailored version of SMS-EMOA with problem specific operators. Improvements to the mutation operator are proposed to eliminate bias and allow mutations consisting of multiple steps. Moreover, landscape analysis is applied in order to explore the landscape of both objectives and investigate the behaviour of the mutation operator. Parameter tuning is applied with the irace package and the Mixed Integer Evolution Strategy to find improved parameter settings and explore tuning with a relatively small number of expensive evaluations. Finally, the performances of the standard and tailored SMS-EMOA algorithms with tuned parameters are compared.

Coupling between a building spatial design optimisation toolbox and BouwConnect BIM

This paper presents a framework in which a building spatial design optimisation toolbox and a building information modelling environment are coupled. The coupling is used in a case study to investigate the possible challenges that hamper the interaction between a designer and an optimisation method within a BIM environment. The following challenges are identified: Accessibility of optimisation methods; Discrepancies in design representations; And, data transfer between BIM models. Moreover, the study provides insights for the application of optimisation in BIM.

Evaluating Memetic Building Spatial Design Optimisation Using Hypervolume Indicator Gradient Ascent

In traditional, single objective, optimisation local optima may be found by gradient search. With the recently introduced hypervolume indicator (HVI) gradient search, this is now also possible for multi-objective optimisation, by steering the whole Pareto front approximation (PFA) in the direction of maximal improvement. However, so far it has only been evaluated on simple test problems. In this work the HVI gradient is used for the real world problem of building spatial design, where the shape and layout of a building are optimised. This real world problem comes with a number of constraints that may hamper the effectiveness of the HVI gradient. Specifically, box constraints, and an equality constraint which is satisfied by rescaling. Moreover, like with regular gradient search, the HVI gradient may overstep an optimum. Therefore, step size control is also investigated. Since the building spatial designs are encoded in mixed-integer form, the use of gradient search alone is not sufficient. To navigate both discrete and continuous space, an evolutionary multi-objective algorithm (EMOA) and the HVI gradient are used in hybrid, forming a so-called memetic algorithm. Finally, the effectiveness of the memetic algorithm using the HVI gradient is evaluated empirically, by comparing it to an EMOA without a local search method. It is found that the HVI gradient method is effective in improving the PFA for this real world problem. However, due to the many discrete subspaces, the EMOA is able to find better solutions than the memetic approach, albeit only marginally.