There are an increasing number of optimal-design paradigms used in architectural design nowadays. In these paradigms, a design task is formulated, or partially formulated, as an optimization problem. Multi-Disciplinary Optimization and Multi-Objective Optimization, as two important optimal-design paradigms, have shown their great potential in improving the performances of complex buildings in recent decades. Nevertheless, current paradigms for ill‑defined conceptual architectural design still lack ways to ensure the achievement of a reliable optimization problem, which hinders reliable design solutions despite the use of advanced optimization algorithms. To address this problem, it is necessary to shift the focus from Optimization Problem Solving to Optimization Problem Formulation. This research particularly focuses on knowledge‑supported, dynamic and interactive Optimization Problem Re-Formulation in order to construct a new Multi‑Objective and Multi-Disciplinary Optimization (MOMDO) method suitable for use in ill‑defined conceptual architectural design. The proposed method consists of two subtype methods: Non‑dynamic, Interactive Re-formulation method (Subtype-I) and Dynamic, Interactive Re‑formulation method (Subtype-II), which can be used to explore design space in a convergent and divergent manner respectively. To support the re-formulation, various kinds of information and knowledge need to be extracted by utilizing different computational techniques, such as advanced sampling algorithms, Self-Organizing Map, Hierarchical Clustering, Smoothing Spline Analysis of Variance, Two-Level Variable Structure and modular programming. Moreover, a software workflow that can provide these computational techniques is developed; it integrates McNeel’s Grasshopper, ESTECO's modeFRONTIER and simulation software tools Daysim, EnergyPlus and Karamba3D. With the support of this software workflow, the proposed method is demonstrated via two case studies concerning the conceptual design of indoor sports halls.@en

Simulation-Based Multi-Objective Optimization (SBMOO) methods are being increasingly used in conceptual architectural design. They mostly focus on the solving, rather than the re-formulation, of a Multi-Objective Optimization (MOO) problem. However, Optimization Problem Re-Formulation (Re-OPF) is necessary for treating ill-defined conceptual architectural design as an iterative exploration process. The paper proposes an innovative SBMOO method which builds in a dynamic and interactive Re-OPF phase. This Re-OPF phase, as the main novelty of the proposed method, aims at achieving a realistic MOO model (i.e., a parametric geometry-simulation model which includes important objectives, constraints, and design variables). The proposed method is applied to the conceptual design of a top-daylighting system, focusing on divergent concept generation. The integration of software tools Grasshopper and modeFRONTIER is adopted to support this application. The main finding from this application is that the proposed method can help to achieve quantitatively better and qualitatively more diverse Pareto solutions.

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The benefits of applying multi-objective optimization (MOO) in building design have been increasingly recognized in recent decades. The existing or traditional computational design optimization (CDO) approaches mostly focus on optimization problem solving (OPS), as they often conduct optimizations directly by assuming the optimization problems in question are good enough. In contrast, the computational design exploration (CDE) approaches defined in this research mainly focus on optimization problem formulation (OPF), which are considered more essential and aim to achieve or ensure appropriate optimization problems before conducting optimizations. However, the application of the CDE is very limited especially in conceptual architectural design. The necessity of re-formulating original optimization problems and its potential impacts on optimization results are often overlooked or not emphasized enough. This paper proposes a new CDE approach that highlights the knowledge-supported re-formulation of a changeable initial optimization problem. It improves upon the traditional CDO approach by introducing a changeable initial OPF and inserting a CDE module. The changeable initial OPF allows expanding the dimensionality of an objective space and design space being investigated, and the CDE module can re-formulate the changeable optimization problem using the information and knowledge extracted from statistical analyses. To facilitate designers in achieving the proposed approach, an improved computational platform is used which combines parametric modeling software (including simulation plug-ins) and design optimization software. Assisted by the platform, the proposed approach is applied to the conceptual design of an indoor sports building that considers multi-disciplinary performance criteria (including architecture-, climate- and structure-related criteria) and a wide range of geometric variations. Through the case study, this paper demonstrates the use of the proposed approach, verifies its benefits over the traditional method, and unveils the factors that may affect the behaviour of the proposed approach. Besides, it also shows the suitability of the computational platform used.

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The comparison of various competing design concepts during conceptual architectural design is commonly needed for achieving a good final concept. For this, computational design exploration is a key approach. Unfortunately, most of existing research tends to skip this crucial process, and purely focuses on the late-stage design optimization based on a single concept that, they assume, has been good enough or accepted already. This paper focuses on information or knowledge extracted from a multi-objective design exploration for the formulation of a good geometrical building design concept. To better support the exploration process, a new integration plug-in is developed to integrate parametric modelling software and process integration and optimization software. Through a case study that investigates the daylight and energy performances of a large indoor space, this paper 1) tackles the importance of design exploration on the formulation of a good design concept; 2) presents and shows the usability of the new integration plug-in for supporting the exploration process.@en

Surrogate-based Optimization is a useful approach when the objective function is computationally expensive to evaluate, compared to Simulation-based Optimization. In the surrogate-based method, analytically tractable “surrogate models” (also known as “Response Surface Models — RSMs” or “metamodels”), are constructed and validated for each optimization objective and constraint at relatively low computational cost. They are useful for replacing the time-consuming simulations during the optimization; quickly locating the area where the optimum is expected to be for further search; and gaining insight into the global behavior of the system. Nevertheless, there are still concerns about the surrogate model accuracy and the number of simulations necessary to get a reasonably accurate surrogate model. This paper aims to unveil: 1) the possible impacts of problem scale and sampling strategy on the surrogate model accuracy; and 2) the potential of Surrogatebased Optimization in finding high quality solutions for building envelope design optimization problems. For this purpose, a series of multi-objective optimization test cases that mainly consider daylight and energy performance were conducted within the same time frame. Then, the results were compared, in pair, based on which discussions were made. Finally, the corresponding conclusions were obtained after the comparative study.@en

Building performance simulations are usually timeconsuming. They may account for the major portion of time spent in Computational Design Optimization (CDO), for instance, annual hourly daylight and energy simulations. In this case, the optimization may become less efficient or even infeasible within a limited time frame of real-world projects, due to the computationally expensive simulations. To handle the problem, this research aims to investigate the potentials of surrogate models (i.e. Response Surface Methodology - RSM) to be used in the building envelope design exploration and optimization that consider visual and energy performance. Specifically, the work investigates how, and to what extent, 1) problem scales may affect the application of RSM, and 2) different ways of using RSM may affect the quality of Pareto Front approximations. Thus, a series of multi-objective optimization tests are carried out; preliminary discussion is made based on the current results.@en

The design of sport buildings has great impact on top-sport as well as on recreational sport-activities. It implies challenging tasks in meeting the performance-requirements. This includes the control of factors like daylight/lighting, air flow, thermal conditions, just to name a few. Such factors impact the performance of athletes and are hard to control in large sport halls; their control is even harder when the public/audience is located within the halls and require different climate conditions. While mechanical installations are often needed during competitions in order to guarantee constant conditions, relaying on mechanical installations during the daily and recreational use of the venues challenges their medium/long term sustainability. Computational form finding approaches can favour the achievement of high-performing and sustainable sport buildings. In this light, the paper tackles the use of Multi-objective and Multidisciplinary design optimization. The paper presents the concept of Multi-objective Multidisciplinary design optimization techniques to support trade-off decisions between multiple conflicting design objectives and interdisciplinary design methodology, during the conceptual design of sport buildings. The proposed method is based on parametric modelling, performance simulation tools and algorithms for computational optimization, for which the paper tackles three specific aspects. First of all, due to the complexity of large sport buildings, the formulation of the optimization and the screening of the related design variables is crucial in order to obtain a meaningful design space, which helps reducing unnecessary computational burden. Secondly, assessing performance based on measurements and analyses is crucial and can be supported by performance simulations tools; however effectively integrating performance simulations tools in the early phase of the design requires new tools. In this light, a customized computational process for the rapid assessment of temperature and airflow patterns is presented. Thirdly, the process requires the combination of design optimization and design exploration, while searching for well-performing solutions. The importance of design exploration is emphasized also for sub-optimal solutions. In order to facilitate the design exploration, the combination of optimization algorithms, multi-variate analysis algorithms and options for exploring design solutions via an interactive dashboard connected to a database are presented. To exemplify the method, specific case studies are developed as collaboration between Delft university of Technology and South China university of Technology.@en

In large sport’s buildings, a big part of energy can be saved by providing natural instead of mechanical ventilation. However, additional challenges arise while controlling airflow and temperatures in different zones. These measures highly depend on the shape, construction and ventilation openings, which are mostly decided in the early design stages. Computational optimization can support these early stages of design, but needs to be performed in efficient ways. In this respect, the project proposes rapid assessment of temperature and airflow patterns using customized Grasshopper components, which would be able to evaluate a given model using CONTAM and EnergyPlus software as simulation engine. The proposed method integrates these simulations within an environment, which is familiar to architects and is largely used for parameterization of design in its early stages. A case study (Jiangmen Sports Center, Jiangmen, China) is used to test the developed process for a large indoor sports hall.@en

Parametric modelling allows quick generation of a large number of design alternatives. Ultimately, it can be combined with optimization algorithms for obtaining optimal performance-driven design. However, setup of design space for optimization is a very complex task requiring designer’s a priori knowledge and experience. Therefore, this paper focuses on the process that happens before the optimization. It proposes to use multivariate analysis algorithms for exploring and understanding the relations between various design parameters, after sampling the design space. Additionally, portrayal of geometry is introduced as an extension of conventional visualization methods, which accounts for evaluation of ill-defined design criteria by using designer’s expertise. The proposed method is computationally efficient and integrated into an environment familiar to architects. It relies on multivariate analysis algorithms together with database querying capabilities and an interactive dashboard developed for geometry portrayal.@en