Indoor arenas are important public buildings catering for various activities (e.g., sports events, stage performances, assemblies, exhibitions, and daily sports for the public) and serving as landmarks in urban contexts. The multi-functional space and long-span roof structure of an indoor arena are highly interrelated, which impact the multi-functionality and structural performance and mainly define the overall form of the building. Therefore, it is crucial to integrate the multi-functional space and long-span roof structure to formulate proper forms for indoor arenas, in order to satisfy various design requirements during the conceptual design. This thesis aims at formulating a computational design method, ‘Computational Design of Indoor Arena (CDIA)’, to support the conceptual design of indoor arenas by using the computational techniques of parametric modelling, Building Performance Simulations (BPSs), Multi-Objective Optimizations (MOOs), surrogate models based on Multi-Layer Perceptron Neural Network (MLPNN), and clustering based on Self-Organizing Map (SOM clustering). In the formulation of CDIA, these techniques are modified, improved and organized into five components and three workflows, to satisfy the demands of the conceptual design of indoor arenas.@en

During the early design process, simulations allow numeric assessment and 3D models allow visual inspection for qualitative criteria. However, exploring different design alternatives based on both is challenging. To support the design exploration of quantitative performance and geometry typology of various design alternatives during the early design stages of indoor arenas, this paper proposed a novel design method of SOM-MLPNN by combing self-organizing map (SOM) and multi-layer perceptron neural network (MLPNN), based on the inspiration of local linear mapping based on self-organizing map (SOM-LLM). In SOM-LLM or SOM-MLPNN, the SOM can support designers to explore the whole design space according to geometry typologies and provides reference/labelled inputs for LLM/MLPNN to approximate multiple quantitative performance data for various design alternatives. Both SOM-LLM and SOM-MLPNN are applied and compared in a design of indoor arena. Besides the development of the method, original contributions include 1) proposing two operations (using a large size of SOM network and using a small amount of input data to train the SOM network) to save the computational time and increase the accuracy in data approximation and 2) proposing a series of data visualizations to interpret the results and support design explorations in different ways.

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Indoor multi-functional sports arenas are a complex building type. Integration of the (multi-) functional space and of the large-span structure of the roof mainly determines the overall geometry of the building, and is one of the most challenging phases of the design. Several interdisciplinary numeric assessments and numerous solutions with diverse geometries (rather than just several specific types) should be considered to make informed design decisions. To support the design exploration in the early design stage for multi-functional arenas, this paper proposes a design process that is composed of a flexible parametric model, a framework of interdisciplinary assessment criteria, and multi-objective optimization (MOO) with post-process tools. The parametric model is defined based on the basic spatial composition of arenas and is flexible to provide a broader design space, including diverse solutions with three frequently-used structural types. The framework of assessment criteria includes indicators of viewing quality for spectators, acoustics, and structures, which can evaluate the design in different aspects. Based on certain assessment criteria, the MOO can be used to search for good designs in the broader space, and the post-process tools facilitate the designer to analyse the results. Two typical arenas (the Barclay Centre and the O2 Arena) are selected as real case studies to demonstrate the proposed process and assess the capacity. Results of the case studies validate the efficacy of the process and the necessity of the broader design space to include diverse solutions with multiple structural types.

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Indoor sports arenas are a kind of important public buildings, which require iconic architectural forms and well performing structures for the long-span roofs. Hence, during the early stage of an arena design, it is crucial for designers to define a proper building form based on integrated design exploration of both geometric typology and structural performance. To support such design exploration, this paper proposes a method based on SOM (self-organizing map)-clustering and structural performance simulation as well as SAG (Sports Arena Generator: a specific parametric model for arenas proposed by the authors). This method can support designers to explore designs according to both geometries and performance and also illustrate the relationships between geometric typology and specific performance values, which is crucial for both architectural design and the research about building performance. A hypothetic arena is used as an example to demonstrate and validate the method.@en

The multi-functional space of sports arena is highly related to the long-span structure. To support the integration of these two aspects, design optimization combining parametric modeling, performance simulations, and searching algorithm can be used. However, optimization is powerful in dealing with quantitative performance, but for some soft requirements on buildings, design exploration of geometries based on the judgments of architects is still necessary. Self-organizing map (SOM), as a model-based clustering algorithm, can be used to support this kind of explorations on geometric typology. Nevertheless, it is difficult to ensure the accuracy of clustering, especially for complex parametric models. To support the design exploration on geometry (besides the exploration on quantitative performance supported by optimization) during the conceptual design of sports arenas, this paper proposed a process based on a versatile and flexible parametric model for sports arenas and self-organizing map (SOM). Within this process, to increase the accuracy of SOM clustering, a pre-processing step for the parameters of design alternatives is also proposed. A design of a hypothetic sports arena is used as a case to demonstrate and verify the process.@en

The integration of architectural (functional spaces) and structural design is especially crucial for the conceptual design of large multi-functional sports halls, due to the strict regulations for functional spaces, the requirement of large-span structure, and the complex interrelationships between these two aspects. This on-going research aims at developing a computational method to support this integration during the conceptual design of large sports hall. This paper proposes a parametric modelling process to support a design exploration which is an important part of the computational method. A typical and simplified large multi-functional sports hall is used as an example case to demonstrate this process.@en