This research develops a spatial transformation model to work as a tool to address all possible layouts that can be expected when a building is changed. This tool provides tangible answers to the different spatial configuration possibilities of an apartment that can exist within one building design. It combines existing knowledge about the flexibility and adaptability of building elements with spatial and configurative information about an meant to generate alternative solutions. It can help the designer to explore variations, educate them on the different possibilities, support their decision-making, provide custom-fit solutions, and collaborate between different actors within a housing project. The main research question asked in this research is: “How can the extent of spatial alternatives of an apartment configuration be explicated within a building design?”. First an apartment building is deconstructed and its constituent parts are analyzed on their spatial flexibility implications. The second part of the research focuses on the definition and requirements of spaces. Every space inside a two person starter’s apartment is evaluated on their configurational, ergonomic, and legislative constraints. The third part of the research combines the building constraints and the spatial definitions of the room to build a layout generator. Finally the floorplans, that are generated by this model are evaluated on their validity and veracity. The overall functionality of the model is compared to an existing spatial flexibility assessment model and a spatial generative model.

Designing structures that are more sustainable is a relevant topic within the construction industry. By choosing materials that have a low embodied energy value and optimizing the structures that can be constructed by these materials, one could potentially minimize the economical and environmental footprint of a structural design.

As burnt clay bricks have a relatively low embodied energy value, are relatively cheap as a construction material and are relatively durable, it is interesting to investigate the optimization of masonry structures. To achieve this, use is made of topology optimization. To accurately optimize the topology of masonry structures however, this optimization must be performed based on the results of a discrete element analysis. This thesis presents several methods to set up such a model based on masonry structures of arbitrary size and lay-out, departing from the smallest scale: the individual brick.

First, a method is developed to create arbitrary shapes for bricks. An algorithm is developed to parametrically create structures for two distinct shapes. A procedure to abstract these structures and translate their geometrical representation to a simplified numerical model is then presented. Several
methods for structural analyses are detailed and their results are evaluated and compared. The results of these analyses are used to optimize the topology of the initial structure by means of the Method of Moving Asymptotes. The resulting structures are then verified using 3DEC. Finally, some applications of the developed method are presented along with future visions.

Hospitals are known as functionally complex buildings in various ways, namely due to their non-trivial spatial connectivity requirements. A spatial configuration has an impact on human behavior, human movement patterns and should match with the operational logic of the buildings. In hospitals, there are several typical problems that can be attributed to the configuration of the building, namely the inefficient circulation of medical staff, difficult way-finding for visitors, lengthy and complex procedures for patients, long walking times, privacy, hygiene issues and so on.
This Ph.D. research aims to develop a computational design methodology for configurational layout optimization of hospital buildings concerning physical matters & human factors, which are directly attributable to the layout/configuration of the hospital. In the optimization models, the considered performance indicators are related with patients (e.g. ease of way-finding), staff (e.g. average walking-time), and operations (e.g. fitness for workflows). Two case studies are studied here as (1) reconfiguration of existing hospitals; and (2) designing the new hospitals by focussing on “layout planning” and “corridor design”. The developed models are programmed in the form of design tool-kits for supporting conceptual design phases.
Effectively, this project presents an interdisciplinary methodological framework that can tackle hospital layout design problems by integrating Computational Design workflows, Graph Theory techniques, Operations Research, and Computational Intelligence into the field of Architectural Space Planning.

Population growth and urbanisation trends bring many consequences related to the increase in global energy consumption, CO2 emissions and a decrease in arable land per person. High‑rises have been one of the inevitable buildings of metropoles to provide extra floor space since the early examples in the 19th century. Therefore, optimisation of high-rise buildings has been the focus of researchers because of significant performance enhancement, mainly in energy consumption and generation. Based on the facts of the 21st century, optimising high-rise buildings for multiple vital resources (such as energy, food, and water) is necessary for a sustainable future.

This research suggests “self-sufficient high-rise buildings” that can generate and efficiently consume vital resources in addition to dense habitation for sustainable living in metropoles. The complexity of self-sufficient high-rise building optimisation is more challenging than optimising regular high-rises that have not been addressed in the literature. The main challenge behind the research is the integration of multiple performance aspects of self-sufficiency related to the vital resources of human beings (energy, food, and water) and consideration of large numbers of design parameters related to these multiple performance aspects. Therefore, the dissertation presents a framework for performance optimisation of self-sufficient high-rise buildings using artificial intelligence focusing on the conceptual phase of the design process. The output of this dissertation supports decision-makers to suggest well-performing high-rise buildings involving the aspects of self sufficiency in a reasonable timeframe.

As part of the passive design strategy, the development of computational solar envelopes plays a major role to construct a cooperative environmental performance exchange between new buildings and their local contexts. However, the state-of-the-art computational solar envelopes pose a great challenge in understanding site characteristics from a given context. Existing methods predominantly construct 3D context models based on basic architectural geometric shapes, which are often isolated from the surrounding properties of local contexts (i.e., vegetation, materials). Thus, they only focus on context-oriented buildings and energy quantities that unfortunately lack a contextual solar performance analysis. It is clear that this condition may result in a fragmented understanding of the local context during the design and simulation process. With the potential application of attribute point cloud information, it is necessary to consider relevant parameters such as surface and material properties of existing contexts during the simulation of solar geometries, which are currently absent in computational frameworks. As such, the new method is required to enable architects not only to measure specific performances of the local context but also to identify vulnerable areas that may affect the proposed design. This research focuses on exploring an integrated computational design method for solar geometry based on solar and shading envelopes, and geometric and radiometric information from point cloud data. In particular, two computational models consisting of SOLEN (Subtractive Solar Envelopes) and SHADEN (Subtractive Shading Envelopes) are developed, which are applied to temperate and tropical climates, respectively. In design practice, these models help architects to produce informed-design decisions towards high-performed building massing.

Identification of design solutions for a built environment that caters to the human needs at all levels, and more specifically, to the needs of the clients and the society, is the main task addressed by architectural design. Architectural design is a prime example of a design task that is characterized by a high degree of complexity. Architectural design problems by definition entail relationships between decisions and objectives that are all but transparent. For the decision-maker to be able to guide design towards fulfilling objectives, a ‘closed-loop’ approach where variations in design solutions are generated and evaluated in an iterative process is employed. Due to the sheer number of alternative solutions to problems of even a moderate scale (due to combinatorial explosion), it is only feasible to iterate over a minuscule fraction of possible solutions. Design intuition of the professionals involved in design is a strong driving force behind the identification of design direction, in which alternatives are explored as part of the preliminary design process. This is an approach that depends on the human cognitive capabilities to navigate the design space and identify potentially promising solutions. Regardless, the complexity associated with architectural design often poses significant challenges to human cognition. Human cognition, while formidable in its ability to flexibly and efficiently navigate challenging environments, is faced with difficulties in addressing the complexity factors outlined previously, namely: the excessive (combinatorially explosive) number of potential solutions to architectural problems, the complex and non-linear relations between objects and their properties and the conflicting nature of design goals that architectural design entails. Thus, design professionals are often faced with the real threat that their decisions may be biased due to the natural limitations of human cognition acting in complex environments.

Due to the reasons highlighted above, a systematic approach to design space exploration must be undertaken, to maximize the potential for discovering optimal solutions to design problems. Due to the nature of such problems that entail multiple conflicting objectives, a single best solution is generally not attainable. Nonetheless, best-tradeoff solutions are distinguished and highly desirable for such multi-objective design problems. The field of Computational Intelligence, and within that in particular Evolutionary Computation-based (EC) intelligent approaches, offer a lucrative option as decision-support tools in design, as they are able to efficiently address the aforementioned proponents of design complexity. EC approaches are able to navigate the design space efficiently and systematically, considering multiple conflicting objectives and hard constraints, and being able to deal with arbitrary relations between design decision variables and design objectives.

In today's setting, products of architecture must lead the way to a sustainable and environmentally friendlier society. As such, the performance of buildings has become the main driving force behind the design process, being referred to as ``performance-driven design''. This initiative emphasizes the quantitative evaluation of a design's function in accordance with established design objectives, related to aspects such as energy performance, visual and thermal comfort, cost and environmental footprint, etc. Simulation-based tools that enable accurate design evaluation are gaining ground and offering valuable insight into the performance of buildings. Nonetheless, making decisions in this multi-objective environment is not trivial, and, as stipulated above, may be challenging to human cognition. Thus, in today’s setting where the quantitative performance of buildings keeps gaining ground, the research on the application of EC in architectural design is high on the scientific agenda.

Recognizing the impact design complexity has on architectural design and the potential that EC-based approaches offer in addressing it, this thesis proposes a comprehensive computational intelligence decision support system that combines components based on intelligence with ones based on cognition, with the ultimate aim of enabling decision-makers manage design complexity and improve decision making. In particular, this thesis adopts the theoretical standpoint that efficient navigation of an unknown environment assumes a fusion of intelligence and cognition. In this sense, and given the already widespread adoption of intelligent approaches (such as EC mentioned above), the main contribution of this thesis is to endow the intelligent approach with cognitive facilities, so as to improve its efficiency to the point that it is readily applicable to the early stages of the architectural design process.

Fusion of intelligent with cognitive approaches, as outlined in the approach proposed by this thesis, offers the unique advantage of a decision support approach that is both powerful, owing to the extensive capabilities of intelligent search algorithms, and flexible, owing to the extensive knowledge modeling capabilities of cognitive approaches. As such, it is uniquely suited to the early conceptual design stage where the need to explore large design spaces, flexibly redefine the design problem, and satisfy preferences that are not included in the primary design goals, are all paramount.

Thus, the word ``comprehensive'' as it appears on this thesis' title obtains a twofold meaning: On one hand comprehension as in the combination of computational intelligence and cognition in a single approach; on the other hand, as in \textit{comprehension} of the environment, the result of an intelligent and cognitive approach to understanding.

Firstly, it seeks to address the excessive computational burden associated with the use of modern high-fidelity simulation software in architecture, to render computational optimization more approachable. There is a clear trend in modern design practice to employ accurate simulation-based performance assessment tools from the very early stages of design. The use of such tools provides a valuable advantage to the decision-maker, in endowing objective awareness regarding the performance of a design solution. On the other hand, such tools are associated with a heavy computational burden, which may limit their application to the conceptual design stage. There exist methods to alleviate the computational burden through the use of computational cognitive machine learning tools, also known as surrogate modeling. However, training of surrogate models can be time-consuming itself, thus limiting the application. This thesis proposes a surrogate model that is modular in that it considers each space of the building in question as a separate entity, encoded through generic variables, and as such promotes model reuse in different design cases.

Secondly, it seeks to advance the state of the art on post-Pareto decision support by proposing a cognitive machine-learning based approach that enables the decision-maker to combine near-optimality with preferences regarding concrete features of the design solution. Post-Pareto decision making is an important step of the decision-making process, that seeks to identify a best-tradeoff solution among the possible ones that best matches the decision-maker's preferences in terms of performance. Such preferences are termed second-order because they follow design objectives in terms of importance. Nonetheless, it is often in architectural design that preferences are expressed in terms of design properties and not performance. Due to the non-linearity between the objective function space and the decision variable space that dictates object properties, it is challenging to exercise decision making using second-order preferences. Here the contribution of this thesis is a machine cognitive approach that learns the underlying relationships between object properties, distinguishing those that are relevant when the object is optimal with respect to design objectives. In other words, only imposing relations that are relevant to achieve optimality, it enables the expression of preferences by the decision-maker that are minimally constrained.

The main output of this thesis is a comprehensive decision support framework; it is a framework, in the sense that it comprises a set of methods and implemented tools that seek to augment decision making in architectural design; it is termed comprehensive in that it employs computational cognition and machine learning to augment the intelligent decision support capabilities throughout the design decision support process. It is also generic and applicable as-is to a wide spectrum of architectural design problems. In the context of this thesis, validation of the proposed approach is performed mainly in case studies relevant to facade design, recognizing this design topic as a complexity-exhibiting exemplar in architectural design practice.

This dissertation reports a PhD research on mathematical-computational models, methods, and techniques for analysis, synthesis, and evaluation of spatial configurations in architecture and urban design. Spatial configuration is a technical term that refers to the particular way in which a set of spaces are connected to one another as a network. Spatial configuration affects safety, security, and efficiency of functioning of complex buildings by facilitating certain patterns of movement and/or impeding other patterns. In cities and suburban built environments, spatialconfiguration affects accessibilities and influences travel behavioural patterns, e.g. choosing walking and cycling for short trips instead of travelling by cars. As such, spatial configuration effectively influences the social, economic, and environmental functioning of cities and complex buildings, by conducting human movement patterns. In this research, graph theory is used to mathematically model spatial configurations
in order to provide intuitive ways of studying and designing spatial arrangements for architects and urban designers. The methods and tools presented in this dissertation are applicable in:
–– arranging spatial layouts based on configuration graphs, e.g. by using bubble diagrams to ensure certain spatial requirements and qualities in complex buildings; and
–– analysing the potential effects of decisions on the likely spatial performance of buildings and on mobility patterns in built environments for systematic comparison of designs or plans, e.g. as to their aptitude for pedestrians and cyclists. The dissertation reports two parallel tracks of work on architectural and urban configurations. The core concept of the architectural configuration track is the ‘bubble diagram’ and the core concept of the urban configuration track is the ‘easiest paths’ for walking and cycling. Walking and cycling have been chosen as the foci of this theme as they involve active physical, cognitive, and social encounter of people with built environments, all of which are influenced by spatial configuration. The methodologies presented in this dissertation have been implemented in design toolkits and made publicly available as freeware applications.

Enhancing our knowledge of the complexities of cities in order to empower ourselves to make more informed decisions has always been a challenge for urban research. Recent developments in large-scale computing, together with the new techniques and automated tools for data collection and analysis are opening up promising opportunities for addressing this problem. The main motivation that served as the driving force behind this research is how these developments may contribute to urban data analysis. On this basis, the thesis focuses on urban data analysis in order to search for findings that can enhance our knowledge of urban environments, using the generic process of knowledge discovery using data mining. A knowledge discovery process based on data mining is a fully automated or semi-automated process which involves the application of computational tools and techniques to explore the “previously unknown, and potentially useful information” (Witten & Frank, 2005) hidden in large and often complex and multi-dimensional databases. This information can be obtained in the form of correlations amongst variables, data groupings (classes and clusters) or more complex hypotheses (probabilistic rules of co-occurrence, performance vectors of prediction models etc.). This research targets researchers and practitioners working in the field of urban studies who are interested in quantitative/computational approaches to urban data analysis and specifically aims to engage the interest of architects, urban designers and planners who do not have a background in statistics or in using data mining methods in their work.