The Constructing industry around the world is growing exponentially. In the Netherlands itself there is a need of constructing 1 million affordable new homes by 2030. Such a massive demand for new buildings has given rise to a need for a solution for mass customization of architectural configurations. Simple repetition or a badly configured building is not ideal and can lead to a lot of social and environmental problems. Generative Design can offer a digital solution for this mass customization problem. It can utilize the power of Artificial intelligence in design to generate a system where users can create custom solutions rapidly. The second part of the customization problem is the user participation in determining the customization goals. More often than not the end users of the built space do not get an opinion in the configuration process and this leads to unsatisfactory and undemocratic design solutions producing urban inequality. In this thesis a comprehensive digital solution by means of a digital platform is provided in an attempt to solve the 3D layout problem in a participatory manner. The platform built in the thesis is intended for designing Open buildings at a scale of support structures. The concept of Open buildings calls for separation of the structure and the infill layer making the buildings open for modification and customization. The 3D-Layout configuration problem in its full sophistication is a classical example of a wicked problem. So, in this thesis the problem is methodically broken down into a group of nested smaller problems with local goals to achieve the global spatial planning goals and various computational methods are tested for solving the problems. The nature of the smaller problems is often iterative hence a robust decision support system is also designed for stakeholder participation and control over the problem-solving method. The computational methods used in the thesis are inspired from various other disciplines like Computer science, Industrial engineering, Operations research, etc. where they are used to solve similar problems in their respective domains. Techniques like Multi-agent systems, Multi-criteria decision analysis, Techniques for solving the clustering and packing problems in operations research are explored to solve the various nested smaller problems in the 3D-Layout problem. Finally, all the methods developed in the project are used to solve a test case for the design of a mixed used Open building consisting of Housing and Commercial spaces in the Buiksloterham region which is one of the first circular neighbourhoods in Amsterdam to showcase the potential of the platform and the feed forward design process developed in the thesis.

The current Dutch standard for daylight in buildings does not assure good daylight quality. Designers therefore often do not know when a building has good daylight quality and how they can design a visually comfortable and healthy building. The new European standard for daylight in buildings is more elaborative and recommends a higher daylight quality. When this standard is introduced, the assessment method must be adopted in the Dutch building regulations. The assessment method however differs a lot from the current Dutch assessment method, which makes it difficult to compare the standards and determine the exact differences. To make it easier to design buildings with good daylight quality, the goal of this research was to establish a set of recommendations. For these recommendations, it was necessary to compare the Dutch and European standards. Three aspects of the standards were considered in this research: the requirements, the assessment methods and the effects on daylight quality. Literature review on the standards gave insight in the requirements and assessment methods. The most obvious difference was that the European standard is more elaborative and assess, besides daylight, also sunlight, glare and view. Another big difference is that the Dutch standard is normative and the European standard descriptive. Both standards do not consider the orientation of a building and opposite obstructions. After a literature study, two case studies were performed. The assessment of daylight in a basement showed that a space that meets the Dutch standard, can have a really bad daylight quality. It also led to the orientation factor which can be used to convert simulated daylight factors. This can be useful to gain insight in the amount of daylight in a space in common situations when the sky is not completely clouded and sunlight influences the daylight quality a lot. The second case had quite good daylight quality, but still did not meet the European standard. There is not enough daylight, at the south east side there is too much glare, and at the north west side there is not enough exposure to sunlight. The last part of the research was a systematic study. Variants were simulated according to the Dutch and European standard. Variants with a minimum daylight area from the Dutch standard had a bad daylight quality and showed that the window shape influences the access of daylight. In almost all other variants with bigger windows, higher reflection factors, or less obstructions, it was not possible to meet the European standard on both side of the building. This means that in a dense area the European standard is almost unachievable. Because of the many influencing factors, there is no clear relation between the equivalent daylight area and the daylight factor. For designers it is recommended that they consider the orientation, surroundings and reflection factors, even though it is not required according to the standards. This gives a more realistic insight in the daylight quality. Rooms should comply with a minimum daylight factor of 0.8% in 50% of the area and an average daylight factor of 1.5% also in 50% of the area. Besides the amount of daylight, sunlight, glare and view also influence the daylight quality and should be taken into account during the design process. Of course, there might be more factors that should be considered by designers. Those are not mentioned in this research, but can be investigated in future research.

Due to technological innovations free-form shapes in architecture can be seen more often nowadays. For instance in façades, roofs and pavilions. In between this design shape and actual producible shape lays a complex process that has to do with approximating and subdividing this shape in economically producible panels. Restrictions for production depend on the used materials, production techniques and the method of assembly. The aim of this research is to develop a method for manufacturing free-form steel structures – with a guarantee for high accuracy – relying only on a CNC laser cutter. This methodology consist out of three parts. The first part focusses on generating a quadrangular subdivision based on the topology of the shape. The second part explains a global approach that increases the developability in the panels, while maintaining a certain accuracy to the original shape. Lastly the modified geometry is transformed to structural elements on which a preliminary structural analysis is executed to provide an estimation of the required dimensions of the elements.

The demand for lower construction costs comes at the price of duplicating architectural designs. As a result, the misperception that modular architecture is repetitive prevails. However, the developments in computational design and MMC (modern methods of construction) can suggest a standardized manufacturing approach to provide mass customization. The research explores the computation of Modular building envelope systems using the Boolean marching cube algorithm under the premise of a ‘participatory generative design framework’ of the 'GO-Design' developed by Pirouz Nourian and Shervin Azadi [1]. In this project, (1) a set of architectural tilesets is developed that incorporates mass-customization by providing the potential for generating various configurations, and (2) a prototype of an interactive digital tool is established to enable future inhabitants to customize the configuration of their modular houses based on the predefined tileset. The algorithm is developed and applied in the Rhino & Grasshopper environment. The BMC algorithm reads the voxels' labels pertaining to each cube's vertices (a collection of 8 neighboring voxels) to load and choose the corresponding tile from a predefined tileset. The project's focus is the design of such a tileset, its engineering consistency, and architectural coherence. As an input, a voxelated massing will be the input, possibly later accompanied by a colored zoning scheme. and the output will be a modular architectural assembly. This workflow is envisaged to become a procedural component of a Configure, Price, & Quote (CPQ) system for the industrialization of mass customizable housing.

The goal of my thesis work was to experiment with potential approaches to addressing issues haunting housing across developed world countries. Namely, housing being: dehumanizing, unaffordable, wasteful in production, and under-performing. The main objective was to define the principles that would help creating the process which would enable configurative freedom for end users in terms of flat positioning, spatial layout, façade features and internal finish selection, whilst reducing cost and strain on environment. The redefinition of housing from machine for living to the ecosystem for living. In a hunt for this redefinition I tried to answer following questions: How can housing schemes become more attuned to the needs of the future occupants? Is there a way for designing with kit-of-parts systems to be combined with generative tools to unlock the hidden potential? How can we limit waste and performance issues in the built industry?

One million homes will be needed in the Netherlands by 2030. Prefabrication in the construction industry can contribute to construct affordable houses, but that results often in standardized and not customized designs. These repetitive designs can be overcome through the introduction of mass-customization in architecture. Computational Architectural Design offers the possibility to tackle mass-customization through developing tools to let the user easily customize a design while providing guidelines and feedback towards a successful building design. This can be done by discretising a building into building blocks that can be controlled through a computational workflow. This thesis elaborates on the potential of developing a design tool that allows the customization of houses through discrete building information modelling. Since the housing design process is a complex and multi-layered problem, the process is broken down into the sub-problems of topological design, building product development, configuration, and data export. For these steps, algorithms from the gaming industry are tested to improve the participation of the end user trough a simplification of the design process. Through design grammars, a relational data structure is created that is compatible to the BIM environment of the industry. The evolved methods are applied to the test case of a rowhouse design in Delft to predict the possible impact of the design tool. The results of this are set in the context of the AEC industry.

July of 2021 marked the beginning of a massive internal conflict in Afghanistan, resulting with the reinstated power of Taliban across the country. The radical change in the regime and the vast destruction of people’s homes and cities led to a steep increase in internal displacement and the total number of Afghan refugees inside and outside of the country. This project shows a new approach to shelter design by making use of local opportunities and implementing traditional vernacular elements in the design. ShellTerra is a shelter design that proposes a solution for internally displaced people in Afghanistan on several levels: from modularity and gamification of the settlement planning on the urban scale, to creating a set of blocks and molds that fit into the compression machine for easier construction and better customization on the block scale. The design uses locally available earthy materials and is fully constructed out of CEBs, which makes the elements reusable, able to being safely demolished and zero-waste. The project proposes low-tech construction methods and the possibility of shelter modules to grow into a settlement, while taking sustainable and people flows into consideration. It takes into account the cultural architectural, inner climate and comfort requirements in the attempt to create a home for the refugee communities, rather than a temporary solution.

Building with earthy materials requires new methods to generate architectural geometry and possibly buildings. Topology optimization is a process often used in mechanical engineering and minimizes the volume while still keeping the maximum stiffness. This research implements this process into architectural models, in order to find geometry based on supports, forces and voids. This implementation can only be made by density dependent forces, which are important in architectural models. Self weight, snow loads and roofing constraints are added in order to make more relieable calculations. To generate architectural geometry the methodology is translated to 3D geometry and several design problems are tested. These problems show generated domes, arches and the proper transferring of forces to foundations. Insights from these design problems show that topology optimization can be used to generate geometries for masonry buildings.

Residents typically have a negligible impact on their living units spatial layout in mass housing projects. Building industry lacks tools for users to create space tailored to their needs. The purpose of this work is facilitate co-creation by providing a pre-configured and engineered set of spatial units corresponding to human activities, in order to lower the technical threshold for participation in the design process. This set provides an accessible search space for modular solutions allowing for free aggregation of space by people without an architectural background. The methodological approach used in this work was Design Science Research.

Transport decision-making determines people’s level of accessibility and deeply influences an individual’s access to social and economic opportunities and the quality of life. Socially vulnerable populations are highly dependent on yet often more likely to have less access to transport services and experience lower accessibility. This creates and reinforces social and spatial inequalities by trapping people in disconnected neighborhoods and segregated areas that continue to be deprived of access to opportunities. This research aims to develop a Spatial Decision Support System (SDSS) to explore how re-purposing existing streets for walking and biking could influence the accessibility of vulnerable neighborhoods to support decision-making in enhancing equity of accessibility. In the SDSS, equity of accessibility is formulated as a generative design (GD) problem, named the Street Allocation Decision Problem (SADP), a single-objective optimization problem that searches for generated designs with the maximum weighted improvement in accessibility per unit of its cost. A GD model is built to solve SADP. Lastly, an operational framework is developed to guide prospective users in tuning and operating the SDSS for their specific context, problem, and objective. The SDSS is tested on a toy problem, a 0.09km2 area in The Hague, The Netherlands. The toy problem is small in scale, easy and fast to implement and useful for initial testing of the model. Preliminary results have demonstrated the feasibility of the model. This is the first and humble attempt at developing an SDSS for enhancing equity of accessibility with a GD model. However, there are shortcomings in methodology and result quality, which compromise the practicality of the model and the interpretability of results. Although, only a proof of concept at the moment, the SDSS is a valuable starting point due to its advantages, such as transparency, modularity, humane-ness, and flexibility. This SDSS is built to involve decision-makers in the design process, which could serve as a useful learning experience for questioning and understanding what is the problem, what is considered equitable, and what are possible solutions. The SDSS has the potential to facilitate learning during transport decision-making in experimental settings or as explorative aids in early design stages.

Throughout the long history of architecture, the effect of gravity is always present in any structures. Surprisingly in tensegrity composition, this primary law of nature seems to be absent. This is because the discontinuous set of struts in the continuous network of proportionally thin cables makes the structures look like floating in the air. However, there has not been much application of tensegrity principle in the construction field due to the lack of design methods and its complexity. By developing a large-span structure using tensegrity systems, a design method and an analysis technique are introduced along to define double-surface tensegrity systems. The design approach explores an innovative way to determine the structural topology and geometry of such the systems. The form-finding process and structural analysis are conducted to discover an appropriate way of analyzing tensegrity and ensuring that the systems are stable.

Indoor localization provides for a much researched subject, as the complexity and size of many public buildings require extensive and properly designed methods to facilitate location specific processes. Indoor localization entails finding a qualitative description of the occupied area, that is human interpretable, rather than a quantitative position in Euclidean space. In other words, the context of an indoor environment has to be understood, such that a position can be transcended to a meaningful location. A space is defined as a mathematical structure with relational properties, to which all its members adhere. As a subset of space, topological space describes the relationships between (parts of) objects that do not change under continuous transformation. It further defines metric space, as a set where distances and angles between all of its elements are defined. Indoor space is then interpreted as a structure bounded by physical or functional elements, enabling human activities. It should entail the geometric place an actor is in, the topological structure that place is a part of, and the semantics giving the place meaning. Many indoor positioning methods have been developed, which can provide an actor with a relative geometric place. Most preferred are positioning systems not relying on a contingent system, which can be performed using a hybrid fusion of sensors embedded into a mobile device. Such a system found to perform sufficiently is VI-SLAM, simultaneously building a geometric place and tracking each pose and heading relatively. Its output is a mesh model, in which a viewshed of the indoor environment is built. From a mesh model, a topological structure can be derived in the form of its dual graph. Now to finalize this representation of indoor space which can be captured using a mobile device, it has to be enriched with a meaningful context. These semantics are generally stored in BIM models. Thus to transcend the position retrieved using SLAM to a location, is has to be matched with a BIM model, so that the appertaining semantics can be connected. The method proposed provides for a possibility to perform graph-based indoor localization, by extracting a graph from both input sources and comparing them, in order to find a match. However different in nature and structure, both input sources can be converted to a graph of similar calibre, such that they can be tested for a match. All operations performed on the graphs are derived from spectral graph theory. The graph simplification and analysis is performed using the eigen spectrum of the graph Laplacian, and the match is performed by remapping the spectral graphs into a vector sub space using the eigen spectrum of the data covariance matrix. After a match between both graphs is found, the current position of the actor within the mesh model can be translated to the room found in the graph. This room is now connected to a room within the reference graph, for which the semantics are stored in the BIM. Returning these to the actor, a location description is formed.

In 2018, a new standard was released for daylight in buildings: EN 17037. This is the first standard for daylight in buildings for Europe. In the Netherlands it will replace the NEN 2057. The determination method for daylight provision for EN 17037 is based on internal illuminance. Additional ambitions for the design quality factors: view, direct sunlight and the prevention of glare have been added to the standard. A sustainable design is all about balancing daylight performance and energy consumption. Large windows will cause the building to overheat and use too much cooling energy. Too small windows will result in low levels of daylight availability and visual comfort. For this reason, it was still unclear whether it is possible to achieve these new recommendations for daylight and at the same time meet the energy requirements for Green building certificates, such as BREEAM and LEED. The main aim of this research was therefore to investigate how much influence the European standard has on the energy consumption of a typical office building and whether the requirements for BREEAM and LEED can still be met. The analysis shows that the European standard has an influence on the daylight and energy credits for BREEAM and LEED. For the daylight credits this standard has a positive influence. For the minimum performance level for daylight provision for the European standard this is not yet enough to meet the requirements for daylight provision for BREEAM and LEED. For BREEAM and LEED, the variants only meet the requirements if the medium and high recommendation levels for the European standard are met. However, the ASE is often too high for a design that meets the high performance level for EN 17037. It should also be taken into account that when a certain performance level for daylight provision has been achieved for the European standard using method 2 (based on internal illuminance per hour for a typical year) this same variant will usually score lower with method 1 (based on daylight factors). The energy consumption goes up when the recommendation level for the European standard is higher. When comparing the average energy consumption of variants with the minimum performance level for the European norm with the high performance level, the total energy consumption goes up 8.33 kWh/m2 . These values can be higher or lower with different orientations. To reduce the negative impact of the European standard on BREEAM and LEED, certain parameters for the design can be chosen differently. The most important parameters for meeting the high-performance level and minimizing primary fossil energy consumption is the window-to-wall ratio and width/depth ratio. To find a balance between daylight and energy consumption, the optimal width/depth ratio is between 1.33 and 0.75. The optimum window-to-wall ratio is between 40% and 60%. To reduce the cooling consumption and ASE even more, designers can consider the introduction of overhangs in the façade, or other interventions that reduce the window SHGC.

In this thesis a two-stage model is proposed, combining the idea of a two-stage model from Anjos and Vieira [1] with a gradient descent approach, much like proposed in Sikaroudi and Shahanaghi [42], in order to solve the facility layout problem for problems consisting of 8 and 12 departments . This method was chosen after analyzing the criteria for a vegetable processing factory and combining this with a literature research to previous methods applied to solve these "facility layout problems". The gradient descent approach uses the partial derivatives of a multi-variable objective, the flow-cost, in order to get a vector which is the direction of greatest descent. Computing this vector for all departments and then moving them creates an iterative improving loop. In addition to the gradient descent approach a swapping procedure and a shooting procedure is introduced in order to reduce the effect of random starting position. From the result of the first-stage model, relative location constraints are extracted to be used in the second-stage model, a linear constraint programming solver capable of running optimizations by google or-tools [36], to greatly reduce the solution space. The proposed method was tested on two toy-problems from Tam and Li [44] and compared to results existing in literature. Additionally, the effect of including a first-stage model was tested as well. When looking directly at the results, the proposed method is consistent and shows good results. Especially the inclusion of the first-stage model helps finding better solutions. Compared to other results found in the literature, the method shows slightly less good results based purely on the objective value. However, the final layouts found in this thesis are more compact.

In the transportation planning process, the Four Step Model (FSM) is used to define the needs and requirements of the transportation system within a city or a region. Despite its wide use, the model is focused on vehicular trips and fails to represent the demand of walking activity. The limited work that has been performed to address the misrepresentation of walking in the context of the FSM, still does not address the last step of the four step model, the assignment of the walking trips to the network. Stemming from this literature gap, and the need to enhance the role of walking activity within the transportation modeling, the main question of this thesis is to develop a method for the assignment of the walking trips to the street network. This research suggest a methodology to model large continuous space and walking trips on it. Thus, the focus is on modeling walking space taking into account the pedestrian scale for the model. To implement that, the size of the spatial analysis zones of the FSM is adjusted to walking scale. Next, continuous walking space is defined by the BGT spatial dataset that offers information for the land covers. In order to model continuous walking space two space discretization techniques are compared; first, Constrained Delaunay Triangulation (CDT) is applied on the walking area polygon, second, a regular quadrilateral grid is overlaid with the walking area. Walking activity is modeled on the network that is formed with the dual graph of each discretization method. In a subsequent step, the trip injection is performed with zone centroids and connectors which were created with the point clustering algorithm DBSCAN having as input points the graph nodes. Finally, A* algorithm is used for deterministic an all-or-nothing network assignment. The results of the applied methodology show that addressing walking within the FSM requires a finer grained approach due to the more localized scale of walking activity. Walking space needs to be defined as a continuous surface. In order to model such surface at the scale of the FSM, a discretization method is required to handle continuous space at large scale. Comparing the result of the two discretization techniques, polygon triangulation and regular grid, the triangulation dual graph results to the ’skeleton’ of the polygon while the regular grid dual graph offers a more dense and homogeneous polygon representation. Additionally, in the trip injection process, the nodes of the regular grid perform better than the triangulation graph nodes. Overall, throughout the whole implementation, the performance of using a regular grid, outweighs the polygon triangulation as discretization method.

In this thesis we present a computational framework, which allows simulating residential location choice. The specific application of the model focuses on representing populations under disaster risk. The aim of the tool is to enable public planning agencies to explore the synthesized households’ location choice under risk and different spatial, financial policy scenarios. The simulated urban system is represented as an agent-based model, where the households are the agents choosing between discrete options to relocate from one house to another. Their choice behavior is built upon a notion, that people make decisions based on regret. This is done with the help of Random Regret Minimization (RRM) model, allowing to capture varying levels of regret (profundity) and enabling incorporating multiple attributes of different dimensionality. Given the agent heterogeneity and the changing availability of the building stock, the choice sets are dynamic. Therefore, the traditional RRM approach would not return stable results: with every new option housing available it would have to be re-calibrated. As a solution, we propose re-interpreting the classical c{RRM} model by scaling the beta values by the choice set attribute variance. This allows to represent $eta$ as a unit-less preference weight, associated with a homogeneous population group. We apply the framework to two different scale case-studies in an earthquake-prone area in Groningen province, The Netherlands. The data used for simulation includes several public and private spatial datasets, as well as aggregate level statistical data to synthesize the properties of the households. As a showcase, we applied the simulation assuming homogeneous and equal preference weights for 7 optimization criteria. These criteria relate to static properties of the building stock and household-related dependencies to the network (job, school locations). To further exhibit model usability within public sector agencies, we also apply the model on several financial policy scenarios. The output of which is captured on both aggregate and semi-disaggregate levels, allowing for interactive exploration of the effects of the proposed scenarios. The model outcomes correspond to the expectations set prior to simulation. It showcases convergence, anticipated optimization behavior and spatial patterns, corresponding to the building stock properties of the region.

The typical solar energy potential simulation workflows used in the AEC industry require an abundance of information regarding the detailed geometry and materialization of the design. These requisites render them incompatible with early-stage design decision-making pertaining to form-finding. By performing solar energy potential simulations at the pre-conceptual design phase, the necessary time for later-stage environmental assessments and design improvements is reduced considerably, while building designs with high performance but low environmental footprint are attained. This research proposes a computational framework to navigate voxel-based morphologies of building envelopes in a performative design space. It investigates a generative workflow through an embedded multi-criteria optimization of solar-related indicators, which are mapped in a solar energy potential field. The formulation of this field consists of an a priori assessment of the solar energy potential in every discrete volumetric unit (voxel) and a vectorized description of the interdependency of them. The astronomical size of this solution space renders the use of metaheuristic methods more appropriate. More specifically, a subtractive strategy that incorporates an MCDA approach is being applied in order to reach user-defined optimization targets. The novelty and potential of the proposed methodology lies in streamlining the early decision making process for designers and architects and expanding the morphological possibilities. Through this framework, the performative design space is effectively navigated and nearly optimal solutions are generated, to act as suggestive mechanisms for informed architectural decisions.