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.

COMPLEX is an alternative to contemporary 2.5d-housing where typologies are generic and flat. Every home in this building is unique and is aggregated and puzzled together using a created computer algorithm. A vaulted structural system creates structural freedom and helps define spaces.

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.

Social Inclusion has been growing as a goal in heritage management. Whereas the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL) called for tools of knowledge documentation, social media already functions as a platform for online communities to actively involve themselves in heritage-related discussions. Such discussions happen both in “baseline scenarios” when people calmly share their experiences about the cities they live in or travel to, and in “activated scenarios” when radical events trigger their emotions. To organize, process, and analyse the massive unstructured multi-modal (mainly images and texts) user-generated data from social media efficiently and systematically, Artificial Intelligence (AI) is shown to be indispensable. This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. AI models were applied, nurtured, and tested, helping to analyse the massive information content to derive the knowledge of cultural significance perceived by online communities. The framework was tested in case study cities including Venice, Paris, Suzhou, Amsterdam, and Rome for the baseline and/or activated scenarios. The AI-based methodological framework proposed in this thesis is shown to be able to collect information in cities and map the knowledge of the communities about cultural significance, fulfilling the expectation and requirement of HUL, useful and informative for future socially inclusive heritage management processes.

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.

With the advent of Computer-Aided Design, the design and fabrication of complex free-form shells have become easier to achieve. However, this results in extensive usage of custom-made formworks for the production of shell components and falseworks which provide support for the shell during the construction process. Therefore, a modular design method is proposed for generating form-active spatial structures out of stackable blocks of a few types, having in mind its potential applications such as housing. Instead of shells, spatial masonry structures are thus the main consideration in the design process considering building on top of a vaulted ceiling. By designing a 3D interlocking grid and introducing a four-step topological design that is coupled with structural verification processes based on finite element modelling and discrete element modelling simulations, the geometry of interlocking stackable modular blocks can be automatically generated for constructing such spatial masonry structures. The proposed method ensures that the designed vaults are modular, reconfigurable, and self-supporting during construction, thus increasing the efficiency of mass production while allowing for combinatorial mass customization in designs.

The emic structure of space heavily depends on the social, cultural, and economic context (Hillier, 1989). Yet, many social housing projects disregard this crucial aspect by standardizing the configuration of space (Sanoff, 2000). To be able to avoid the standardization of configuration and benefit from the cost-efficiency of mass production (Pine, 1993, Tseng, 2007), we propose a serious game that utilizes participatory design and gamification to facilitate co-creation and enable people to re-enforce their social and cultural preferences in the configuration of the spaces while benefiting from the economic viability of a mass-produced kit-of-parts. The proposed construction game is a mix of board game with Lego pieces. Each Lego piece have different possible dimension depending on the function which is being represented. The gameboard represents the plot on which the housing will be implemented. During the Planning phase of the game the players will use those components to generate a draft configuration that translates their preferences and requirements. Later in the Configuration stage, the game mechanisms are introduced to engage the players to communicate and discuss their ideas, make trades and commitments between them. The proposed configuration comprises fully modular pieces, so it could be easily manufactured and replicated. A test case base is conducted to further analyse on how effective the game has been in facilitating the participation in housing configuration, without the obstacle of technical knowledge of the architectural design and construction.

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.

This thesis concerns the application of different multi-objective optimization (MOO) methods and strategies for finding the optimal envelope for a given building plot and lighting performance indicators. More specifically, the PV potential and daylighting potential of the building are maximized using different optimization solvers. Auxilliary objectives are introduced to constrain the model to a certain compactness and size. The method utilises an existing data framework called TopoGenesis and solves the problem using the PyGmo library.

A ray tracing is used to find all possible collisions between the objective test
points and the building mass and environment. The problem is first presented as a standard integer programming problem, but solving this problem is not feasible if complexity needs to be kept at a reasonable level. An alternative method of continuous optimization is therefore proposed that uses (meta)heuristics to find an optimal solution for maximizing the objective functions. The occupation status of the massing is used as inputs for the decision variables.

After the application of this method on small scale toy problems, a few of the
design options are selected and evaluated by their performance indicators, as well as the measure with which the option makes sense from a more traditional design perspective. The comparison of the performance and results of both methods give insight into the recommended workflow, settings, and pitfalls for finding an optimal solution to a multi-criteria design problem with visibility objectives. From the initial results, the Non-Sorted Genetic Algorithm seems to be the best option for solving these types of problems, and the PV potential objective is validated. The daylighting potential objective performs less satisfactory and suggestions are made on alternative approaches for this metric.

The project uses a tangible and visual method for eliciting the shareholder’s spatial needs using a game-based approach. The design game resembles a board game in conjunction with Lego and functions on a more abstract level in relation to the built environment. The game is intended for use in a collaborative design that allows end users to collectively configure their space based on their spatial requirements and priorities which otherwise may not be met by standard conventional design in the top-down approach. The development of the design game abstracts the working spatial and social qualities of slum settlements within Ababa’s inner city (specifically kebele compound houses) and embeds them into a system of configurational elements and rules for configuring a low-rise courtyard-based incremental housing. This system of design flows a modular co-ordination system based upon a tartan grid. The end users use the game elements (boards, modules, activity cards & tokens) to configure their space based on the defined game rules. In the game play process, the stakeholders (players) will identify and weigh different domestic and commercial spaces, functions and sizes based upon their priority to form their configuration of hierarchic spatial framework and shared spaces. The method of developing the game can be replicated to create design games for different contexts. The game serves as a simulation of a design problem that facilitates trade-offs, design ideation, projection of stakeholder needs, preferences, and co-creation. At the basic level, the game can be used to collect information about end-user preferences regarding spatial configuration, the use of tokens creates a trade-off where players have to prioritize their immediate spatial needs. At a higher level, the game can be used in a bottom-up planning strategy for ideating new housing solutions and upgrading existing ones.

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 injection of renewable energy into the grid requires meticulous planning and execution due to the unpredictable nature of weather and in-turn the energy generated by these sources. Moreover, the inclusion of grid-independent energy generators requires accurate tools to predict energy yield in order to simplify matters such as grid-congestion management. One such tool useful to compute solar energy yield, which is under development, is the PVMD toolbox from the PhotoVoltaic Devices and Materials research group within the Delft University of Technology. This toolbox, which is completely based on MATLAB , allows users to specify parameters ranging from the micro-scale (such as the chemistry and characteristics of a single cell) to the macro-scale (such as the electrical parameters of the whole system).
However, in its present version, the toolbox can simulate the yield only for
simple systems, such as a single PV panel without any obstructions present nearby.
This makes it incompatible to solve complex problems such as models with solar panels installed in the built environment.

Therefore, the primary focus of this thesis is on the optical side of the PV simulation, by enabling the toolbox to precisely compute reflected component of irradiance and include shading effects on modules from nearby objects in the scene. This functionality will help consider the presence of features such as dormers, chimneys and highly reflective surfaces present in urban environments which contribute to solar power output. To achieve this, attention was paid towards the formats and sources of geometric data (3D objects in particular) which could be used as an input in the toolbox. Once the data format was selected, the 3D object is parsed into a MATLAB readable format and refined in order to preserve geometric information.
Afterwards, the scene was completed by enumerating the geometric data of
the solar cells and placing them beside the object or on an external surface of the object. Optical properties were then obtained in the form of spectral reflectance data from NASA’ ECOSTRESS Library and converted into the appropriate format to assign these material properties to the geometries. This Opto-geometric data is then parsed back into a file which can be read by RADIANCE, a visual rendering software capable of backward ray-tracing. This ray-tracing technique is used to obtain the irradiance available for each solar cell at a given time instant.

The developed workflow is validated by comparing it with real data stored at the PV Monitoring Station within the TU Delft Campus. For the given data-set, the mean percentage error is computed to be 1.89% , with a standard deviation of 12.49%. This proves that the proposed workflow is suitable to visualise shading performance and the effect of reflected irradiance. This model can be further improved by considering parameters such as spectrally responsive albedo and inclusion of other geometry formats. The outputs from this project can also be used as inputs to compute the spectral absorptance of different layers within a solar cell, such as tandem solar cells or develop sensitivity maps for different modules present in the scene.

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.

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.

Usually in a renovation design process one of the first main tasks of the architect is to distribute effectively the program of requirements (spaces with their corresponding areas) in the building. To do so the architect inserts in the existing floorplan the spaces in the form of circles; the so called “bubble diagram”. The purpose of this project is to propose a methodology to wave proximity relationships with illuminance requirements. The designer inserts existing geometry of the building in the software. An illuminance analysis of the room is performed so as to determine the ideal light locations for each room according to regulations. The user inserts the desired proximity and illuminance requirements of the rooms in the form of points and lines and prioritizes them. Given these inputs the goal is to place the rooms in positions that the objective function (potential energy) is minimized. The tool finds the optimal position of the rooms regarding proximity and illuminance requirements in the given boundaries without overlapping themselves by simulating it as a spring network and produces the bubble diagram. The diagram produced serves as the starting point for the designer to further develop the layout into a proper floorplan manually.

Reduction of energy consumption and to increase its generation is necessary, as the population lives in urban settlements consume three-quarters of global resources, and these numbers are continually growing. Building-integrated photovoltaic panels (BIPV) which would assist for the resolution of the problem can be applied by replacing the façade cladding with BIPV panels whenever possible. The optimum orientation of PV panels for the Netherlands is south with an angle of 37°, which maximises total electricity production. While the process is simple for new buildings and systems installed in areas with no orientation restrictions or horizon obstructions, the scenario becomes more challenging in urban settlements. As the premises cannot be reoriented in an urban context, solutions may be finding the best places to install BIPV panels on the façade and tilting them. This process can be deployed simultaneously with the building refurbishment that is needed to reach the current envelope insulation standards. Tilting can increase the energy yield, but this would increase the production costs and thus, initial investment costs. The balance between energy yield and added production costs can be found by locating the right panel in the right place on a limited budget. In this study, an early-stage computational design method to optimally allocate and reorient BIPV façade modules to reach a cost-effective and applicable solution is presented. The method was tested in a case setting of a concrete façade retrofit.

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.

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.

Design of elderly care buildings like nursing homes usually looks for balances between the livability for residents and the circulation efficiency for staffs and workers. Losing the balance between these two has the potential of either dissatisfying residents, uncomfortable living experiences, or causing low efficiency in operations, which might lead to extra working loads, longer service hours and higher service costs. This thesis first proposes an interactive computational methodology to embed daylight hour potential analysis and pathfinding of circulations into the early stage of the architectural design workflow, then the methodology is implemented to a test design case and the result is evaluated and compared to a manually designed result.