Design for a co-housing development near Lelylaan station in Amsterdam, based on the concept of the Homo Ludens. The design is supported by thematic research into the use of a design game for the co-creation of co-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?

The standard way of building consists on average of 7000 parts and processes which in combination with the decrease in productivity within the construction sector limits the number of actors that can be involved. However, this leads to a housing shortage in many countries. The production of homes should be accessible, affordable and should offer a variety of housing solutions. In order to reach this the production chain of architecture must be rethought by considering design and production throughout the whole process. Modularity of discrete building blocks can be a way to reach efficient production processes as well as diverse housing solutions by means of combinatorial variations. This method has been used in pavilion and furniture designs, but only briefly for housing solutions, mostly the main references can be found in game design – such as Lego. The goal of this research is to devise a methodology to design a small set of stackable timber elements to form both a structure and the inner shells of a building that can be configured into versatile and affordable housing units using combinatorial design and compression-only structures (which is designed based on equilibrium). This leads to an accessible and open-ended building system that strives to be as simple as laying out a Lego structure.

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.

The merging of multiple partial maps of indoor environments created
by teams of human or robot agents into a single global map is a key problem
that, when solved, can improve mapping speed and quality. Existing map
merging approaches generally depend on external signals which are not available indoors or only use the geometric properties of an environment. Inspired by the human understanding of environments in relationship to their context we propose a map merging system that extracts and uses topometric maps, a map representation containing both the geometric and topological characteristics of an environments, to solve the map merging problem in indoor spaces. In this research we demonstrate an intuitive approach to extracting topometric maps of 3D, multi-floor, indoor environments and use both the topological and geometric characteristics contained in the topometric maps to perform context-aware map matching and fusion.

This thesis introduces an adaptive framework for mass-customization of design for residential infill solutions. The proposed method focuses on a tough challenge of delivering minimal compact infill configurations for the urban context, where the square meters are expensive, and where the height is cheap. Industrialization of housing on the one hand and participation in design on the other hand are the key focus points of this thesis. The ‘open building’ approach is chosen to separate the building into two parts: the support and the infill parts. The support is considered as a collective flexible part of the 3D infrastructure of a city and the infill is considered to be a individual customizable residential unit of a building. The main question is focused on designing a computational method for ensuring mass-customization of housing infills with participatory mechanisms.

Within the coming years it is expected that PV installations could be established on every possible surface and terrain, within the urban environment. Due to the complex morphology of buildings in urban environments,systems will likely be more susceptible to partial shading compared to other conventional PV systems. In particular bifacial PV modules are highly susceptible to shading since they rely on irradiance on both the front and the back surface. To better estimate the irradiance received by such systems, particularly in urban environments, much research is conducted to develop fast and accurate simulation tools. The research described in this report investigated the different simulation frameworks that have been developed for modeling bi-facial PV systems, and develop a new simulation framework capable of simulating irradiance within urban environments. Available models capable of simulating rear irradiance differ in input variables considered,simulation time required and accuracy of predictions. Empirical models, for example, can result in inaccurate predictions since not all variables affecting rear irradiance are considered. Only the 3D view factor, back-ward and forward ray tracing methods fulfill the requirements to simulate the backside irradiance/irradiation when the aim is to perform simulations in more complex urban environments. Out of the existing simulation models tested, the backward ray tracing model performed within the Radiance software proved to be the fastest simulation tool for modeling yearly irradiation or a single irradiance measurement incident under free horizon conditions. Time of use (TOU) simulations, however, results in longer simulation time for backward ray tracing performed in Radiance, since a ray tracing simulation at each time instance is required. To calculate the irradiance a receiving surface receives in an urban environment a method is worked out based on view factors and ray casting. Within the software Rhinoceros a CAD design of the surrounding environment is created, while the plug-in Grasshopper is used for the ray casting and mathematical calculations. Through a series of sanity checks, it was determined that the developed methods are reliable for calculating irradiance/irradiation when the aim is to perform simulations in more complex urban environments. It was also identified how different sky models can result in large differences in irradiance simulated. Sky models such as Isotropic, Hay and Davis or simplified Perez underestimates the irradiance, when receiving surfaces are tilted in comparison with the Perez luminance distributed sky model. The model was validated using monitoring station measurements and compared with simulations performed with other ray tracing models. DHI, DNI measurements obtained from the Solys 2 are used for replicating the irradiance measured at the dual-axis and single-axis tracker. Since their orientation was fixed throughout the measurement period they are referred to as POA 1 and POA 2 respectively. With POA 1 and 2 having a 90 and 30 degree tilt respectfully and an azimuth of 67 and 180 degree respectfully. Making POA 1 the front/back side of a typical (bifacial) east-west configuration and POA 2 the front of a tilted (bifacial) module. Two measurement days are considered, a fully overcast day and a clear sunny day. For the overcast day, an relative RMSE value of 23.09% and 11.7% are achieved for POA 1 and 2. For both cases the irradiation was mostly underestimated, considering the negative MBE and positive MAE. On the clear sunny day, relative RMSE values of 20.35% and 5.26% are achieved for POA 1 and 2. The model again mostly underestimates on the sunny day. A simple DHI correction with the factor 1 divided over the SVF at the Solys 2 is performed in order to investigate the impact of potentially corrupted DHI measurements on the irradiance simulations. With relative RMSE value of 19.96% and 5.2% recorded for POA 1 and 2 on the overcast day. While on the sunny day relative RMSE values of 19.38% and 3.77% are achieved for POA 1 and 2 respectively. Only slightly changing the model predictions. When compared to ray tracing models the model performs slightly better than the forward ray tracing model. A possible reason why the forward ray tracing was underestimated was proposed to be due too the small aim area used. However increasing the aim area would require a larger number of rays to maintain the same accuracy which results in longer simulations.Possible improvements to the model could be, adding reflected irradiance term on reflecting surfaces and the Perez luminance distribution sky model.

Architecture within the last twenty years has developed in many ways. One such direction was reintroducing the physical world and physical phenomenon back to architectural language and design space. This is especially true as buildings have had to become more and more environmentally sustainable and developable with programs such as LEED, BREEAM, and Zero House popping up. While less pronounced, the same is true for other aspects of architecture too; such as pedestrian flow, constructability, and structural design. Each design in hi-tech architecture has become a compromise of these systems. While significant funding has gone into understand how to design for energy efficient buildings, architects typically shy away from structural systems leaving these problems for structural engineers to solve. However, great design comes to fruition when the two systems work hand in hand. This method of design, integrated design, is starting to appear more and more in the design space with gridshell structures becoming classic icons of the beauty of this marriage. Gridshell structures are becoming more widely known as architects such as Foster + Partners, Asymptote, and John McAslan & Partners have developed work that has become widely lauded and incredibly awe inspiring. These slender shapes still allow for large amounts of natural light as well as enable architects and designers to cover vast areas without the need of massive beams and a large amount of columns, instead finding rigidity in its own form. This research project, attempts to create a system for architects and designers to play with in order to develop an efficient and useful gridshell concept structure and does this by approaching the perspective from two points. On one side a computational system is built based on Finite Element Analysis (FEA) data and signal processing while on the other, structural concepts are explored in order to determine a geometric relationship between load, form, and principal stresses.

3D reconstruction of the built environment is a widely studied Geomatics topic. Resulting city models are used for a great variety of purposes. The reconstruction of roofs remains challenging. These roofs are not only building blocks for the city models but are of direct interest as well. The company READAAR uses roof segments for the detection of asbestos and PV potential analysis. Typical clients for such analysis are muncipalities and provinces. Currently, READAAR extracts roof segments from the gridded LiDAR dataset: algemeen hoogtebestand nederland 2 (AHN2). The use of LiDAR data however comes with some limitations. Most importantly, outside the Netherlands, LiDAR data is not always available as it is relatively expensive to gather. Furthermore, the point density of the AHN is 6-10 points/m^2, which limits the amount of detail that can be extracted. In the Netherlands countrywide aerial stereo imagery is available at a resolution of 10cm, this potentially gives a point density of 100 points/m^2 after image matching. This research explores the possibilities of using aerial stereo imagery instead of LiDAR data for the efficient large-scale extraction of 3D roof segments. A workflow is designed in which stereo matching and extraction of segments are integrated. This makes the workflow both efficient and easily scalable. Roof segments are extracted in two steps. First, a watershed segmentation is applied to retrieved color segments. Second, these color segments are clustered based on their orientation. The resulting roof segments generally have a higher quality than the segments retrieved with READAARs LiDAR-based approach. However, problems do occur, especially in shaded areas. This could possibly be solved by integrating LiDAR data when available. Another recommendation for future research is improving the matching by using multi-view matching or possibly neural networks. Furthermore, the segmentation could potentially be improved by using multiple images from different years and processing building blocks.