In April 2018, a graduation project in the field of architecture was concluded. The project entailed the design of a temporary community centre in the earthquake-damaged historic town centre of L’Aquila, Italy. The underlying social concept was focused on participation of the local population in the construction of the community centre. The design includes a grid-like timber shell structure, built-up out of a repetition of only two main structural and light-weight elements: the members and the joints. The load-bearing structure of this architectural design formed the basis for this graduation thesis, which is aimed at its optimisation. The optimisation is carried out focused on many objectives, e.g. its weight, its environmental impact, its structural validity. The goal of this thesis can be summarised by the following research question. Given the architectural design, how can the main load-bearing structure be optimised for seismic contexts using parametric modelling, considering the following objectives: maximal demountability and structural simplicity, minimal material use, structural weight and environmental impact? An optimisation method has been chosen combining automated and manual processes, as this gives a high sense of control to the designer. Using computational and parametric modelling techniques (Rhino, Grasshopper and Karamba), a database comprising 10 836 different structural configurations has been generated with different geometries and materials. The database contains the generated outcomes to structural and environmental analyses for each of the structural configurations. The database entries have been ranked manually based on structural, architectural and social demands, resulting in a top 28 models. Having defined a ranked set of structural configurations, manual checks and iterations can take place. The first step is checking the global and local stability using proposed joint designs as input. The next step is checking the safety of the structure during seismic events using a response spectrum analysis. As the Top-1 design passed all analyses, no iterations were deemed necessary. In conclusion, with regard to the design, further structural optimisation could take place by reconsidering the shape of the structure. However, the process of automated database generation followed by manual exclusion, ranking and iterations give a great sense of control compared to automated optimisation alternatives. This optimisation process has proven to be very effective in finding a design that balances all objectives well.

The seismic building structural type (SBST) reflects the main load-bearing structure of a building and therefore its behaviour under seismic load. For numerous areas in earthquake prone regions this information is outdated, unavailable, or simply not existent. Traditional methods to gather this information, such as building-by-building inspections, are costly and highly time-consuming, making them unfeasible for assessing large building inventory. For this reason, the use of remote sensing data has been proposed to allow a fast acquisition of relevant building information on urban and regional scale. Subsequently, machine learning algorithms may be used to analyse the gathered data, e.g. to classify a building stock into groups with similar seismic behaviour. This thesis investigates into such an approach for the building stock of Groningen in the Netherlands. Our focus lies on the extraction of detailed geometric information from a point cloud gained by aerial laser scanning. We thereby follow the assumption that similar structural systems and materials can be inferred from geometric similarities. To describe the geometric shape of a building we apply Shape DNA, a spectral shape descriptor based on the eigenvalues of the Laplace-Beltrami operator. In a first experiment on a synthetically generated building stock we succeed in predicting the roof type of different buildings with accuracies above 80%, only relying on the Shape DNA. The experiment shows promising results, however, further research is necessary in order to explore the usability on real building data. In a second experiment we use a sample of buildings from the Groningen building stock. Here we can automatically predict detailed SBST information, such as the type of lateral load resisting system, with accuracies above 80% only by taking a buildings footprint area and year of construction into account. (see thesis document for extended abstract)