The assessment of the risk maps for the seismic vulnerability at large scale is based on the vulnerability of each building. In order to determine these vulnerabilities, it is first required to assign to each building its construction class. The construction class is needed to define the seismic behavior of the building. Since the structures have been built in different times, this construction classes are different between them, and their vulnerability values have been pre-defined in order to simplify the analysis. If, for a small city the task of assign to each building its construction class can be done by hand, in a big city, with thousands of buildings, this process can result long and hard. In this Master project the possibility of assigning the construction classes by using a machine learning method will be studied. This method use as a data set of the city of Lausanne from previous works which contains around 1000 buildings analyzed by hand with their construction classes. The goal of this method is to find the relationships between the attributes of the buildings to their construction classes. Thanks to the statistical offices, it is possible to obtain the attributes in an automatic way for every building in a city. Based on the relations that the machine learning method finds (and using the statistical attributes) the construction class for every building can finally be determined, in a quick and efficient manner. Once obtained the vulnerability’s values for each building, the risk maps can be drawn. In this project there will be studied three maps, two with the Europeans’ typologies (LM1, LM2), and one with the Swiss’ typologies (UniGE). Some comparisons will be made between the maps, in order to highlight the differences. Some other comparisons will be made to show the impact of the new micro zonation of Lausanne and of the new optimized method for the determination of the seismic displacement demand (N2 optimized method) on the maps. This is done for the mechanical methods (LM2 and UniGE) only. Finally, there will be shown the relationships between the soil’s characteristics (micro zonation), the building’s attributes and the city’s historical evolution with the risk maps.