Capacity curves obtained from nonlinear static analyses are widely used to perform seismic assessments of structures as an alternative to dynamic analysis. This paper presents a novel ‘en masse’ method to assess the seismic vulnerability of urban areas swiftly and with the accuracy of mechanical methods. At the core of this methodology is the calculation of the capacity curves of low-rise reinforced concrete buildings using neural networks, where no modeling of the building is required. The curves are predicted with minimal error, needing only basic geometric and material parameters of the structures to be specified. As a first implementation, a typology of prismatic buildings is defined and a training set of more than 7000 structures generated. The capacity curves are calculated through push-over analysis using SAP2000. The results feature the prediction of 100-point curves in a single run of the network while maintaining a very low mean absolute error. This paper proposes a method that improves current seismic assessment tools by providing a fast and accurate calculation of the vulnerability of large sets of buildings in urban environments.

This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

A project named PERSISTAH is being developed to study the seismic vulnerability of primary schools in Huelva (Spain) and the Algarve (Portugal). This area has a moderate seismicity but this is affected by a nearby area where earthquakes of large magnitude (Mw≥6) and longreturn periods happen. The seismic vulnerability of URM (UnReinforced Masonry) buildings has been observed and analysed in the last decades. The seismic retrofitting of these buildings is required in order to improve their seismic behaviour. Many retrofitting techniques have been developed for that purpose, most of them very complicated and expensive. Therefore, these are not appropriate to retrofit a large number of buildings. This is especially relevant in areas of moderate seismicity where the cost-efficiency ratio must be carefully considered. The aim of this paper has been to develop a simple, effective and affordable technique to retrofit these buildings. These buildings are characterised by numerous openings which causes a great weakness in the URM walls. Then, a technique that consists in installing a steel encirclement or a grille in the openings of the walls has been proposed. This is a specific retrofitting technique for URM walls since this technique substantially improves the seismic capacity of these structures. To test the technique a case study is proposed. The building under study is a primary school located in Huelva and built in 1961. Results have shown that the capacity of the building is notably increased. Also, the performance point and the damage level of the structure are decreased.

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An approach to represent and analyze socioeconomic contexts as well as to reason with them, in order to extract useful conclusions about the social perception emerging from citizens' beliefs and feelings, is introduced. We concentrate here in the formal aspects of the solution, completing this way our work [4].