Detecting social patterns within 20th century documentary photos: a deep learning based approach

The job of a historian is to understand what happened in the past, resorting in many cases to written documents as a firsthand source of information. Text, however, does not amount to the only source of knowledge. Pictorial representations, in fact, have also accompanied the main events of the historical timeline. In particular, the opportunity of visually representing circumstances has bloomed since the invention of photography, with the possibility of capturing in real-time the occurrence of a specific events. Thanks to the widespread use of digital technologies (e.g. smartphones and digital cameras), networking capabilities and consequent availability of multimedia content, the academic and industrial research communities have developed artificial intelligence (AI) paradigms with the aim of inferring, transferring and creating new layers of information from images, videos, etc. Now, while AI communities are devoting much of their attention to analyze digital images, from an historical research standpoint more interesting results may be obtained analyzing analog images representing the pre-digital era. Within the aforementioned scenario, the aim of this work is to analyze a collection of analog documentary photographs, building upon state-of-the-art deep learning techniques. In particular, the analysis carried out in this thesis aims at producing two following results: (a) produce the date of an image, and, (b) recognizing its background socio-cultural context,as defined by a group of historical-sociological researchers. Given these premises, the contribution of this work amounts to: (i) the introduction of an historical dataset including images of “Family Album” among all the twentieth century, (ii) the introduction of a new classification task regarding the identification of the socio-cultural context of an image, (iii) the exploitation of different deep learning architectures to perform the image dating and the image socio-cultural context classification.

The influence of infill walls on seismic behavior of a historical building in Shanghai

Seismic assessment and seismic strengthening are the key issues need to be figured out during the process of protection and reusing of historical buildings. In this thesis the seismic behaviors of the hinged steel structure, a typical structure of historical buildings, i.e. hinged steel frames in Shanghai, China, were studied based on experimental investigations and theoretic analysis. How the non-structural members worked with the steel frames was analyzed thoroughly. Firstly, two 1/4 scale hinged steel frames were constructed based on the structural system of Bund 18, a historical building in Shanghai: M1 model without infill walls, M2 model with infill walls, and tested under the horizontal cyclic loads to investigate their seismic behavior. The Shaking Table Test and its results indicated that the seismic behavior of the hinged steel frames could be improved significantly with the help of non-structural members, i.e., surrounding elements outside the hinged steel frames and infilled walls. To specify, the columns are covered with bricks, they consist of I shape formed steel sections and steel plates, which are clenched together. The steel beams are connected to the steel column by steel angle, thus the structure should be considered as a hinged frame. And the infilled wall acted as a compression diagonal strut to withstand the horizontal load, therefore, the seismic capacity and stiffness of the hinged steel frames with infilled walls could be estimated by using the equivalent compression diagonal strut model. A SAP model has been constructed with the objective to perform a dynamic nonlinear analysis. The obtained results were compared with the results obtained from Shaking Table Test. The Test Results have validated that the influence of infill walls on seismic behavior can be estimated by using the equivalent diagonal strut model.