PSI - Issue 64

Francesco Calabrò et al. / Procedia Structural Integrity 64 (2024) 1759–1766 Francesco Calabrò, Giovanna E. Minniti, Antonino Fotia, Raffaele Pucinotti / Structural Integrity Procedia 00 (2019) 000 – 000

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The implementation of an advanced monitoring system becomes crucial in this scenario, as it allows for continuous real-time assessment of the lesion's evolution and structural response. Calabria's seismic history and diverse geological conditions heighten the importance of such a monitoring system, providing insights into the structure's resilience against dynamic forces and potential vulnerabilities. In framing this case study, it is imperative to gather specific details about the building's historical background, construction materials, and any previous remedial actions undertaken. Additionally, obtaining information on local climate conditions, soil composition, and seismic history will contribute to a comprehensive understanding of the structural dynamics. These contextual details are vital for tailoring the monitoring system to the unique challenges posed by the regional context.

Fig. 2 . A Mansory structure with a crack on the principal facade. Moreover, the proposed monitoring system, likely employing drones and advanced imaging technologies, aims to capture high-resolution data on the lesion's characteristics. This data, processed through sophisticated algorithms, not only facilitates precise crack detection but also enables the system to predict potential structural issues. The seamless integration of this monitoring system with the broader investigative framework ensures a holistic approach, combining traditional contextual analysis with cutting-edge technological solutions for a thorough exploration of the masonry structure's health and stability. 4.2. Survey and modelling results The results of the survey and modeling process are anticipated to yield a comprehensive understanding of the structural conditions and the specific characteristics of the identified lesion on the primary facade of the masonry structure in Calabria. Leveraging advanced monitoring technologies, such as drones equipped with high-resolution cameras, the survey aims to capture detailed images and data points that will be instrumental in constructing a precise 3D model of the building. The collected imagery will be subjected to a meticulous analysis using sophisticated algorithms, enabling the identification and classification of the observed structural lesion. The implementation of a Convolutional Neural Network (CNN) for automatic fracture recognition in an image has yielded significant results in identifying and characterizing structural damages. The analysis conducted by the CNN has demonstrated a high accuracy in discriminating damaged areas, allowing for a quick and precise detection of fractures within the structure. The CNN has proven capable of recognizing specific details of fractures, including parameters such as size, shape,