Summary
Historic stone masonry structures shape the identity of many European cities. They stimulate the sense of history to new generations, engage local communities with their environment and promote the sense of a common past. Moreover, built heritage is a source of economic growth, while its protection and maintenance is key for the sustainable development of our urban environment. Nevertheless, the structural analysis of historic stone masonry buildings is challenging because the shape of the stones and their arrangement within a wall, which characterizes the structural behavior, is unique and therefore different for each building. The objective of DEMA-NDT is to derive digital twins of existing masonry walls from non-destructive tests. Non-Destructive Techniques are currently used as a way to estimate the presence of voids within the wall of an existing structure, but still cannot give an accurate representation of the internal morphology of the wall. DEMA-NDT aims to develop algorithms based on machine learning that will use as input data from NDT and photos from the face of the walls and can estimate with greater accuracy the internal morphology of existing walls than current algorithms. Such algorithms will revolutionize the structural assessment of existing masonry structures, because they will allow to design targeted and minimal interventions specific to each existing structure, minimizing costs, maximizing safety and compatibility with cultural heritage values.
General information
- PI: Savvas Saloustros (EESD / IIC)
- Start date: 01.09.2022
- Duration: 6 months