Description and objectives

The project was created with the aim of developing an intelligent system capable of monitoring the structural health of buildings and infrastructure equipped with seismic devices such as isolators and dissipators in real time.

The system is designed to acquire and process data from sensors located near the anti-seismic devices and in other strategic parts of the building. This data, collected in a “black box” installed at the highest points of the structure, is saved, analysed and applied to a digital model of the structure (digital twin) to estimate structural risk in real time. Through the use of artificial neural networks and advanced numerical methods, the system is able to predict abnormal or dangerous behaviour caused by external stresses, such as earthquakes, wind or anthropogenic vibrations, anticipating potential malfunctions and suggesting maintenance interventions in advance.

In addition to its predictive function, the project aims to build a parametric database of structural responses, which will be useful for improving the future design of anti-seismic devices, while also contributing to the evolution of regulations in the sector. The data can be networked and shared with manufacturers, researchers, regulatory bodies and maintenance personnel.

The research is highly multidisciplinary and involves collaboration with university experts in the field of structural engineering. The scientific methodologies employed include statistical and stochastic signal analysis, data expansion using chaos polynomials to increase the effectiveness of neural networks, and the use of customised FEM software integrated into the Matlab environment for structural simulations.

The project introduces a product and system innovation in the field of seismic safety, making protective devices more reliable and durable.
In addition, it promotes a more widespread and informed use of these technologies, especially in public buildings such as schools, hospitals and bridges, where timely diagnosis and maintenance are essential for people’s safety.