A Low Cost Platform for Distributed Data-Driven Structural Health Monitoring

Monitoring of infrastructures through sensor networks that acquire accelerations, tilts, angular velocities and environmental quantities can diagnose structural damage in time and prevent building collapse, thus becoming a key technology in today's smart-cities. This paper presents the design and prototyping of a custom low-cost synchronous Structural Health Monitoring (SHM) system. The goal of data-driven modeling of infrastructures through Machine Learning techniques imposes the constraint of synchronous and continuous monitoring and requires the ability to control the system and off-load data remotely to an Edge-Cloud continuum. In the proposed system analysis of the acquired signals can take place through REST calls on the storage system and the use of tools built in Python language. At the end of the work, preliminary tests of acquisition, analysis of data writing times and measurement of times-of-flight of an elastic wave on a building under investigation are presented.