Design and test of a Smart Sensor Box for structural and seismological monitoring with a high-performance QMEMS accelerometer

Structural health monitoring (SHM) systems are critical to ensure the safety and integrity of various structures, such as buildings, bridges, viaducts, dams and pipelines. To guarantee the efficacy and reliability of SHM systems, the choice of hardware and sensors and precise planning of installation, data transfer mode, and processing are essential. In this study, we introduce a novel smart multi-parametric sensor box prototype for seismic and structural monitoring equipped with a digital accelerometer, a bi-axial inclinometer and a temperature, humidity and pressure sensor. In order to maximize performance, the Sensor Box has been tested with two digital tri-axial QMEMS accelerometers with high sensitivity and ultra-low self-noise densities, the best-performing of which has just 20 ng/√Hz of self-noise. The combination of data management from multiple sensors, precise timing and edge computing, is accomplished by utilizing two modular specialized boards. One has been designed for synchronization and the other to manage sensors and data by a dual-core STM32H7 microcontroller (MCU); the latter may also be connected to a single-board computer (SBC). The MCU's (or SBC's) ability to enable edge computing provides a robust platform for implementing early warning systems for numerous applications, including seismological and SHM approaches. In particular, we implemented an Earthquake Early Warning (EEW) system on-site that generates alerts and warnings in real-time upon detecting seismic events.

I. Introduction

The integration of MEMS sensors, edge computing, AI, and low-power hardware represents a significant advancement in the field of structural health monitoring (SHM) [e.g. 1]. This synergy not only enhances the accuracy of structural evaluations but also introduces intelligence and sustainability into the monitoring system. As these new frontiers emerge, the convergence of such technologies promises a future where structures are not merely monitored but actively cared for, ensuring resilience, safety, and sustainability in our built environment. Only in recent years has edge computing emerged as a transformative paradigm in the realm of SHM, offering a paradigm shift in how data is processed, analysed, and acted upon in real-time. This innovative approach addresses the challenges associated with traditional cloud-centric architectures by bringing computational capabilities closer to the data source—in this case, the monitored structure.