Structural Health Monitoring Systems: an Overview

Scuro, Carmelo; Fusaro, Pierpaolo Antonio

doi:10.1109/metrolivenv54405.2022.9826933

Cited by 1 publication

(1 citation statement)

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“…Structural health monitoring (SHM) aims to provide a robust and effective approach to obtaining valuable information on structural conditions and supporting methodologies for the identification and, under specific conditions, localization of potential damages. Comprehensive reviews on SHM are available in [ 3 , 4 , 5 , 6 ], while their specific use in bridge monitoring is addressed in [ 7 ]. SHM can be implemented at either a global or local level [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Sensing Solution for SHM, Exploiting a Dedicated Approach for Signal Recognition

Andò,

Greco,

Navarra

et al. 2024

Sensors

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Health assessment and preventive maintenance of structures are mandatory to predict injuries and to schedule required interventions, especially in seismic areas. Structural health monitoring aims to provide a robust and effective approach to obtaining valuable information on structural conditions of buildings and civil infrastructures, in conjunction with methodologies for the identification and, sometimes, localization of potential risks. In this paper a low-cost solution for structural health monitoring is proposed, exploiting a customized embedded system for the acquisition and storing of measurement signals. Experimental surveys for the assessment of the sensing node have also been performed. The obtained results confirmed the expected performances, especially in terms of resolution in acceleration and tilt measurement, which are 0.55 mg and 0.020°, respectively. Moreover, we used a dedicated algorithm for the classification of recorded signals in the following three classes: noise floor (being mainly related to intrinsic noise of the sensing system), exogenous sources (not correlated to the dynamic behavior of the structure), and structural responses (the response of the structure to external stimuli, such as seismic events, artificially forced and/or environmental solicitations). The latter is of main interest for the investigation of structures’ health, while other signals need to be recognized and filtered out. The algorithm, which has been tested against real data, demonstrates relevant features in performing the above-mentioned classification task.

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