Evidence map›Paper›PMID 38931805›Full record

ArticleSensors (Basel, Switzerland)2024

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

Bruno Andò, Danilo Greco, Giacomo Navarra, Francesco Lo Iacono

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bruno AndòDepartment of Electrical Electronic and Computer Engineering (DIEEI), University of Catania, 95123 Catania, Italy.ORCID 0000-0001-5099-6131
Danilo GrecoDepartment of Electrical Electronic and Computer Engineering (DIEEI), University of Catania, 95123 Catania, Italy.ORCID 0009-0003-8646-1261
Giacomo NavarraDepartment of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy.ORCID 0000-0001-7997-8770
Francesco Lo IaconoDepartment of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy.ORCID 0000-0003-1035-8017

Funding

1.1.5 POR FESR 2014-2020 action G89J18000710007
6 · The paper itself

Abstract

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.

Indexed as

embedded architecturesensing systemsignals classificationstructural health monitoringtime–frequency analysis

Identifiers

PMID38931805
PMCPMC11207466

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.