A hybrid wireless sensor network for acoustic emission testing in SHM

Große, Christian U.; McLaskey, Gregory C.; Bachmaier, S.; Glaser, Steven D.; Krüger, Markus

doi:10.1117/12.775641

Cited by 21 publications

(18 citation statements)

References 7 publications

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“…These include short-gauge, long-gauge, or distributed sensors in the increasing order of spatial coverage. As an example, the fiber optic sensors are typically placed on a straight line (i.e., one dimensional) throughout the structure and enable sensing over very large distances [9][10][11][12]. However, they also have a high spatial selectivity and generate a large signal only when the damage is nearby.…”

Section: Introductionmentioning

confidence: 99%

Large-Area Resistive Strain Sensing Sheet for Structural Health Monitoring

Aygün

Kumar

Weaver

et al. 2020

Sensors

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Damage significantly influences response of a strain sensor only if it occurs in the proximity of the sensor. Thus, two-dimensional (2D) sensing sheets covering large areas offer reliable early-stage damage detection for structural health monitoring (SHM) applications. This paper presents a scalable sensing sheet design consisting of a dense array of thin-film resistive strain sensors. The sensing sheet is fabricated using flexible printed circuit board (Flex-PCB) manufacturing process which enables low-cost and high-volume sensors that can cover large areas. The lab tests on an aluminum beam showed the sheet has a gauge factor of 2.1 and has a low drift of 1.5 μ ϵ / d a y . The field test on a pedestrian bridge showed the sheet is sensitive enough to track strain induced by the bridge’s temperature variations. The strain measured by the sheet had a root-mean-square (RMS) error of 7 μ ϵ r m s compared to a reference strain on the surface, extrapolated from fiber-optic sensors embedded within the bridge structure. The field tests on an existing crack showed that the sensing sheet can track the early-stage damage growth, where it sensed 600 μ ϵ peak strain, whereas the nearby sensors on a damage-free surface did not observe significant strain change.

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Section: Introductionmentioning

confidence: 99%

Large-Area Resistive Strain Sensing Sheet for Structural Health Monitoring

Aygün

Kumar

Weaver

et al. 2020

Sensors

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“…In recent years, monitoring systems based on acoustic emission have successfully been used to detect (fatigue) cracking, Grosse, McLaskey, Bachmaier, Glaser, and Krüger (2008), Holford, Davies, Pullin, and Carter (2001), Nair and Cai (2010). This type of system records the acousto-ultrasonic emissions generated by a growing crack, i.e.…”

Section: Introductionmentioning

confidence: 99%

Maintenance decision model for steel bridges: a case in the Netherlands

Attema

Acharige

Morales-Nápoles

et al. 2016

Structure and Infrastructure Engineering

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A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore, insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables result in significant uncertainties regarding model outcome. To reduce some of these uncertainties, the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties.

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“…Recently, a few wireless AE platforms, based on cellphone communication, have been created to perform structural monitoring of concrete or steel structures such as bridges (Ledeczi et al, 2009;Grosse et al, 2008). To date, none of the existing AE systems allow long-term monitoring under harsh outdoor conditions with autonomous power supply.…”

Section: Introductionmentioning

confidence: 99%

A custom acoustic emission monitoring system for harsh environments: application to freezing-induced damage in alpine rock walls

Girard

Beutel

Gruber

et al. 2012

Geosci. Instrum. Method. Data Syst.

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Abstract. We present a custom acoustic emission (AE) monitoring system designed to perform long-term measurements on high-alpine rock walls. AE monitoring is a common technique for characterizing damage evolution in solid materials. The system is based on a two-channel AE sensor node (AEnode) integrated into a wireless sensor network (WSN) customized for operation in harsh environments. This wireless architecture offers flexibility in the deployment of AE-nodes at any position of the rock wall that needs to be monitored, within a range of a few hundred meters from a core station connected to the internet. The system achieves near real-time data delivery and allows the user to remotely control the AE detection threshold. In order to protect AE sensors and capture acoustic signals from specific depths of the rock wall, a special casing was developed. The monitoring system is completed by two probes that measure rock temperature and liquid water content, both probes being also integrated into the WSN. We report a first deployment of the monitoring system on a rock wall at Jungfraujoch, 3500 m a.s.l., Switzerland. While this first deployment of the monitoring system aims to support fundamental research on processes that damage rock under cold climate, the system could serve a number of other applications, including rock fall hazard surveillance or structural monitoring of concrete structures.

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A hybrid wireless sensor network for acoustic emission testing in SHM

Cited by 21 publications

References 7 publications

Large-Area Resistive Strain Sensing Sheet for Structural Health Monitoring

Large-Area Resistive Strain Sensing Sheet for Structural Health Monitoring

Maintenance decision model for steel bridges: a case in the Netherlands

A custom acoustic emission monitoring system for harsh environments: application to freezing-induced damage in alpine rock walls

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