Evidence of frost-cracking inferred from acoustic emissions in a high-alpine rock-wall

Amitrano, David; Gruber, Stephan; Girard, Lucas

doi:10.1016/j.epsl.2012.06.014

Cited by 87 publications

(108 citation statements)

References 32 publications

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“…The sensors were pressed on steel plates and screwed onto extension bolts anchored about 5 cm deep in rock. The high number of events detected on all channels, as well as the statistical properties of AEs, proved that the type of sensors were appropriate for this application (Amitrano et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…As we focus on progressive damage mechanisms that occur in the near-surface, the spatial detection range of AE monitoring (on the order of a meter) is sufficient. The feasibility of using AE monitoring for this purpose was assessed by carrying out a pilot study which yielded promising results (Amitrano et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies that have monitored AEs in outdoor conditions have been restricted to short time periods, during which the monitoring system was carefully protected from the changing environmental conditions in a nearby heated building, for example (Cheon et al, 2011;Amitrano et al, 2010Amitrano et al, , 2012.…”

Section: Introductionmentioning

confidence: 99%

“…In order to finalize the requirements of the AE platform to be designed for our multi-year monitoring objective, we first carried out a four-day pilot experiment using a commercially available AE acquisition system (Amitrano et al, 2012). During this experiment, a six-channel acquisition system was used to monitor AEs at the surface of a rock wall, which encountered daily freeze-thaw cycles.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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.

Self Cite

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“…The definition of an event can be ambiguous. Geoscientific studies often aim at 25 identifying events related to rupture or fracture in rock and/or ice originating for example from thermal stresses, pressure variations or earthquakes (Amitrano et al, 2012). We will refer to them as events or seismic events.…”

Section: Characterizationmentioning

confidence: 99%

Systematic Identification of External Influences in Multi-Year Micro-Seismic Recordings Using Convolutional Neural Networks

Meyer¹,

Weber²,

Beutel³

et al. 2018

Preprint

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Abstract. Natural hazards, e.g. due to slope instabilities, are a significant risk for the population of mountainous regions.Monitoring of micro-seismic signals can be used for process analysis and risk assessment. However, these signals are subject to external influences, e.g anthropogenic or natural noise. Successful analysis depends strongly on the capability to cope with such external influences. For correct slope characterization it is thus important to be able to identify, quantify and take these influences into account. 5In long-term monitoring scenarios manual identification is infeasible due to large data quantities demanding accurate automated analysis methods. In this work we present a systematic strategy to identify multiple external influences, characterize their impact on micro-seismic analysis and develop methods for automated identification. We apply the developed strategy to a real-word, multi-sensor, multi-year micro-seismic monitoring experiment on the Matterhorn Hörnliridge (CH). We present a convolutional neural network for micro-seismic data to detect external influences originating in mountaineers, a major un-10 wanted influence, with an error rate of less than 1 %, which is 3x lower than comparable algorithms. Moreover, we present an ensemble classifier for the same task obtaining an error rate of 0.79 % and an F1 score of 0.9383 by using images and micro-seismic data. Applying the classifiers to the experiment data reveals that approximately 1/4 of events detected with an event detector are not due to seismic activity but due to anthropogenic mountaineering influences and that time periods with mountaineer activity have a 9x higher event rate. Due to these findings we argue that a systematic identification of external 15 influences, like presented in this paper, is a prerequisite for a qualitative analysis.

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Microseismicity of an Unstable Rock Mass: From Field Monitoring to Laboratory Testing

et al. 2018

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The field‐scale microseismic (MS) activity of an unstable rock mass is known to be an important tool to assess damage and cracking processes eventually leading to macroscopic failures. However, MS‐event rates alone may not be enough for a complete understanding of the trigger mechanisms of mechanical instabilities. Acoustic Emission (AE) techniques at the laboratory scale can be used to provide complementary information. In this study, we report a MS/AE comparison to assess the stability of a granitic rock mass in the northwestern Italian Alps (Madonna del Sasso). An attempt to bridge the gap between the two different scales of observation, and the different site and laboratory conditions, is undertaken to gain insights on the rock mass behavior as a function of external governing factors. Time‐ and frequency‐domain parameters of the MS/AE waveforms are compared and discussed with this aim. At the field scale, special attention is devoted to the correlation of the MS‐event rate with meteorological parameters (air temperature and rainfalls). At the laboratory scale, AE rates, waveforms, and spectral content, recorded under controlled temperature and fluid conditions, are analyzed in order to better constrain the physical mechanisms responsible for the observed field patterns. The factors potentially governing the mechanical instability at the site were retrieved from the integration of the results. Abrupt thermal variations were identified as the main cause of the site microsesimicity, without highlighting irreversible acceleration in the MS‐event rate potentially anticipating the rock mass collapse.

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Evidence of frost-cracking inferred from acoustic emissions in a high-alpine rock-wall

Cited by 87 publications

References 32 publications

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

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

Systematic Identification of External Influences in Multi-Year Micro-Seismic Recordings Using Convolutional Neural Networks

Microseismicity of an Unstable Rock Mass: From Field Monitoring to Laboratory Testing

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