Waveforms and frequency spectra of elastic emissions due to macrofractures in solids

Schiavi, Alessandro; Niccolini, Gianni; Tarizzo, Paolo; Carpinteri, Alberto; Lacidogna, Giuseppe; Manuello, A.

doi:10.1007/978-1-4614-0222-0_73

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Table 3 shows the values of the threshold, the sampling frequency, the high-pass filter, and the three time-parameters PDT, HDT, and HLT, which we calibrated to recognize correctly at least 95% of the hits from a sample of 500 AE randomly extracted from those recorded in the loading phases P3 and P4. We set the threshold on 60 dB to reject all the signals identified as noise in the first part of the loading phase P3; this value is consistent with other studies in the literature [ 37 , 49 ].…”

Section: Results Of the Case Studymentioning

confidence: 60%

“…The electrical signal identifies an acoustic event, also called a hit [ 46 ], when it crosses a certain threshold, expressed in volts (V) or similarly in decibels (dB). This threshold is defined as the minimum amplitude that the signal must have to be considered in the analysis [ 47 ]: typical values for reinforced concrete structures are around 40–45 dB [ 48 ], but sometimes it can be up to 60 dB [ 37 , 49 ]. Moreover, the signal must cross the threshold at least three times consecutively to be one hit.…”

Section: Ae Principle and Observable Quantitiesmentioning

confidence: 99%

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Structural Health Monitoring Based on Acoustic Emissions: Validation on a Prestressed Concrete Bridge Tested to Failure

Tonelli

Luchetta

Rossi

et al. 2020

Sensors

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The increasing number of bridges approaching their design life has prompted researchers and operators to develop innovative structural health monitoring (SHM) techniques. An acoustic emissions (AE) method is a passive SHM approach based on the detection of elastic waves in structural components generated by damages, such as the initiation and propagation of cracks in concrete and the failure of steel wires. In this paper, we discuss the effectiveness of AE techniques by analyzing records acquired during a load test on a full-size prestressed concrete bridge span. The bridge is a 1968 structure currently decommissioned but perfectly representative, by type, age, and deterioration state of similar bridges in operation on the Italian highway network. It underwent a sequence of loading and unloading cycles with a progressively increasing load up to failure. We analyzed the AE signals recorded during the load test and examined how far their features (number of hits, amplitude, signal strength, and peak frequency) allow us to detect, quantify, and classify damages. We conclude that AE can be successfully used in permanent monitoring to provide information on the cracking state and the maximum load withstood. They can also be used as a non-destructive technique to recognize whether a structural member is cracked. Finally, we noticed that AE allow classifying different types of damage, although further experiments are needed to establish and validate a robust classification procedure.

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Section: Results Of the Case Studymentioning

confidence: 60%

Section: Ae Principle and Observable Quantitiesmentioning

confidence: 99%

Structural Health Monitoring Based on Acoustic Emissions: Validation on a Prestressed Concrete Bridge Tested to Failure

Tonelli

Luchetta

Rossi

et al. 2020

Sensors

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“…It was found that the fault, stress and boundary deformation had a close interaction for the shear strain generation area evolved (Mekinnon 2006). It was also found the presence of AE relatively quiet period by rock uniaxial compression AE test (Schiavi et al 2011). It was found that the AE energy of material was accelerated after destruction through the AE monitoring tests on concrete, rock and other materials under uniaxial loading conditions (Carpinteri et al 2008).…”

Section: Current Status On the Precursor Characteristics Of Rockburstsmentioning

confidence: 97%

Introduction

Wang¹

2017

Evolution, Monitoring and Predicting Models of Rockburst

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“…Many scholars had some research on predicting rockburst. Schiavi et al (2011b) found that the phenomenon of a relatively quiet period appeared before rockburst under uniaxial compression. , Carpinteri and Lacidogna (2008) made testing on concrete, rock and other materials with AE monitoring technology, and the result demonstrated that AE energy accelerates release before rock failure.…”

Section: Introductionmentioning

confidence: 99%

Evolution, Monitoring and Predicting Models of Rockburst

Wang

2018

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Waveforms and frequency spectra of elastic emissions due to macrofractures in solids

Cited by 7 publications

References 6 publications

Structural Health Monitoring Based on Acoustic Emissions: Validation on a Prestressed Concrete Bridge Tested to Failure

Structural Health Monitoring Based on Acoustic Emissions: Validation on a Prestressed Concrete Bridge Tested to Failure

Introduction

Evolution, Monitoring and Predicting Models of Rockburst

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