J. Cent. South Univ. (2015) 22: 3571−3577
DOI: 10.1007/s11771-015-2897-8
Acoustic emission characteristics of rock under impact loading
LIU Xi-ling(刘希灵)1, 2, LI Xi-bing(李夕兵)1, HONG Liang(洪亮)3, YIN Tu-bing(尹土兵)1, RAO Meng(饶蒙)1
1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China;
2. Norman B. Keevil Institute of Mining Engineering, University of British Columbia, Vancouver V6T1Z4, Canada;
3. School of Civil Engineering, Hunan City University, Yiyang 413049, China
© Central South University Press and Springer-Verlag Berlin Heidelberg 2015
Abstract: Acoustic emission tests were performed using a split Hopkinson pressure bar system (SHPB) on 50-mm-diameter bars of
granite, limestone, sandstone and skarn. The results show that the amplitude distribution of hits is not well centralized around 50 dB,
and that some hits with large amplitudes, usually larger than 70 dB, occur in the early stages of each test, which is different from the
findings from static and low-loading-rate tests. Furthermore, the dominant frequency range of the recorded acoustic emission
waveforms is between 300 kHz and 500 kHz, and frequency components higher than 500 kHz are not significant. The hit with the
largest values of amplitude, counts, signal strength, and absolute energy in each test, displays a waveform with similar frequency
characteristics and greater correlation with the waveform obtained from the elastic input bar of the split Hopkinson pressure bar
system compared with the waveforms of the other hits. This indicates that the hit with the largest values of amplitude, counts, signal
strength, and absolute energy is generated by elastic wave propagation instead of fracture within the rock specimen.
Key words: rock; acoustic emission (AE); split Hopkinson pressure bar (SHPB); hit driven features; frequency characteristics;
correlation analysis
1 Introduction
As a non-destructive means of testing, the acoustic
emission (AE) technique is widely used in geotechnical
engineering both for monitoring stability and for safety
forewarning in many underground mines. Some open
pits in China are handicapped by hidden cavities under
benches that were excavated by earlier underground
mining. Working personnel and machines are directly
threatened by these hidden dangers, especially drilling
machines working above the cap rock preparing
boreholes for blasting. Thus, safety related to the
problem of cavities is of great concern during the process
of open pit mining. The most urgent consideration in
open pits is the evaluation of the stability of the cap rock
overlying the cavity as bench blasting advances. An
effective method for this is to monitor rock mass rupture
within the cap rock using AE, and one associated task is
to monitor rupture as the drilling machines work above
[1−2]. In this case, the rock mass of the cap rock will
bear dynamic loads generated by drilling impact, and the
signals detected by the AE sensor should differ from
those obtained under static loads and age the same
situation as stress waves generated by bench blasting
propagating within the cap rock. Therefore, to provide
suggestions for the interpretation of the results obtained
from such on-site monitoring, the AE characteristics of
rock under such dynamic loads should be considered,
and this constitutes the original purpose of this work.
Actually, dynamic loads are common in mining and
geotechnical engineering, e.g., those generated by
drilling, blasting, earthquakes, and projectile penetration,
and the corresponding AE research would be useful for
on-site monitoring.
Furthermore, crack propagation and failure
mechanism are basic topics in the static and dynamic
performance of rocks. The AE technique is often used as
an auxiliary experimental means for studies of these
topics; however, because of the complexities of both the
theory and testing techniques in rock dynamics, the
dynamics of crack propagation and failure mechanism
are still not well understood. Therefore, it is meaningful
to perform research on the dynamic AE characteristics of
rock, which might provide additional perspective in
understanding rock dynamic mechanical properties, and
complete the research of rock AE characteristics under
intermediate and high-strain-rate loading.
It has been often quoted that AE history began in
1950 with the publication of Kaiser’s dissertation [3]. In
Foundation item: Projects(51204206, 41272304, 41372278) supported by the National Natural Science Foundation of China; Project(20110162120057)
supported by PhD Program Foundation of Ministry of Education China; Project(201012200232) supported by the Freedom Explore
Program of Central South University, China
Received date: 2014−07−23; Accepted date: 2014−11−26
Corresponding author: LIU Xi-ling, PhD; Tel: +86−731−88879612; E-mail: [email protected]
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the dissertation, KAISER [4] discovered a famous
irreversibility, which is now called the Kaiser effect. In
1979, DROUILLARD and LANER [5] compiled about
2000 references about AE, which were published in his
book “Acoustic Emission: A Bibliography with
Abstracts”. Since then, he has collected over 5000
additional references, many of which have been listed in
the AE literature section of the Journal of Acoustic
Emission [6]. Thus, numerous researches on rock AE
have been reported, such as studies of the AE
characteristics of rock deformation and failure under
tension, compression, and bending loads; those to
establish correlations between AE signals and rock
properties; investigations of the initiation point and
growth location of unstable cracking under various stress
states in rock specimens; studies of fractal and chaotic
features of rock AE; and determinations of the yield limit
of plastic rock by AE technique. However, this research
has been conducted mainly under static loads and seldom
has focused on rock AE characteristics under impact
loading. Some scholars have studied the effects of
loading rate on rock AE, but these have been conducted
mainly on experimental systems with low loading rates
[7−10]. SHCHERBAKOV et al [11] carried out tests that
exposed granite specimens to uniaxial compression and
impact using a dropped weight to obtain AE signals
generated by micro-cracking, and VETTEGREN et al
[12] studied the dynamics of fractoluminescence,
electromagnetic emission, and AE induced in a granite
specimen by a striker blow. However, these two studies
only discussed the AE rate and intensity, and a
comprehensive account of AE characteristics was
lacking. Furthermore, some on-site works have been
conducted to monitor the rupturing of the rock mass
during tunneling and rock burst in underground mines
using the AE technique; however, only the
corresponding impact loads were recorded and discussed
[13−16], and no related experimental studies have been
published. There have been few reports of research on
rock AE under conditions of high-rate loading, and we
suggest the principal reasons for this are as follows.
First, the rock mass being monitored generally bears
its own weight and its state of stability under such static
pressure is the subject of concern. The signals detected
by the AE sensors are generated mostly by the rock mass
rupturing under static pressure. Even when signals
generated by the stress waves of blasting or impact
drilling have been detected, because of the known time
and location of the vibration source, these signals can be
easily identified and used to calibrate the monitoring
apparatus. Thus, the on-site monitoring environment
based on static pressure is the principal reason why rock
AE characteristics under impact loading have seldom
been considered.
J. Cent. South Univ. (2015) 22: 3571−3577
Furthermore, compared with the monitoring of the
static performance of rocks, the testing methods used for
studying rock dynamics are unsatisfactory. Many
scholars have discussed the available testing methods
and their applications [17−25]. In addition, the
International Society for Rock Mechanics Commission
on Rock Dynamics has suggested the dynamic testing
methods to be used in determining the dynamic strength
of rock materials [26]; however, limitations still exist and
some dubious and unclear points still need to be
addressed [21, 27]. Therefore, the limitations of the
methods for rock dynamics testing are another reason
why such AE experimental research has been lacking.
However, as mentioned above, the topic of rock AE
characteristics under impact loading needs to be
addressed. Our experimental results and analysis on this
topic will be presented, which is intended to provide
additional perspective both for on-site monitoring and
research of rock dynamics.
2 Experimental setup
Because its loading rate matches well with those of
drilling and blasting, the 50-mm-diameter split
Hopkinson pressure bar (SHPB), recommended by the
International Society for Rock Mechanics Commission
on Rock Dynamics [26], was employed in this work as
the impact-loading device. A PCI-2 system and one
ultra-mini sensor-PICO (Physical Acoustics Corporation,
NJ, USA), glued onto the rock specimen, were used for
the collection of AE signals. A schematic of the entire
testing system is shown in Fig. 1. AE signals detected by
the sensor were pre-amplified by 40 dB; the detection
threshold and sampling rate were set at 45 dB and 40
Msps (million samples per second), respectively. Four
kinds of rock (granite, limestone, sandstone, and skarn)
were selected for the experimental study, and five
specimens of each rock type with a length (25 mm) to
diameter (50 mm) ratio of 0.5:1 were prepared for
testing.
Fig. 1 Schematic of split Hopkinson pressure bar system with
acoustic emission (AE) testing device (1−Gas tank; 2−Pressure
vessel; 3−Control valve; 4−Striker; 5−Light beams; 6−Input
bar; 7−Strain gauge; 8−Specimen; 9−Output bar;
10−Absorption bar; 11−Dash pot; 12−Electronic counter;
13−Bridge; 14−Ultra-dynamic strain gauge; 15−Transient wave
memory; 16−Data processing unit; 17−AE sensor; 18−AE
signal collecting and processing unit)
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[28]. It is also found that those hits with large amplitude
usually occur during the early stages of the hit sequence,
and all have numerous counts (Fig. 4).
3 Results and discussion
3.1 Analysis of hit driven AE features
There are controllable hit-based parameters
available on the PCI-2 board, and these parameters
demonstrate the basic features of the AE signal. Figure 2
shows a fictitious AE waveform with some typical
features. Here, several of them are chosen to illustrate
rock AE hit features under impact loading.
Fig. 3 Typical amplitude distribution histogram
Fig. 2 Acoustic emission (AE) hit features extraction diagram
With an increase of loading rate, the total AE
testing time drops, given the same parameter setup of the
AE testing system and similar rock properties. Thus, the
recorded number of hits decreases with an increase of
loading rate. Owing to the rapid dynamic loading and
rupturing, a much smaller number of hits (Table 1) were
recorded compared with the number in static and
low-loading-rate tests.
Table 1 Hit numbers of each test
Sample code
Granite
Limestone Sandstone
Skarn
1−1
85
72
60
73
1−2
88
73
29
78
1−3
78
97
68
130
1−4
46
57
72
92
1−5
97
67
65
59
The best-known intensity display of AE is the
amplitude distribution plot, which shows how many of
the hits were large and how many were small. The
amplitude distribution histogram, shown in Fig. 3, is
typical of the tested specimens. The histogram in Fig. 3
is scattered beyond the 45-dB threshold and has several
hits with large amplitude (usually larger than 70 dB).
This differs from the distribution in the static and
low-loading-rate tests, which are always centralized
around 50 dB and seldom exhibit large-amplitude hits
Fig. 4 Hits vs amplitude and counts
By investigating the recorded parameters of each
test, those hits with large amplitude and numerous counts
are also found to have large values of signal strength and
energy. In particular, one has values much larger than the
others in terms of amplitude, counts, signal strength
(Fig. 5), and absolute energy (Fig. 6). Moreover, such
things can be found in every type of rock specimen test.
Here, the symbol Hmax is used to represent the hit that has
the largest values of amplitude, counts, signal strength,
and absolute energy. To get a better understanding of
Hmax, a specific analysis will be described later in this
work.
3.2 Real-time frequency features
Frequency analysis plays an important role in AE
data interpretation, and the fundamental frequency
characteristics of an observed AE signal depend on the
source and its distance from the sensor. Frequencies
below 1 Hz have been observed at large-scale field sites,
whereas in laboratory studies, AE signals have often
been observed to contain frequencies greater than
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waveform in SHPB AE tests, and this waveform is
typical of a hit with small values of amplitude, counts,
signal strength, and absolute energy. It appears from
Fig. 8 that the frequencies are within the ranges of
geological material studies shown in Fig. 7 (④ and ⑥).
The dominant frequency range is between 300 kHz and
500 kHz, and the frequency components higher than 500
kHz are not significant. This is also the case for most of
the waveforms except for that of Hmax. However, as
shown in Fig. 9, the dominant frequency range is
between 10 kHz and 60 kHz, which indicates that the
relatively low-frequency components of Hmax are
significant compared with those of the other hits in the
rock AE SHPB tests.
Fig. 5 Signal strength of each type of rock specimen test
Fig. 6 Absolute energy of each type of rock specimen test
500 kHz [29]. Figure 7 indicates the frequency range
over which AE and other associated studies have been
conducted. It has also been reported that the AE
frequency range was 20 kHz to 100 MHz when granite
specimens were exposed to uniaxial compression and
impacted by a dropped weight [11].
Figure 8 shows a frequency spectrum of a typical
3.3 Hmax analysis
3.3.1 Waveform
As mentioned before, one hit with significantly
large values of amplitude, counts, signal strength, and
absolute energy was detected in every specimen test,
which is named Hmax. A typical waveform of Hmax is
shown in Fig. 10, and Fig. 11 illustrates a typical
waveform of a hit with small values of the corresponding
parameters.
It is very clear that the voltage value of the
waveform in Fig. 10 is much larger than that in Fig. 11,
and some parts even reach ±10 times the voltage range,
which means that the signal is very strong. Therefore, we
think that Hmax is not generated by rock fracturing, but
that it is the result of impact-induced elastic wave
propagation within the rock specimen instead. However,
for further verification, the ultra-mini sensor was fixed to
the input bar of the SHPB system and similar impact
loads applied. Because the elastic bar scarcely emits
fracturing waves under impact loading, the collected
waveforms are mostly of those induced by elastic wave
propagation. Figure 12 shows a typical waveform
collected on the input bar. If Hmax was generated by
elastic wave propagation, then the waveform collected
Fig. 7 Frequency ranges of various types of acoustic emission (AE) studies (Circled numbers represent regions where studies were
undertaken) [29]
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waveforms in Figs. 10, 11, and 12, respectively. The
amplitude of the spectrum in Fig. 14 is far less than those
in Figs. 13 and 15, and the frequency corresponding to
the large amplitude of the spectrum in Fig. 14 is higher
than those in Figs. 13 and 15. These all indicate that the
spectra in Figs. 13 and 15 share many similarities both in
amplitude and frequency distribution.
Fig. 8 Frequency spectrum of typical acoustic emission
waveform in SHPB tests
Fig. 11 Typical waveform of other hit with small values of
amplitude, counts, signal strength and absolute energy
Fig. 9 Frequency spectrum of typical waveform of Hmax
Fig. 12 Typical waveform of hit collected on elastic input bar
Fig. 10 Typical waveform of Hmax
on the input bar should be somewhat similar to the
waveform of Hmax. Therefore, the concern in the
following is to verify this similarity by analyzing their
frequency spectrum and correlation.
3.3.2 Spectrum analysis
Figures 13, 14, and 15 show the frequency spectra
(with zero-frequency component in the middle) of the
Fig. 13 Frequency spectrum of waveform in Fig. 10
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Table 2 Calculated correlation coefficient of S−M and S-E
S
E
Granite Limestone Sandstone Skarn
ρSE'= ρSM'=0.1
—
—
—
0.692
82
ρSE"=
Limestone
—
—
— ρSM"=0.296
0.580
ρSE'"=
—
—
ρSM'"=0.206 —
Sandstone
0.585
ρSE""=
ρSM""=
Skarn
—
—
—
0.601
0.219
Granite
3.3.4 Summary
With significantly large values of amplitude, counts,
signal strength, and absolute energy, Hmax is very
different from the other hits. The waveforms of Hmax and
the one collected on the elastic input bar share similar
frequency characteristics and have greater correlation.
This verifies that Hmax is generated by elastic wave
propagation within the rock specimens.
Fig. 14 Frequency spectrum of waveform in Fig. 11
4 Conclusions and future work
Fig. 15 Frequency spectrum of waveform in Fig. 12
3.3.3 Correlation analysis
Correlation coefficient is always used in digital
signal processing to evaluate the similarity of two signals.
If it is supposed that x(n) and y(n) are two finite signals,
then the correlation coefficient is defined as
L
 xy 
 x ( n ) y ( n)
n 1
L
L
[  x ( n )  y ( n )]
n 1
2
2
(1)
1/ 2
n 1
where L is the length of the signal.
It is demonstrated in Eq. (1) that |ρxy|≤1. If two
signals are completely correlated (the same signal), then
|ρxy|=1; if two signals are totally independent, ρxy=0.
Symbol S is used to represent the waveform of Hmax,
symbol M is used to represent the waveform of the hit
with small values of amplitude, counts, signal strength,
and absolute energy, and symbol E is used to represent
the waveform collected on the input bar. The calculated
correlation coefficients of the various typical waveforms
in each type of rock specimen test are listed in Table 2,
and the results show that S has greater similarity with E
than with M.
AE parameters of rock recorded in the tests
conducted on the SHPB system present many different
features compared with such tests under static and
low-rate loading. A particularly significant feature is the
existence of Hmax, and it is verified that Hmax is the result
of elastic wave propagation within the rock specimens.
Actually, the signals of rock fracturing and elastic
wave propagation are both detected by the sensor during
the process of the AE test under impact loading; thus,
Hmax is not the only signal generated by elastic wave
propagation. Of course, these hits generated by elastic
wave propagation can be considered an intrinsic feature
of rock AE under impact loading and can be used
together with other hits to describe the rock AE
characteristic. However, we consider that the signals
generated by fracturing are the ones of greatest interest,
and the two types of signals should be distinguished.
This distinction allows better understanding of the AE
characteristics of rock fracturing under impact loading,
and could be a reference for on-site AE monitoring in
environments of frequent dynamic loading. However, the
experimental results show that, in addition to Hmax, there
are no obvious features of other hits that can be used to
identify whether they are generated by rock fracturing or
by elastic wave propagation. Therefore, further
identification work must be performed and this will be
the focus of our future research.
Nomenclature
SHPB
AE
Hmax
Split Hopkinson pressure bar
Acoustic emission
Hit with the largest values of amplitude,
counts, signal strength and absolute energy
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x(n)
y(n)
L
ρxy
S
M
E
ρSE'
ρSE"
ρSE'"
ρSE""
ρSM'
ρSM"
ρSM'"
ρSM""
Finite signal appearing in Eq. (1)
Finite signal appearing in Eq. (1)
Length of the signal appeared in Eq. (1)
Correlation coefficient that can be calculated
from Eq. (1)
Waveform of Hmax
Waveform of hit with small values of
amplitude, counts, signal strength and absolute
energy
Waveform detected on the elastic input bar of
SHPB
Correlation coefficient of E and S in granite
test
Correlation coefficient of E and S in limestone
test
Correlation coefficient of E and S in sandstone
test
Correlation coefficient of E and S in skarn test
Correlation coefficient of S and M in granite
test
Correlation coefficient of S and M in limestone
test
Correlation coefficient of S and M in sandstone
test
Correlation coefficient of S and M in skarn test
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