Spatial-temporal infrared radiation precursors of coal failure under uniaxial compressive loading

Infrared Physics and Technology 93 (2018) 144–153

Contents lists available at ScienceDirect

Infrared Physics & Technology journal homepage: www.elsevier.com/locate/infrared

Regular article

Spatial-temporal infrared radiation precursors of coal failure under uniaxial compressive loading ⁎

Liqiang Maa,b, , Hai Suna, a b

T

⁎

State Key Laboratory of Coal Resources and Mine Safety, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China Key Laboratory of Mine Geological Hazards Mechanism and Control, Xi’an 710054, Shaanxi, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Variance of original infrared image temperature (VOIIT) Variance of successive minus infrared image temperature (VSMIT) Infrared radiation characteristics Precursor of upcoming failure (PUF) Precursor of initial failure (PIF)

In order to find out the abnormity of infrared radiation temperature field during the coal bearing failure process and express the infrared radiation characteristics quantitatively at the time of coal failure. In this paper, two quantitative analysis indicators based on variance, Variance of Original Infrared Image Temperature (VOIIT) and Variance of Successive Minus Infrared Image Temperature (VSMIT), were adopted to study the infrared radiation (IR) characteristics of coals during uniaxial compression loading. Original infrared images and successive differencing of infrared images were used to reveal the spatial distribution characteristics and evolution process of fractures, and the spatial-temporal precursors of coal failures were obtained. The precursor of upcoming failure (PUF) and the precursor of initial failure (PIF) were discovered. About 80% of the coal samples tested showed significant PUF at a stress level of about 93% (ranging between 86.3 and 98.0%) relative to the peak stress (σp) on average. Typically, the PUF was observed about 30.0 s prior to a failure. Furthermore, about a minute prior to failure, 30% of the coal samples showed significant PIF at a stress level of about 80% relative to σp on average. This study demonstrates the potential ability to use infrared remote sensing technology for forecasts and early warnings of natural and engineering disasters caused by rock failures, such as rock bursts, landslides and earthquakes.

1. Introduction Mine disasters, including those related to the load bearing of rock, such as coal pillar instability, rock bursts, coal bursts, mine earthquakes, coal and gas outbursts, roof caving and water bursting in mines, occur frequently in the world. Rock fractures, which can also cause natural and engineering disasters, are the root cause of these disasters [1,2]. Therefore, effective monitoring and precursor identification of the load bearing process of rock are important foundations for the prevention, early warning, and reduction of these disasters. There have been countless reports of observations of infrared radiation (IR) related to rock failures in mines and earthquakes [3]. Hypotheses and models, such as piezoelectric potentials of quartz [4], streaming potentials by moving ground water [5], emanation of special gases [6], tribological electromagnetics [7], moving dislocations [8], surface oscillating dipoles [9,10] and P-hole effects [11] have been proposed successively in the scientific community in an attempt to explain the IR phenomena and characteristics in the process of rock fractures and failures.

Many scholars have conducted IR observations of rocks (concrete) under different loading conditions, finding that the IR precursors of fractures and failures are related to rock properties and the responses to stress. Most rocks produce IR images abnormalities before failure [12–16]. The spatial differentiation of radiation temperature fields and the IR abnormalities along future fracture zones occur in these images [17,18]. Therefore, the representations, types, and temporal and spatial migration characteristics of the IR precursors of rock fractures can be qualitatively obtained [19–21]. The concept of the average infrared radiation temperature (AIRT) of the rock surface, the turn of which can reflect the quantification of the IR abnormalities that occur before rock fractures and reveal that the precursor occurs at a stress level of 79–90% of σp, has been proposed for quantitative description [22,23]. However, IR effects are closely related to the strains of rocks and the development of fractures. Different temperature increases and decreases occur across regions of rocks (surfaces) at different stages of loading [16]. The mutual offsetting of these opposite trends is likely to result in no significant change in the AIRT, though there may be significant abnormalities in the infrared thermal images. Therefore, there

⁎ Corresponding authors at: State Key Laboratory of Coal Resources and Mine Safety, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China (L. Ma). E-mail addresses: [email protected] (L. Ma), [email protected] (H. Sun).

https://doi.org/10.1016/j.infrared.2018.07.034 Received 28 November 2017; Received in revised form 6 July 2018; Accepted 27 July 2018 Available online 29 July 2018 1350-4495/ © 2018 Published by Elsevier B.V.

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

(a) Isolation system

(b) Schematic diagram of the infrared radiation observation system Fig. 1. Infrared radiation observation system.

anomaly area of the infrared images at the corresponding time.; then, the damages on coal rock as well as the development of fractures were analyzed so as to predict the fracture zone in the sample and the development process. Accordingly, both temporal and spatial precursors of failures in coal sample were concluded. Infrared thermal imaging has gained popularity in the research field of non-contact, non-destructive monitoring and disasters in rock mechanics [24,25]. Thus, using Infrared remote sensing technology to study the IR precursors before coal failures promises to be an effective method [26–28].

is asynchrony between the abnormalities in the infrared thermal images and those in the AIRT [24]. Thus, it is necessary to find a better indicator for effectively expressing the IR precursor of rock failure. Under uniaxial loading, stress concentration appeared in coal sample, which led to fracture at this position and thereby induced stress drop. Because of local fracture, infrared radiation (IR) temperature field on the surface of coal sample would necessarily be differentiated and discretized. The variance of temperature field is appropriate for charactering the deviation degree of IR radiation temperature field from the mean value during the loading process. Apparently, a greater variance of the IR radiation temperature field on coal surface is indicative of more obvious differentiation in the field and more serious fracture degree in coal sample. This paper adopts two variance-based indicators for quantitative analysis based on the temporal and spatial evolution characteristics of the IR emitted from the surface of coal samples under uniaxial loading, namely the variance of original infrared image temperature (VOIIT) and the variance of successive minus infrared image temperature (VSMIT), to quantitative describe the evolution characteristics of IR temperature field of coal and rock under loading. The precursor of upcoming failure (PUF) of coal was obtained, which can be used as a significant indicator for predicting the failure of rock under loading. In addition, an attempt was made to discuss the precursor of initial failure (PIF) of the coal samples. According to the time of PUF and PIF appearance, the damage of coal and rock structure and the growth condition of cracks can be studied through the temperature

2. Experimental design 2.1. Experimental equipment and materials A SANS universal testing machine with a maximum load of 300 kN was used as the loading equipment in this experiment, and the load rate was of 0.2 mm/min. The infrared camera FLIR A615 was used to detect IR. The parameters of the camera were as follows: thermal sensitivity (NETD), less than 0.05 °C; resolution, 640 × 480 pixels; pixel pitch, 17 μm; time constant, 8 ms; frame rate, 25 frames per second; and wavelength range, 7.5–14 μm. The mechanical properties of coal depend on its physical properties, shape and size [29–31]. Two coal sizes, 50 mm × 50 mm × 100 mm and 50 mm × 50 mm × 50 mm, were selected to avoid the effects of 145

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

0.42

0.08

16

0.06

12

0

30

60

90

120

VOIITp

5 125.4 (Failure)

94.4 (PIF)

(PUF) 99.8

0.04

(PIF) 93.8

0.02 0.00 0

0 150

30

60

12

0.00

(PIF) 342

150

VOIITp

8

0

120

0

30

0.06

/MPa

VOIITp

0.03

399 (PUF)

4

(b) Coal sample B4

0.04

0.01

90

8

time/s

time/s

(a) Coal sample A1

0.02

(Failure) 111.5

(Failure) 617.4

0.04

4

0.02

0 600

0.00

(PIF) 522.1

498.5 (Failure)

300 450 time/s (c) Coal sample C2

0

140

20

(PUF) 548.3

280 420 time/s

560

/MPa

0.33 0.30

10

(PUF) 121.0

0.36

/MPa

VOIITp

0.39

/MPa

15

10

0 700

(d) Coal sample D9

Fig. 2. Changes in the temperature variances of the original infrared images and loads with time.

the same time before commencing the experiment, which did not occur until the infrared thermal images of coal samples showed no significant changes.

coal shape and size. Four lumps of coal obtained from four different coal mines were processed into 29 coal samples. The first lump of coal, obtained from the Xiaojiawa coal mine located in Lvliang coalfield, Shanxi province, was processed into seven 50 mm × 50 mm × 100 mm coal samples, which were numbered Ai (i = 1–7); the second lump of coal, obtained from the Xinjulong coal mine located in Heze coalfield, Shandong province, was processed into six 50 mm × 50 mm × 100 mm coal samples, which were numbered Bi (i = 1–6); the third lump of coal, obtained from the Bofang coal mine located in Jincheng coalfield, Shanxi province, was processed into six 50 mm × 50 mm × 50 mm coal samples, which were numbered Ci (i = 1–6); the fourth lump of coal, obtained from the Gaozhuang coal mine located in Zaozhuang coalfield, Shandong province, was processed into ten 50 mm × 50 mm × 50 mm coal samples, which were numbered Di (i = 1–10). The coal samples were placed under the same conditions as their natural environment at least 24 h prior to the experiment [32].

3. Experimental results 3.1. VOIIT 3.1.1. Definition Original infrared images are a series of surface temperature distribution images of an object output from an infrared camera. The twodimensional temperature matrix of the pth-frame original infrared image is expressed as:

fp (x , y )

(1)

where P represents the frame number index of infrared images; and x and y represent the row and column numbers of the temperature matrix of the camera, respectively. The physical meaning of VOIIT can reflect the variation tendency of the temperature field dispersion of the original infrared images in the coal samples loading process. The larger the VOIIT value, the more obvious evolution and differentiation of temperature field of original infrared image at this time than the Initial time. It is defined as [35]:

2.2. Experimental process The infrared camera was placed one meter in front of coal samples. The testing machine synchronously recorded the force and displacement data of coal samples under uniaxial loading until they fractured. An isolation box made of aluminum (Fig. 1(a)) was used to confine the path between the infrared thermal imager and the samples to reduce outside environmental influences [33,34]. Black paper was pasted on the inner surface of the aluminum plate in order to prevent light reflection by the aluminum. In the experiment, the graphite is evenly coated on the upper and lower end faces of the coal sample, which can reduce the friction between the coal sample and the indenter, so as to reduce the end effect without changing the mechanical properties of the coal samples. Fig. 1(b) is a schematic diagram of the IR observation of coal samples under uniaxial compressive loading [15]. The clocks of the camera and the testing machine were adjusted to

VOIITp =

1 1 MN

N

M

∑ ∑ [fp (x , y)−AIRTp ]2 y=1 x=1

where AIRTp =

1 1 MN

N

(2)

M

∑ ∑ fp (x , y ) ; and M and N represent the maxy=1 x=1

imums of x and y, respectively. 3.1.2. Temporal precursor The stress-time curves and VOIIT-time curves for the coal samples were drawn (see Fig. 2). Results demonstrate that the VOIIT and stress of all the coal samples showed a sudden change simultaneously when 146

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

samples Ai (i = 1–7) showed a PUF at a stress level of 87.0–97.5% (92.5% on average) of σp, 4.4–17.7 s (10.4 s on average) earlier than the time of failure; coal samples Bi (i = 1–6) showed a PUF at a stress level of 86.3–94.1% (90.4% on average) of σp, 11.7–49.9 s (31.0 s on average) earlier than the time of failure; coal samples Ci (i = 1–6) showed a PUF at a stress level of 93.6–98.0% (95.1% on average) of σp, 4.5–99.5 s (44.3 s on average) earlier than the time of failure; and coal samples Di (i = 1–10) showed a PUF at a stress level of 89.1–95.6% (92.6% on average) of σp, 18.0–59.6 s (32.5 s on average) earlier than the time of failure. The 50 mm × 50 mm × 100 mm coal samples showed a PUF closer to the time of failure than the shorter coal samples.

stress (σ) reached the peak stress (σp), and they suffered a failure (macro-fracture). For example, coal samples A1, B4, C2, and D9 showed a sudden change at 125.4 s, 111.5 s, 498.5 s, and 617.4 s, respectively. However, is it possible that the VOIIT would undergo a sudden change before the σ of the coal samples reaches σp, and as such, allows us to predict the failures of the coal samples? In order to find the infrared radiation precursor of coal and rock failure, the Pauta criterion is adopted in this article. Using the three times standard deviation of VOIIT amplitude (variation amplitude of adjacent frames variance) as a threshold to screen it. If the infrared radiation mutation exceeds the threshold, it will be considered as a precursor. The study found that there were multiple infrared radiation precursors before the coal samples failure. We refer to the infrared radiation precursor near the failure as PUF, and the infrared radiation that has a certain period of time from the coal samples failure is called PIF.

(2) PIF Nine coal samples (accounting for 31.0% of the total coal samples) showed a PIF, meaning they showed a sudden change in their temperature variance before they showed a PUF. For example, coal sample A1 showed a “pulse” change at 94.4 s, coal sample B4 showed a “leap” change at 93.8 s, coal sample C2 showed a “leap” change at 342 s, and coal sample D9 showed a “pulse” change at 522.1 s, indicating the initial significant macro-fractures of the coal samples (see Fig. 2) Table 2 shows both the ratio of σ to σp when the coal samples showed a PIF and the period of time prior to the onset of failure. The coal samples showed a PIF at a stress level of 60.4–91.9% (79.5% on average) of σp, 14.2–156.0 s (64.1 s on average) earlier than the time when they suffered a failure. Therefore, this stress level can be used for the medium-range forecasts of coal failures. The fracture that the coal samples suffered while in the plastic deformation regime before reaching σp and suffering a failure could cause adjustments in the stresses and IR abnormalities, resulting in sudden changes in VOIIT. These changes include sudden decreases exhibited by samples A1 and C2, sudden increases exhibited by samples B4 and D9 (see Fig. 2), and the formation of PIF and PUF.

(1) PUF Before σ approached σp and coal samples suffered a failure, the VOIIT of some coal samples showed a sudden change, i.e. a PUF, which can be used for short-range forecasts. For example, coal samples of A1, C2 and D9 showed a “leap” change at 121.0 s, 399.0 s and 548.3 s, respectively. Coal sample B4 showed a “pulse” change at 99.8 s (see Fig. 2). Table 1 depicts the ratio of σ to σp when coal samples showed a PUF, and how long coal samples showed a PUF prior to the time when they suffered a failure. The coal samples showed a PUF at a stress level of 86.3–98.0% (the total average was 92.7%) of σp, 30.0 s earlier than the time when they suffered a failure. Therefore, this stress level can be used as a warning for short-range and impending forecasts. Coal Table 1 PUF characteristics.

3.1.3. Significance

Coal sample No.

σ/σp/%

Time prior to the time of a failure/s

A1 A2 A3 A4 A5 A6 A7 Average B1 B2 B3 B4 B5 B6 Average C1 C2 C3 C4 C5 C6 Average D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 Average Average

97.5 93.9 / 91.3 / 92.7 87.0 92.5 89.0 / 92.0 94.1 / 86.3 90.4 95.2 94.1 93.6 98.0 94.7 / 95.1 92.1 93.9 / 95.6 90.4 95.5 95.6 91.4 89.1 89.7 92.6 92.7

4.4 17.7 / 11.7 / 6.8 11.3 10.4 43.6 / 18.6 11.7 / 49.9 31.0 4.5 99.5 73 39.1 5.5 / 44.3 23.0 22.5 / 18.0 36.5 30.1 20.5 29.9 59.6 52.7 32.5 30.0

(1) PUF The amplitude of the VOIIT, Ap , was defined as the absolute value (dimensionless) of the difference between VOIITs at two adjacent moments. Ap in the pth-frame is:

Ap = |VOIITp + 1−VOIITp|

(3)

Table 3 presents the amplitudes of the VOIIT when PUF occurs (PUF amplitude) and average amplitude of the coal samples in the loading process as well as the ratio between the two. The PUF amplitude of all the coal samples was 106.7 times as large as their average amplitude. The PUF amplitudes of the 50 mm × 50 mm × 100 mm coal samples were 17.0–501.9 times as large as their average amplitudes: those coal samples in group A were 308.1 times on average as large as their average amplitudes; and those coal samples in group B were 141.2 times on average as large as their Table 2 PIF characteristics.

Six coal samples such as A3 and A5 did not show a PUF. 147

Coal sample No.

σ/σp/%

Time prior to the time of a failure/s

A1 A4 B1 B4 C1 C2 D5 D7 D9 Average

71.0 88.1 69.4 84.9 91.9 82.4 60.4 83.4 83.6 79.5

31.0 28.7 36.3 17.7 14.2 156.0 155.0 61.5 76.8 64.1

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

Table 3 PUF amplitudes of VOIIT.

Table 4 PIF amplitudes of VOIIT.

Coal sample No.

PUF amplitude/10-4

Average amplitude/10−4

Ratio

Coal sample No.

PIF amplitude/10−4

Average amplitude/10−4

Ratio

A1 A2 A3 A4 A5 A6 A7 Average

186.7 620.1 / 853.2 / 264.3 221.4 429.1

11.0 1.4 1.4 1.7 1.2 0.7 1.1 2.6

17.0 442.9 / 501.9 / 377.6 201.3 308.1

B1 B2 B3 B4 B5 B6 Average

115.3 / 22.2 419.7 / 168.3 181.4

1.0 1.0 1.1 1.4 1.5 1.3 1.2

115.3 / 20.2 299.8 / 129.5 141.2

A1 A4 B1 B4 C1 C2 D5 D7 D9

197.9 53.1 33.3 29.0 84.3 33.9 31.1 21.8 22.3

11.0 1.7 1.0 1.4 1.7 2.2 2.1 1.7 1.9

18.0 31.2 33.3 20.7 49.6 15.4 14.8 12.8 11.7

Average

56.3

2.7

23.1

C1 C2 C3 C4 C5 C6 Average

247.1 33.3 35.9 23.3 8.7 / 69.7

1.7 2.2 1.8 1.8 1.3 1.9 1.8

145.4 15.1 19.9 12.9 6.7 / 40.0

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10

33.2 12.3 / 21.2 31.0 47 23.3 19 38.8 24.2

1.9 1.5 2.0 1.7 2.1 1.3 1.7 1.6 1.9 1.8

17.5 8.2 / 12.5 14.8 36.2 13.7 11.9 20.4 13.4

Average Average

27.8 150.8

1.8 1.9

16.5 106.7

3.1.4. Spatial precursor The infrared precursors of the failures of coal samples obtained based on VOIIT were based on the changes in the variance curves. Therefore, only the temporal information about the infrared precursors was obtained. To obtain the spatial information about the infrared precursors, it is necessary to observe the corresponding infrared images. Denoising original infrared images are usually used to represent the structural responses of coal samples under loading [13]. Figs. 3 and 4 show the denoising original infrared images of coal samples B4 and C2, respectively. The temperatures in the infrared image of a coal sample were distributed evenly and indiscreetly at the beginning of loading. A small area, which was characterized by the abnormality in infrared temperatures, appeared in the infrared image at the moment when PIF occurred. As loading continued, this area spread gradually and developed into an area characterized by obvious abnormality when the coal sample showed a PUF. This resulted in changes in the discreteness of the temperatures distributed in space, which was observed as a sudden increase in coal sample B4 (see Fig. 3(e)) and coal sample C2 (see Fig. 4(e)). The coal sample split along this area in the form of beveling when it suffered a failure. The migration of the abnormal area in the infrared image reflected the spatial characteristics of the development, migration, and propagation of the fractures of the coal sample. However, factors affecting these results are the temperature resolution of the camera and the ability of the human eye to resolve image. A sudden change in VOIIT is caused by the abnormality in the temperatures in an infrared image. Therefore, the temporal information about the precursor of the failures of the coal samples was obtained by first analyzing the change in VOIIT with stress. By analyzing the corresponding infrared images, the spatial information about the precursor of the coal sample failures was obtained. The mutual complementation of the temporal and spatial information about the infrared precursors is apparent from IR data.

Six coal samples such as A3 and A5 did not show a PUF.

average amplitudes. The PUF amplitudes of the 50 mm × 50 mm × 50 mm coal samples were 6.7–145.4 times as large as their average amplitudes: the coal samples in group C were 40.4 times on average as large as their average amplitudes; and the coal samples in group D were 16.5 times on average as large as their average amplitudes. VOIIT significantly reflected the information about the precursor of the failure of coal samples. The average of the amplitudes of all the coal samples at the compaction and elastic deformation stages was 1.9 × 10−4, and the average amplitudes of both the 50 mm × 50 mm × 100 mm and 50 mm × 50 mm × 50 mm coal samples fell in the interval 1 × 10−4–2 × 10−4 with no significant difference (the amplitudes of the 50 mm × 50 mm × 100 mm coal samples were slightly smaller than those of the 50 mm × 50 mm × 100 mm coal samples). The total average of the PUF amplitudes of all coal samples was 150.8 × 10−4: those in group A fell in the interval 186.7 × 10−4–853.1 × 10−4; those in group B fell in the interval 22.2 × 10−4–419.7 × 10−4; those in group C fell in the interval 8.7 × 10−4–247.1 × 10−4; and those in group D fell in the interval 12.3 × 10−4–38.8 × 10−4. The PUF amplitudes of the 50 mm × 50 mm × 100 mm coal samples changed more significantly than those of the 50 mm × 50 mm × 50 mm coal samples.

3.1.5. Universality There were 23 coal samples (79.3% of all samples) that showed a PUF, including five samples in group A (71.4%), four samples in group B (66.7%), five samples in group C (83.3%), and nine samples in group D (90%). The probability of detecting failure precursors is 41% in previous studies [19]. In this paper, the detection probability is 79.3% with the universality obtained by adopting indicator of VOIIT, greatly improving the probability of detecting coal and rock failure precursors and can be used for the early warning of coal failures. In addition, all coal samples that showed a PIF also showed a PUF. The coal samples from different coal mines showed an infrared precursor at slightly different moments, but the stress level range was generally 86.3–98.0% (the total average was 92.7%) of σp. This stress level can be used as a sign for the monitoring of coal stability.

(2) PIF Table 4 presents the amplitudes of the VOIIT when PIF occurs (PIF amplitude) and average amplitudes of coal samples in the loading process as well as the ratio between them. The PIF amplitudes of VOIIT fell in the interval 21.8–197.9 (56.3 on average), 11.7–49.6 times (23.1 times on average) as large as its average amplitudes. 148

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

a t=0.9s, the infrared image at the beginning of loading; b t=93.3 s, the infrared image followed by the occurrence of a PIF; c t=93.8 s, the infrared image when the coal sample showed a PIF; d t=99.1 s, the infrared image followed by the occurrence of a PUF; e t=99.8 s, the infrared image when the coal sample showed a PUF; f t=111.3 s, the infrared image followed by the failure of the coal sample; g t=111.5 s, the thermal image when the coal sample suffered a failure

respectively (see Fig. 5). It was analyzed whether or not the VSMIT of the coal samples could show a sudden change before their σ reached σp to make it possible to predict the failures of the coal samples. Using the three times standard deviation of VSMIT amplitude (variation amplitude of adjacent frames variance) as a threshold to screen it. If the infrared radiation mutation exceeds the threshold, it will be considered as a precursor. We refer to the infrared radiation precursor near the failure as PUF, and the infrared radiation that has a certain period of time from the coal samples failure is called PIF.

3.2. VSMIT 3.2.1. Definition A series of successive difference infrared images can be obtained from the original infrared images taken at constant time intervals, having each succeeding image subtracting its immediate predecessor. It describes the difference between the IR temperatures at two adjacent moments and reduces the effect of radiance differences and environmental radiation differences on coal samples. In this study, the time interval was set to one frame to timely and reliable discover abnormalities in the momentary change of the coal surface IR temperature by making full use of the data about the original infrared images. The twodimensional temperature matrix of the pth-frame successive difference infrared image of a coal sample is expressed as:

φp (x , y ) = fp + 1 (x , y )−fp (x , y )

(1) PUF Before σ approached σp and the coal samples suffered a failure, the VSMIT of 19 coal samples (79.3% of all coal samples) showed a significant “pulse” change, namely PUF (see Fig. 5). VSMIT clearly showed a PUF at the same time with VOIIT. Table 1 depicts the ratio of σ to σp when VSMIT showed a PUF, and how long the coal samples showed a PUF prior to the time when they suffered a failure.

(4)

where fp (x , y ) is the two-dimensional temperature matrix of the pthframe original infrared image, and fp + 1 (x , y ) is the two-dimensional temperature matrix of the (p + 1)th frame of an original infrared image. The physical meaning of VSMIT can reflect the degree of the successive minus infrared images deviate from the mean value, which emphasizes the sudden change of the infrared radiation temperature field. The larger the VSMIT value, the greater temperature field of original infrared images will instantaneous change (adjacent frames). It is defined as:

VSMITp =

AIRTp′ =

1 1 MN

1 1 MN

N

y=1 x=1

N

(2) PIF Similar to VOIIT, the VSMIT of nine coal samples (31.0% of all coal samples) showed a PIF, as shown in Fig. 5. Table 2 both shows the ratio of σ to σp when VSMIT showed a PIF, and the period of time prior to the onset of failure.

M

∑ ∑ [φp (x , y)−AIRTp′]2

Fig. 3. Original infrared images of the coal sample B4. (a) t = 0.9 s, the infrared image at the beginning of loading; (b) t = 93.3 s, the infrared image followed by the occurrence of a PIF; (c) t = 93.8 s, the infrared image when the coal sample showed a PIF; (d) t = 99.1 s, the infrared image followed by the occurrence of a PUF; (e) t = 99.8 s, the infrared image when the coal sample showed a PUF; (f) t = 111.3 s, the infrared image followed by the failure of the coal sample; (g) t = 111.5 s, the thermal image when the coal sample suffered a failure.

3.2.3. Significance The amplitude of the VSMIT, Ap′ , was defined as the absolute value (dimensionless) of the difference between VSMITs at two adjacent moments. Ap′ in the pth frame:

(5)

M

∑ ∑ φp (x , y)

Ap′ = |VSMITp + 1−VSMITp|

y=1 x=1

(6)

Table 5 presents the amplitudes of the VSMIT when PUF occurs (PUF amplitude) and average amplitudes of the coal samples in the loading process as well as their ratios.

3.2.2. Temporal precursor Fig. 5 shows the stress-time curves and VSMIT-time curves for coal samples. Similar to VOIIT, the VSMIT and stress of all the coal samples showed a sudden change at the same time when their σ reached σp, and they suffered a failure. For example, coal samples A1, B4, C2, and D9 showed a sudden change at 125.4 s, 111.5 s, 498.5 s, and 617.4 s,

(1) PUF The PUF amplitude was 281.5 times as large as the average

a t=0.9 s, the infrared image at the beginning of loading; b t=341.7 s, the infrared image followed by the occurrence of a PIF; c t=342.0 s, the infrared image when the coal sample showed a PIF; d t=398.2 s, the infrared image followed by the occurrence of a PUF; e t=399.0 s, the infrared image when the coal sample showed a PUF; f t=498.3 s, the infrared image followed by the failure of the coal sample; g t=498.5 s, the thermal image when the coal sample suffered a failure by the failure of the coal sample; (g) t = 498.5 s, the thermal image when the coal sample suffered a failure. 149

Fig. 4. Original infrared images of the coal sample C2. (a) t = 0.9 s, the infrared image at the beginning of loading; (b) t = 341.7 s, the infrared image followed by the occurrence of a PIF; (c) t = 342.0 s, the infrared image when the coal sample showed a PIF; (d) t = 398.2 s, the infrared image followed by the occurrence of a PUF; (e) t = 399.0 s, the infrared image when the coal sample showed a PUF; (f) t = 498.3 s, the infrared image followed

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

0.03

0.08

16

0.06

12

0.01

(PUF) 121.0

(PIF) 94.4

0.00 0

30

60

90

120

10

5

8

0.04 (PUF) 98.8

0.02

0 150

0.00 0

4

(PIF) 93.8

30

60

90

120

0

time/s

time/s (a) Coal sample A1

(b) Coal sample B4

0.012

12

0.008

8

0.004

(PUF) 399.0 (Failure) 498.5 4

0.03

(PIF) 342.0

0

150

300

450

VSMITp

/MPa

0.02

0.01

0.00 0

0 600

(Failure) 522.1 20

(PUF) (PIF) 548.3 522.1

140

280

420

560

/MPa

30

VSMITp

0.000

(Failure) 111.5

/MPa

(Failure) 125.4

VSMITp

0.02 /MPa

VSMITp

15

10

0 700

time/s

time/s

(c) Coal sample C2

(d) Coal sample D9

Fig. 5. Changes in the temperature variances of the successive difference infrared images and loads with time.

3.2.4. Spatial precursor Figs. 6 and 7 show the successive difference infrared images of coal samples B4 and C2, respectively. Based on the principle of successive difference infrared images, it is possible to identify the temperature increase (decrease) of two adjacent frames of infrared images of a coal sample. When it showed a PUF, the abnormal area in its infrared image was significant, which is demonstrated in coal samples B4 in Fig. 6 and C2 in Fig. 7, respectively. When it suffered a failure, it split along this abnormal area in the form of beveling. The characteristics in the infrared image are the most significant when this indicator reaches its maximum, meaning the temperatures in an infrared image are most discrete. Therefore, this moment is the right time to identify the region with upcoming failure based on the load bearing of surrounding rock.

amplitude in the loading process. The PUF amplitudes of the 50 mm × 50 mm × 100 mm coal samples were 11.1–1905.7 times as large as their average amplitudes: those of the coal samples in group A were 825.4 times on average as large as their average amplitudes; and those of the coal samples in group B were 368.2 times as large as their average amplitudes. The PUF amplitudes of the 50 mm × 50 mm × 50 mm coal samples were 8.0–448.5 times as large as their average amplitudes: those of the coal samples in group C were 134.4 times on average as large as their average amplitudes; and those of the coal samples in group D were 22.6 times as large as their average amplitudes. At the early stage (the compaction and elastic deformation stages) of loading, the average of the amplitudes of the coal samples was 0.7 × 10−4, and the average amplitudes of both the 50 mm × 50 mm × 100 mm and 50 mm × 50 mm × 50 mm coal samples fell in the interval 0.6 × 10−4–1.0 × 10−4 with no significant difference (the amplitudes of the 50 mm × 50 mm × 100 mm coal samples were slightly smaller than those of the 50 mm × 50 mm × 50 mm coal samples). The total average of the PUF amplitudes was 193.1 × 10−4: those in group A fell in the interval 8.9 × 10−4–1334.0 × 10−4; those in group B fell in the interval 53.0 × 10−4–596.6 × 10−4; those in group C fell in the interval 16.6 × 10−4–117.4 × 10−4; and those in group D fell in the interval 5.6–35.7. It is evident that the PUF amplitudes of the 50 mm × 50 mm × 100 mm coal samples changed more significantly than those of the 50 mm × 50 mm × 50 mm coal samples.

3.2.5. Universality The coal samples whose VSMIT showed a PUF accounted for 79.3% of the total coal samples, just as the coal samples whose VOIIT showed a PUF accounted for 79.3% of the total coal samples. The PUF of VSMIT was also universal when failures were about to occur. Therefore, it can be used as a criterion for the upcoming failures of coal samples under uniaxial loading. 4. Discussions (1) Features of VOIIT and VSMIT

(2) PIF The IR temperature field of coal and rock displays differentiation and discretization phenomena before suffering failure. However, existing indicators cannot reflect the spatial differentiation characteristics of the temperature field and the time-phase characteristics of the change in the temperature field effectively. This paper adopts two indicators, VOIIT and VSMIT. VOIIT is defined as the discreteness of the

Table 6 presents the amplitudes of VSMIT when PIF occurs (PIF amplitude) and average amplitudes of coal samples in the loading process as well as the ratio between them. The PIF amplitudes of VSMIT fell in the interval 5.4–60.8 (26.6 on average), which were 6.7–86.9 times (36.9 on average) as large as the average amplitudes. 150

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

surface IR temperature values of a coal sample at a specific moment; VSMIT is defined as the discreteness of the changes in the IR temperature. The two indicators showed insignificant changes during the compaction and elastic deformation of coal samples. However, they showed synchronous sudden changes during the plastic deformation of coal samples. These synchronous changes present the capability of overcoming AIRT’s lack of significance and universality in reflecting infrared information [19].

Table 5 PUF amplitudes of VSMIT. Coal sample No.

PUF amplitude/10−4

Average amplitude/10−4

Ratio

A1 A2 A3 A4 A5 A6 A7 Average

8.9 1334.0 / 869.2 / 247.5 209.5 533.8

0.8 0.7 0.6 0.6 0.8 0.6 0.6 0.7

11.1 1905.7 / 1448.7 / 412.5 349.2 825.4

B1 B2 B3 B4 B5 B6 Average

97.4 / 53.0 407.7 / 596.6 288.7

0.7 0.6 0.6 0.7 0.7 0.9 0.7

139.1 / 88.3 582.4 / 662.9 368.2

C1 C2 C3 C4 C5 C6 Average

296.0 19.3 22.7 16.6 117.4 / 94.4

0.7 0.9 1.0 1.0 0.7 1.0 0.9

448.5 20.7 23.4 16.2 163.1 / 134.4

D1 D2 D3 D4 D5 D6 D7 D8 D9 D10

33.7 15.5 / 18.2 10.6 9.4 5.6 35.7 8.8 8.3

0.7 0.7 0.8 0.7 0.9 0.7 0.7 0.7 0.8 0.7

48.1 22.1 / 26.0 11.8 13.4 8.0 51.0 11.0 11.9

Average Average

16.2 193.1

0.7 0.7

22.6 281.5

(2) Characteristics of PUF and PIF Wu et al. [22] used the AIRT indicator and found that there are three forms of PUF of the coal and rock (short dropping, fast rising and dropping-to-rising). However, the results were only analyzed qualitatively and lacked of quantitatively analysis. In this paper, the PUF amplitude of VOIIT and VSMIT of the coal samples was 106.7 and 281.5 times their average amplitude during the loading process was significant, respectively. The thermal infrared imager only detects the IR temperature of one surface of the coal sample without detecting the other three surfaces’ IR temperature. Additionally, coal is a special kind of rock with complex and changeable defects in large discrete in it, such as pores and fissures. It has lower ultimate strength compared with hard rocks such as granite and marble [19]. Thus, not every coal sample will show a significant infrared precursor before failure. The probability of detecting failure precursors is 41% in previous studies [19]. In this paper, the detection probability is 79.3% by adopting indicators of VOIIT and VSMIT, greatly improving the probability of detecting coal and rock failure precursors. Compared to the precursors in the previously studies, the two indicators adopted in this paper are easier to detect the infrared radiation precursors before coal and rock failure and provide a new way to quantitatively analyze the infrared radiation for coal and rock during loading and catastrophic process.

Six coal samples such as A3 and A5 did not show a PUF.

(3) The generation mechanism of PUF and PIF

Table 6 PIF amplitudes of the VSMIT. −4

Average amplitude/10

−4

Coal sample No.

PIF amplitude/10

A1 A4 B1 B4 C1 C2 D5 D7 D9

26.7 47.8 14.0 26.5 60.8 6.0 40.0 5.4 12.3

0.8 0.6 0.7 0.7 0.7 0.9 0.9 0.7 0.8

33.4 79.7 20.0 37.9 86.9 6.7 44.4 7.7 15.4

Average

26.6

0.8

36.9

Both fracture and failure in coal rock depend on the applied stress field. Once the applied stress increases to satisfy unstable crack propagation condition, fracture will occur in the coal [36]; at that moment, the applied stress may still not reach the failure strength, and the fractured coal may not reach the ultimate bearing capacity. Failure (i.e., macro-fracture) will occur when the applied stress reaches or exceeds the rock’s failure strength [36]. During the loading process, local stress concentration can be observed in the coal sample before failure, thereby inducing fracture and stress drop. Local fractures can inevitably bring about differentiation and discretization in infrared radiation field on the coal surface, causing drastic changes of VOIIT and VSMIT, two infrared radiation variance indexes. Therefore, infrared radiation variance indexes and stress mutate simultaneously, and infrared thermography becomes abnormal in

Ratio

a t=0.9 s, the infrared image at the beginning of loading, b t=93.3 s, the infrared image followed by the occurrence of a PIF, c t=93.8 s, the infrared image when the coal sample showed a PIF, d t=99.1 s, the infrared image followed by the occurrence of a PUF, e t=99.8 s, the infrared image when the coal sample showed a PUF, f t=111.3 s, the infrared image followed by the failure of the coal sample, g t=111.5 s, the thermal image when the coal sample suffered a failure 151

Fig. 6. Successive difference infrared images of the coal sample B4. (a) t = 0.9 s, the infrared image at the beginning of loading, (b) t = 93.3 s, the infrared image followed by the occurrence of a PIF, (c) t = 93.8 s, the infrared image when the coal sample showed a PIF, (d) t = 99.1 s, the infrared image followed by the occurrence of a PUF, (e) t = 99.8 s, the infrared image when the coal sample showed a PUF, (f) t = 111.3 s, the infrared image followed by the failure of the coal sample, (g) t = 111.5 s, the thermal image when the coal sample suffered a failure.

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

Fig. 7. Successive difference infrared images of the coal sample C2. (a) t = 0.9 s, the infrared image at the beginning of loading, (b) t = 341.7 s, the infrared image followed by the occurrence of a PIF, (c) t = 342.0 s, the ina t=0.9 s, the infrared image at the beginning of loading, b t=341.7 s, the infrared image followed by the frared image when the coal sample occurrence of a PIF, c t=342.0 s, the infrared image when the coal sample showed a PIF, d t=398.2 s, the infrared showed a PIF, (d) t = 398.2 s, the inimage followed by the occurrence of a PUF, e t=399.0 s, the infrared image when the coal sample showed a PUF, frared image followed by the occurf t=498.3 s, the infrared image followed by the failure of the coal sample, g t=498.5 s, the thermal image when rence of a PUF, (e) t = 399.0 s, the infrared image when the coal sample the coal sample suffered a failure showed a PUF, (f) t = 498.3 s, the infrared image followed by the failure of the coal sample, (g) t = 498.5 s, the thermal image when the coal sample suffered a failure.

is used to denoise the infrared image [27,33], so as to obtain the accurate instantaneous change information of the infrared temperature field, when the coal sample fracture and failure. Denoising original infrared images can show the real-time spatial distribution of the surface IR temperatures of coal samples under loading [13]. However, it is difficult to identify changes in local areas when the temperatures in two adjacent frames of infrared images show a small difference. Successive difference infrared images make it easy to identify local areas characterized by temperature increases or decreases on the surface of coal samples. Original infrared images and successive difference infrared images complement each other, making it possible to understand both the overall and local changes. The areas in the two images characterized by abnormality in temperature changes did not coincide exactly, because the original infrared images showed areas characterized by present abnormality, and the successive difference infrared images showed areas characterized by past abnormality. However, a combination of the two makes it possible to identify the temporal and spatial evolution processes of IR abnormality.

pre-fracture region. However, at that moment, coal sample does not reach the ultimate bearing capacity, while stress is still on the rise and does not reach the failure strength; no failure thus appears in the coal sample (i.e., before the occurrence of disaster), and this condition cannot be regarded as macro-fracture. This fracturing process is actually the precursor of failure. Accordingly, the drastic changes of VOIIT and VSMIT (as shown in Figs. 2 and 5, coal sample A1 and coal sample B4 underwent drastic changes at 121.0 s and 98.8 s, respectively) can be regarded as the precursors of upcoming failure (PUF) for coal sample. Infrared radiation information of the coal sample when failure (macro-fracture) occurred was also monitored, corresponding to the drastic changes of VOIIT and VSMIT in A1 and B4 at 125.4 s and 111.5 s, respectively, as shown in Figs. 2 and 5. Since failure occurred in the coal sample at that moment (i.e., disaster has already occurred), the drastic changes of VOIIT and VSMIT show slight significance to the prediction and early-warning of stress-induced disasters such as rockburst and coalburst. Using the proposed PUF, the warning was overall 30.0 s earlier than the onset of failure, and moreover, temporal and spatial fracture characteristics before coal failure were observed. The present research can provide reference for prediction and early-warning of stress-induced natural disasters including rockburst and coalburst and some human engineering disasters. The fracture precursor of the coal sample generally appears in micro-fracture phase [36]. This study found that, before the plastic deformation stage, some coal samples showed another infrared precursor (similar to the drastic changes of VOIIT and VSMIT of A1 and B4 at 94.4 s and 93.8 s, respectively, as shown in Figs. 2 and 5), namely, precursor of initial failure (PIF), which can also serve as the precursor of fracture. VOIIT and VSMIT changed more significantly than stress, suggesting that PIF is significant and therefore noteworthy. Physically speaking, PIF was a state point. Before PIF, coal samples were predominantly characterized by micro-fracture; after PIF, coal samples were predominantly characterized by crack connection and sudden development of a macro-fracture. However, since coal includes many complex and scattered pores and fissures, it has low ultimate strength than hard rocks such as granite and marble [16]. Additionally, the precursors of fracture may not be as obvious and general as those of failure, and therefore are generally quite complex and more difficult to identified [36]. It can be observed that only 31.0% of coal samples showed a PIF in this experiment. The changes of VOIIT and VSMIT were more significant than that of PIF in stress. Therefore, PIF is significant and deserves further attention.

(5) Application prospects of in-situ monitoring VOIIT and VSMIT are sensitive to changes in stress and therefore, they can be used to identify PIF and PUF, which can then be used to determine the criterion for the issuance of different levels of early warnings of failure. These warnings can be used for the forecasts and early warnings of impending and sudden natural and engineering disasters. VOIIT and VSMIT can be used to detect whether the coal and rock occurs failure under loading and lose the bearing capacity. They can also be used to assess the effectiveness of pillars left in coal mines and evaluate the risks of rock and coal bursting. 5. Conclusions Two indicators of coal and rock failure, VOIIT and VSMIT, have been adopted in this paper. VOIIT and VSMIT were analyzed to discover PIF and PUF and to determine temporal precursors. The original infrared images and successive difference infrared images corresponding to the temporal precursors were analyzed to determine spatial precursors. As a result, complete temporal and spatial information about the precursors of the fractures and failures of coal samples were obtained. This information can be used for the early warnings of disasters caused by coal fractures. The following conclusions were made:

(4) The advatages of successive difference infrared image (1) 79.3% of the coal samples were found to show a PUF, 31.0% of the coal samples were found to show a PIF, and the spatial–temporal precursors of coal fractures and failures were obtained. (2) The time when the coal samples showed a PUF was 30.0 s earlier on total average than the onset of failure. Therefore, PUF can be used for the short-range and impending forecasts of coal fractures. The time when the coal samples showed a PIF was 64.1 s earlier on average than that when the coal samples suffered a failure. Therefore, PIF can be used for the medium-range forecasts of rock

Multiform noise disturbance exist in the infrared detection process inevitable, including the influence of ambient temperature on the measurement results, and the image temperature drift of the un-cooling infrared focal plane array [25,37,38]. These noise signals are often used as so-called infrared anomaly signals to explain the fracture process of coal and rock. The infrared image collected during the experiment has the disadvantage of low signal-to-noise ratio. Therefore, the infrared thermal image is firstly enhanced. On this basis, the filtering technique 152

Infrared Physics and Technology 93 (2018) 144–153

L. Ma, H. Sun

fractures. (3) The coal samples showed a PUF at a stress level of 86.3–98.0% (the total average was 92.7%) of σp, and showed a PIF at a stress level of 60.4–91.9% (79.5% on average) of σp. (4) The PUF amplitude of VOIIT and VSMIT of the coal samples was 106.7 and 281.5 times as large, respectively, as their average amplitude in the loading process. In addition, the two-variance indicators showed a PUF at the same time and for the same number of coal samples.

[14]

[15]

[16]

[17]

The results of this paper can provide a foundation for the monitoring and early warning of engineering and natural disasters (such as pillar collapse, rock bursts, coal bursts and earthquakes) using infrared remote sensing data.

[18]

[19]

Acknowledgments

[20]

This paper was supported by the National Key Basic Research Program of China (973 Program) (grant number 2015CB251600), the State Key Laboratory of Coal Resources and Mine Safety (grant number SKLCRSM13X03), the Open Project of Key Laboratory of Mine Geological Hazards Mechanism and Control (grant number KF2017-02) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

[21]

[22]

[23]

Conflict of interest

[24]

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

[25]

References

[26] [27]

[1] X.M. Wang, Z.M. Zhu, M. Wang, P. Ying, L. Zhou, Y.Q. Dong, Study of rock dynamic fracture toughness by using VB-SCSC specimens under medium-low speed impacts, Eng. Fract. Mech. 181 (2017). [2] X.P. Lai, M.F. Cai, F.H. Ren, M.W. Xie, T. Esaki, Assessment of rock mass characteristics and the excavation disturbed zone in the Lingxin Coal Mine beneath the Xitian river, China, Int. J. Rock Mech. Min. Sci. 43 (4) (2006) 572–581, https://doi. org/10.1016/j.ijrmms.2005.10.005. [3] G.O. Cress, B.T. Brady, G.A. Rowell, Sources of electromagnetic radiation from fracture of rock samples in the laboratory, Geophys. Res. Lett. 14 (4) (1987) 331–334, https://doi.org/10.1029/GL014i004p00331. [4] S. Yoshida, T. Ogawa, Electromagnetic emissions from dry and wet granite associated with acoustic emissions, J. Geophys. Res. Solid Earth 109 (2004) (B9) 271–297. 10.1029/2004JB003092. [5] V.V. Kormiltsev, A.N. Ratushnyak, V.A. Shapiro, Three-dimensional modeling of electric and magnetic fields induced by the fluid flow movement in porous media, Phys. Earth Planet. Inter. 105 (105) (1998) 109–118, https://doi.org/10.1016/ S0031-9201(97)00116-7. [6] Y. Rapoport, M. Hayakawa, V. Ivchenko, D. Juarez-R., S. Koshevaya, O. Gotynyan, Change of ionospheric plasma parameters under influence of electric field of the lithospheric origin and of a radon emanation, EGS-AGU-EUG Joint Assembly, 2003. doi:10.1016/j.pce.2003.09.018. [7] A. Takeuchi, H. Nagahama, T. Hashimoto, Surface electrification of rocks and charge trapping centers, Phys. Chem. Earth. 29 (4) (2004) 359–366, https://doi. org/10.1016/j.pce.2003.09.016. [8] P.A. Varotsos, V. Hadjicontis, A.S. Nowick, The physical mechanism of seismic electric signals, Acta Geophys. Polon. 49 (4) (2001) 415–421. [9] A. Rabinovitch, V. Frid, D. Bahat, Surface oscillations — a possible source of fracture induced electromagnetic radiation, Tectonophysics 431 (1) (2007) 15–21, https://doi.org/10.1016/j.tecto.2006.05.027. [10] A. Carpinteri, G. Lacidogna, A. Manuello, G. Niccolini, A. Schiavi, A. Agosto, Mechanical and electromagnetic emissions related to stress-induced cracks, Exp. Tech. 36 (3) (2012) 53–64, https://doi.org/10.1111/j.1747-1567.2011.00709.x. [11] F. Freund, On the electrical conductivity structure of the stable continental crust, J. Geodyn. 35 (3) (2003) 353–388, https://doi.org/10.1016/S0264-3707(02) 00154-0. [12] M.P. Luong, Infrared thermovision of damage processes in concrete and rock, Eng. Fract. Mech. 35 (1) (1990) 291–301, https://doi.org/10.1016/0013-7944(90) 90207-W. [13] M.C. He, W.L. Gong, D.J. Li, H.M. Zhai, Physical modeling of failure process of the

[28]

[29]

[30]

[31] [32]

[33]

[34]

[35]

[36]

[37]

[38]

153

excavation in horizontal strata based on IR thermography, Int. J. Min. Sci. Technol. 19 (6) (2009) 689–698, https://doi.org/10.1016/S1674-5264(09)60128-9. F.T. Freund, A. Takeuchi, B.W.S. Lau, A. Al-Manaseer, C.C. Fu, N.A. Bryant, D. Ouzounov, Stimulated infrared emission from rocks: assessing a stress indicator, Earth 2 (1) (2007) 7–16, https://doi.org/10.5194/ee-2-7-2007. V.I. Sheinin, D.I. Blokhin, Features of thermomechanical effects in rock salt samples under uniaxial compression, J. Min. Sci. 48 (1) (2012) 39–45, https://doi.org/10. 1134/S1062739148010054. L.X. Wu, J.Z. Wang, Infrared radiation features of coal and rocks under loading, Int. J. Rock Mech. Min. Sci. 35 (7) (1998) 969–976, https://doi.org/10.1016/S01489062(98)00007-2. L.X. Wu, N.G. Geng, J.H. Wang, C.Y. Cui, Remote sensing rock mechanics (RSRM) and associated experimental studies, Int. J. Rock Mech. Min. Sci. 37 (6) (2000) 879–888, https://doi.org/10.1016/S1365-1609(99)00066-0. L.X. Wu, S.J. Liu, Y.H. Wu, J.Z. Wang, From qualitative to quantitative information: the development of remote sensing rock mechanics (RSRM), Int. J. Rock Mech. Min. Sci. 41 (2004) 310–316, https://doi.org/10.1016/j.ijrmms.2003.12.011. L.X. Wu, S.J. Liu, Y.H. Wu, C.Y. Wang, Precursors for rock fracturing and failure—Part I: IRR image abnormalities, Int. J. Rock Mech. Min. Sci. 43 (3) (2006) 473–482, https://doi.org/10.1016/j.ijrmms.2005.09.002. S.J. Liu, L.X. Wu, Y.B. Zhang, Temporal-spatial evolution features of infrared thermal images before rock failure, J. Northeast. Univ. 30 (7) (2009) 1034–1038 (in Chinese). X.P. Lai, H. Sun, P.F. Shan, C. Wang, N. Cui, Y. Yang, Acoustic emission and temperature variation in failure process of hard rock pillars sandwiched between thick coal seams of extremely steep, Chin. J. Rock Mech. Eng. 34 (11) (2015) 2285–2292, https://doi.org/10.13722/j.cnki.jrme.2015.1047 in Chinese. L.X. Wu, S.J. Liu, Y.H. Wu, C.Y. Wang, Precursors for rock fracturing and failure—Part II: IRR T-Curve abnormalities, Int. J. Rock Mech. Min. Sci. 43 (3) (2006) 483–493, https://doi.org/10.1016/j.ijrmms.2005.09.001. Y.X. Zhao, Y.D. Jiang, Acoustic emission and thermal infrared precursors associated with bump-prone coal failure, Int. J. Coal Geol. 83 (1) (2010) 11–20, https://doi. org/10.1016/j.coal.2010.04.001. S.J. Liu, J.L. Wei, J.W. Huang, L.X. Wang, Y.B. Zhang, B.Z. Tian, Quantitative analysis methods of infrared radiation temperature field variation in rock loading process, Chin. J. Rock Mech. Eng. 34 (2015) 2968–2976, https://doi.org/10. 13722/j.cnki.jrme.2014.0656 (in Chinese). W.L. Gong, Y.Y. Peng, X.M. Sun, M.C. He, S.Y. Zhao, H.Q. Chen, T. Xie, Enhancement of low-contrast thermograms for detecting the stressed tunnel in horizontally stratified rocks, Int. J. Rock Mech. Min. Sci. 74 (2015) 69–80, https:// doi.org/10.1016/j.ijrmms.2014.12.002. S.J. Liu, L.X. Wu, Y.H. Wu, Infrared radiation of rock at failure, Int. J. Rock Mech. Min. Sci. 43 (6) (2006) 972–979, https://doi.org/10.1016/j.ijrmms.2005.12.009. M.C. He, X.N. Jia, W.L. Gong, L. Faramarzi, Physical modeling of an underground roadway excavation in vertically stratified rock using infrared thermography, Int. J. Rock Mech. Min. Sci. 47 (7) (2010) 1212–1221, https://doi.org/10.1016/j.ijrmms. 2010.06.020. L.Q. Ma, Q.Q. Li, X.Q. Cao, T. Zhou, Variation characteristics of internal infrared radiation temperature of coal-rock mass in compression process, J. China Univ. Min. Technol. 42 (3) (2013) 331–336 (in Chinese). S. Offermann, J.L. Beaudoin, C. Bissieux, H. Frick, Thermoelastic stress analysis under nonadiabatic conditions, Exp. Mech. 37 (4) (1997) 409–413, https://doi.org/ 10.1007/BF02317306. J.A. Hudson, S.L. Crouch, C. Fairhurst, Soft, stiff and servo-controlled testing machines: a review with reference to rock failure, Eng. Geol. 6 (3) (1972) 155–189, https://doi.org/10.1016/0013-7952(72)90001-4. A. Carpinteri, A catastrophe theory approach to fracture mechanics, Int. J. Fract. 44 (1) (1990) 57–69, https://doi.org/10.1007/BF00012552. L.Q. Ma, S.K. Wang, D.S. Zhang, N. Adeleke, H. Sun, Y. Zhang, W. Liu, Infrared radiation noise characteristics and its denoising method during coal uniaxial compression loading test, J. Min. Saf. Eng. 34 (1) (2017) 114–120, https://doi.org/ 10.13545/j.cnki.jmse.2017.01.018 (in Chinese). H. Sun, L.Q. Ma, N. Adeleke, Y. Zhang, Background thermal noise correction methodology for average infrared radiation temperature of coal under uniaxial loading, Infrared Phys. Technol. 81 (2017), https://doi.org/10.1016/j.infrared. 2017.01.001. L.Q. Ma, Y. Zhang, H. Sun, S.K. Wang, A. Najeem, Experimental study on dependence of infrared radiation on stress for coal fracturing process, J. China Coal Soc. 42 (1) (2017) 140–147, https://doi.org/10.13225/j.cnki.jccs.2016.5003 (in Chinese). L.Q. Ma, H. Sun, Y. Zhang, T. Zhou, K. Li, J.S. Guo, Characteristics of infrared radiation of coal specimens under uniaxial loading, Rock Mech. Rock Eng. 49 (4) (2016) 1567–1572, https://doi.org/10.1007/s00603-015-0780-4. Z.Y. Xu, B. Wang, Y.L. Hu, R.H. Yang, A discussion on the influence of the difference between the rock fracture and failure to the earthquake simulation and the precursor studies, Chin. J. Geophys. 52 (3) (2009) 712–719 (in Chinese). J.W. Huang, S.J. Liu, Z.Y. Xu, C.L. Ma, L.X. Wu, Experimental study on the influence of the radiation background on the variation in thermal infrared radiance of loaded rock, Spectroscopy 38 (1) (2018) 49–53 (in Chinese). J.W. Huang, S.J. Liu, X. Gao, Z.C. Yang, Q. Ni, L.X. Wu, Experimental study of the thermal infrared emissivity variation of loaded rock and its significance, Remote Sens. 10 (6) (2018) 818, https://doi.org/10.3390/rs10060818.