Study on Damage Rupture and Crack Evolution Law of Coal Samples Under the Influence of Water Immersion Pressure

Abstract

Underground reservoir technology in coal mines enables the effective storage and utilization of water resources disturbed by mining activities. Owing to the effects of mining operations and water extraction/injection activities, the water head in underground reservoirs fluctuates dynamically. The total bearing capacity of a coal pillar dam is significantly reduced due to the combined effects of overlying rock stress, dynamic and static water pressures, and mining-induced stresses, which are critical for ensuring the safe operation of underground reservoirs. Based on the correlation between different water head heights and the corresponding water pressures on the coal pillar dam, a custom-made coal rock pressure water immersion test device was used to saturate the coal samples under various water pressure conditions. The mechanical deformation and failure characteristics of the samples and fracture propagation patterns under different water pressure conditions were studied using uniaxial compression, acoustic emission (AE), and three-dimensional X-ray microimaging. The results indicated that, compared with the dry state, the peak strain of the water-immersed coal samples increased to varying degrees with increasing water pressure. Additionally, the average porosity and the number of pores with diameters in the range of 0 to 150 μm significantly increased in water-immersed coal samples. Under the combined influence of water immersion pressure and uniaxial stress, loading the water-saturated coal samples to the fracture damage threshold significantly intensified deformation, failure, and fracture propagation within the samples, and the failure mode changed from tension to a composite tensile–shear failure.

1. Introduction

The western mining regions, which constitute the primary coal production bases in China, contain over 80% of the country’s coal reserves and contribute to more than 70% of its total production. However, these regions are constrained by factors, such as climate and precipitation, resulting in water resource shortages and fragile, easily disturbed ecological environments [1,2,3,4]. Coal mining leads to the development of extensive mining-induced fractures in the overlying strata, allowing ground water and surface water to flow into the goaf and form mine water. Each ton of coal mined generates approximately two tons of mine water, with a comprehensive utilization rate of only about 35% [5]. To mitigate underground water hazards, significant volumes of mine water are discharged to the surface, leading to soil salinization and intensifying the conflict between coal mining activities and regional water resource protection efforts.

To effectively protect and utilize mine water resources, scholars have explored water-preserving mining technologies based on the “blocking method” and underground reservoir water resource protection technologies based on the “drainage method”. The “blocking method” employs techniques such as height-limited mining, backfill mining, and zonal mining to prevent the connection between fractures in the overlying strata and aquifers during coal extraction, thereby preserving groundwater resources in situ [6,7,8]. The “drainage method” focuses on initially purifying mine water through gangue adsorption and other means and safely storing it in underground reservoirs for the comprehensive underground treatment and utilization of mine water [9,10]. Underground coal reservoirs are the main technical means for protecting and utilizing mining-induced water resources and for meeting the living, industrial, and ecological needs of mining areas [11,12]. For instance, they provide a new water source for nearby power companies and coal refining operations, transforming groundwater from a hazard to a resource. When storage capacity and safety requirements are met, these systems can also be designed as Pumped Hydro Energy Storage (PHES) or Underground Pumped Hydro Energy Storage (UPHES) systems [13,14]. PHES is currently the largest available form of large-scale energy storage, storing electricity in the form of water potential energy, which can be converted back to electricity during peak demand periods [15,16]. This capability allows for the storage of intermittently supplied energy, contributing to the growing demand for sustainable energy sources.

Underground coal mine reservoirs primarily store mine water within the goaf, and the stability of the coal pillar dams is a crucial factor affecting the storage function and safety of the goaf. Over time, this stability gradually diminishes because of the coupled effects of the overlying rock self-weight stress, mine water pressure, and gangue disturbance stress [17,18,19,20]. The continuously fluctuating reservoir water level subjects the coal pillar to the corresponding dynamic water erosion pressure, which also significantly reduces the solidity and load-bearing capacity of the coal pillar [21,22]. Yao et al. [23] utilized X-ray diffraction and nondestructive water immersion tests to analyze the composition and water absorption mechanisms of coal. Increasing moisture content reduced the compressive strength and elastic modulus of coal by 50.3% and 42.4%, respectively. Conversely, rising loading rates initially increased these properties, with maximum enhancements of 74.2% and 82.5%, before subsequent declines. At low moisture content and loading rates, coal samples exhibited pronounced brittleness, with the failure mode shifting from tensile to shear.

The impact of water on the physical and mechanical properties of rocks has been extensively investigated in laboratory experiments, with a particular emphasis on the relationship between physical and mechanical parameters and water content conditions [24,25,26]. The influence of water on the mechanical properties of coal rock is typically detrimental, with the degree of weakening potentially reaching up to 90% [27]. Researchers [28,29,30] have studied the effects of moisture content on the compressive and tensile properties of rocks, finding that tensile strength is significantly more affected by moisture content than compressive strength. These studies have demonstrated that the mechanical parameters of rocks are mostly subjected to varying degrees of reduction under the influence of water.

In the context of underground reservoirs and pumped storage applications, coal pillars are commonly employed as dam structures in engineering practice. However, studies on the microstructural changes in coal samples and their response to water-related environmental conditions remain limited. The continuous fluctuation of reservoir water levels—caused by the replenishment of fissure water and the drainage or storage of mine water—leads to dynamic changes in water erosion pressure on coal pillars, significantly reducing their strength and load-bearing capacity [31]. Ding et al. [32] examined the deterioration of the tensile properties of coal samples subjected to varying water immersion pressures and durations. Their study revealed that prolonged immersion times led to significant reductions in peak strength, increased plastic deformation, compromised end-surface integrity, higher fracture location entropy, and increases in porosity, average pore diameter, and median pore diameter. Similarly, Wang et al. [33] conducted ultrasonic longitudinal wave velocity measurements on coal samples with different water contents, densities, and pore diameters, finding that the longitudinal wave velocity gradually increased as the water content of the coal samples rose and the wave speed increased more rapidly in type II coal with high water saturation due to its higher microporosity and correlated positively with porosity and fractal dimension.

Therefore, investigating the damage and fracture evolution of coal samples under varying water pressure conditions is crucial for comprehending the stability and failure mechanisms of water storage structures within goafs.

In light of this, and building upon previous research, an independently developed coal rock pressure water immersion test device was utilized to pretreat the coal samples. Through uniaxial compression testing, acoustic emission (AE) analysis, and three-dimensional (3D) X-ray micro-imaging, the structural deterioration process and the macro- and micro-mechanisms driving the mechanical properties of coal samples under the action of water immersion at different pressures were investigated to provide an important scientific basis for an in-depth understanding of the stability of the water storage structure in the air-mining zone.

2. Materials and Methods

2.1. Materials

The coal samples used in the experiments were sourced from the Ningdong mining area in western China. All samples were extracted from the same coal block and processed into cylindrical specimens with a diameter of 50 mm and a height of 100 mm. After mass and wave velocity testing, samples with regular shapes, intact structures, and similar physical properties were selected to minimize the impact of rock heterogeneity on the experimental results. Eleven samples were ultimately selected and labeled as #1, #2, …, #19. The sampling location and coal samples are shown in Figure 1.

Before the experiment, the mass and wave velocity of the coal were determined to eliminate errors introduced by the heterogeneity of the coal samples and to ensure the consistency and reliability of the experimental results. The results shown in

Table 1. Physical properties of the coal samples used in this study.

Rock Type	Sample Mass		Seismic Velocity
	Average (g)	Discrete Degree	Average (m/s)	Discrete Degree
Coal	267.91	0.02168	1450.53	0.02185

and Figure 2 indicate that the properties of the coal samples were relatively consistent, with low variability, making them suitable for subsequent experimental research.

2.2. Methods

This study involved two main experiments: uniaxial compression and water immersion tests on the coal samples. The experimental steps were as follows:

(1)

Drying: The coal samples were dried in an electric air-drying oven, heating under a constant temperature of 110 °C for 8 h. To ensure the complete evaporation of free water without the decomposition of crystal water, a drying time of 8 h was selected, which greatly exceeded the maximum duration specified in GB/T 212-2008 [34] for moisture content determination in coal (2 h for lignite). This extended drying period ensured the complete volatilization of free water.

(2)

Cooling and sealing: The coal samples were cooled to room temperature and sealed with plastic wrap to prevent reabsorption of moisture from the air.

(3)

Immersion: Samples #1–12 were placed under dry conditions and water pressures of 0 MPa, 0.3 MPa, and 0.5 MPa, respectively, until the immersed samples were fully saturated. The corresponding groundwater head levels were 0, 30, and 50 m based on the following water pressure formula: P = ρgh, where P refers to the water pressure, Pa; ρ is the density of the flowing water (approximately 1 g/cm³); g is typically set to 10 N/kg; and h represents the groundwater head level in the underground reservoir, m.

(4)

Uniaxial compression tests: Uniaxial compression tests were conducted on the samples, and the stress thresholds of the samples under different water pressures were calculated using the stress–strain curve.

(5)

Computed tomography (CT) scan (Part 1): Step 3 was repeated for samples #13, #14, and #15, and then CT scans were performed on the coal samples.

(6)

CT scan (Part 2): Step 3 was repeated for samples #16, #17, #18, and #19. Subsequently, uniaxial compression loading was performed on the fracture damage stress threshold determined in Step 4, and CT scans were performed on the coal samples before and after loading, and an AE experiment was conducted on the coal samples during loading.

The p values for samples #1–19 are listed in

Table 2. Coal samples treated under different immersion pressures.

	Samples	Air-Dried	P = 0 MPa	P = 0.3 MPa	P = 0.5 MPa
Experiment
Study on failure mode of coal sample		#1–3	#4–6	#7–9	#10–12
Immersion pressure test			#13	#14	#15
Study on fracture distribution and evolution		#16	#17	#18	#19

.

The process of super-resolution reconstruction for CT images based on Generative Adversarial Networks (GANs) aims to establish a mapping between high- and low-resolution images, thereby achieving high-resolution image reconstruction. The steps for model construction are as follows:

(1)

Data Collection: High-resolution images are obtained for model training.

(2)

Preprocessing: Images are cropped to 128 × 128 pixels to reduce computational resource consumption.

(3)

Downsampling: Low-resolution images are generated using the Bicubic method to simulate image degradation.

(4)

Generator Construction: A U-Net architecture is employed, incorporating residual connections and multi-scale convolutional kernels, as shown in Figure 3.

(5)

Discriminator Construction: Similarly, U-Net is used with added residual connections and frequency normalization, as shown in Figure 4.

(6)

Initial Training of Generator: The generator is optimized for Peak Signal-to-Noise Ratio (PSNR) using the L1 loss function.

(7)

Adversarial Training: The model is trained with a combination of L1 loss, perceptual loss, and GAN loss to improve image quality and realism.

(8)

Parameter Tuning: The model is optimized based on training results.

(9)

Performance Testing: The model is evaluated on the test set using PSNR and Structural Similarity Index (SSIM) metrics.

The model uses the L1 Loss (MAE Loss), which exhibits robust performance against outliers, to compute the generator’s loss. The loss is calculated as follows:

$$\mathrm{L}1 \mathrm{Loss}=MAE={\displaystyle \frac{1}{M}} {\sum }_{i=1}^m| y_i-\widehat{y} |,$$

(1)

where MSE refers to the mean square error between the original image and the reconstructed image.

PSNR and SSIM are used as key metrics to evaluate the quality of the reconstructed images. PSNR is defined by comparing the maximum possible power of the original signal to the noise power that affects the precision of signal representation. A higher PSNR indicates better image quality and less distortion. SSIM, on the other hand, considers the luminance, contrast, and structural information of the image, where a value closer to 1 indicates a better reconstruction effect.

$$MSE={\sum }_i^H{\sum }_{j=0}^W{\displaystyle \frac{[ \widehat{f} (i, j)-f(i, j){ ]}^2}{H\times W}},$$

(2)

$$\mathrm{PSNR}=10{\log }_{10}({\displaystyle \frac{{MAX}_i^2}{MSE}}),$$

(3)

and

$$\mathrm{SSIM}(x, y)={\displaystyle \frac{(2{\mu }_x{\mu }_y+C_1\left)\right(2{\sigma }_{xy}+C_2)}{({\mu }_x^2+{\mu }_y^2+C_1\left)\right({\sigma }_x^2+{\sigma }_y^2+C_2)}},$$

(4)

where MSE represents the mean square error between the original and reconstructed images. MAX refers to the maximum grayscale value of the image, typically 255 for 8-bit images. H and W represent the height and width of the image in pixels. μ_i and σ_i² are the mean and variance of the image, where i ∈ {x, y}. σ_xy denotes the covariance between images x and y. C_i are constants introduced to ensure the stability of the calculation and to prevent division by zero. C_i can be calculated as C₁ = (K₁L)² and C₂ = (K₂L)², where L is the largest possible pixel value of the image, and K₁ and K₂ are small constants, typically 0.01 and 0.03, respectively. For 8-bit images, L is usually 255, which means that C₁ is typically set to 6.5025 and C₂ is set to 58.5225.

For the CT scans, the two-dimensional slices obtained were reconstructed with super-resolution, and noise was removed using a filtering method. The results of CT image processing are shown in Figure 5. Then, using Avizo software (Avizo 2021.2), the Otsu method was used to determine the critical value for pore segmentation, which was applied to the super-resolution reconstruction. Finally, using multi-region of interest analysis and grid processing methods, a 3D model of the microscale pore structure of the coal rock was reconstructed. Figure 6. shows the flow of CT image processing and 3D reconstruction.

The ball-and-stick model is a space-filling model used to represent the three-dimensional structure of chemical molecules. The model simplifies pores using the maximum sphere method and extracts key parameters for both pores and throats. To simplify the topological structure of the pore space, a morphological refinement algorithm was used to extract the central axis of the pore space, which was then used to construct an equivalent pore network model. In this model, pores are represented by spheres and throats by tubes, thus simplifying the description of the pore–space connectivity.

2.3. Experimental Apparatus

The main experimental apparatus and the diagrams illustrating these principles are shown in Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11.

The equipment primarily consists of a sealed chamber, an air pressure pump, a water storage tank, a regulating water tank, a pressure gauge, and other components. These parts are interconnected through an internal pipeline system, enabling the setup of various water immersion conditions for coal-bearing sedimentary rock samples, including pressurization, long-term fixed or variable water levels, and cyclic flushing. The system provides a submersion pressure range of 0 to 0.5 MPa, with a control accuracy of 0.01 MPa, fully meeting the experimental requirements.

During the experiment, the equipment continuously applied pressure to the purified water inside the sealed chamber using an air pressure pump. The pressure was automatically adjusted and maintained at a constant level within the chamber through the use of a pressure gauge and a pressure valve.

The water content of the rock samples is calculated using the following formula:

$$w_t={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n({\displaystyle \frac{m_{it} -m_{i0}}{{\mathrm{m}}_{i0}}} \times 100\%),$$

(5)

where w_t represents the average water content (%) of the rock samples at time t during water immersion, m_i0 denotes the mass of the i-th rock sample in its dry state (g), and m_it is the mass of the i-th rock sample after immersion for time t (g). n is the number of repeated experiments under the same conditions, which is set to 1 in this case.

During the coal sample’s water infiltration process, the water content w and time t followed a negative exponential function [31]. The sample was placed inside the sealed chamber and submerged in water. After soaking for 10 h, the sample was periodically removed every 20 min, lightly wiped to remove surface water droplets, and weighed on an electronic scale. The process was repeated until the relative error between two consecutive measurements was less than 0.5%, at which point the sample was considered water-saturated.

Vernier calipers, a DHG-9140A electrically heated constant-temperature drying oven (The manufacturer is Shanghai Jinghong Experimental Equipment Co., Ltd., made in Shanghai, China), a coal rock pressure water immersion device, an electronic balance, an MTS835 electrohydraulic servo testing machine (The model number is C64.106. The manufacturer is Metus Industrial Systems (China) Co., Ltd., made in the Eden Prairie, MN, USA), and a high-resolution X-ray machine were used in the experiments.

The MTS835 electrohydraulic servo testing machine can apply an axial load of approximately 1000 kN. The sampling frequency and test accuracy were 1000 Hz and ±0.5%. The experiment was performed using a displacement control method at a loading rate of 0.4 mm/min.

The high-resolution X-ray machine is suitable for samples ranging from 2 to 50 mm (in the direction of the light), with a scanning accuracy of 1/1000 and a grayscale level of 0 to 255 for recognizable images.

A PCI-2 system was used to acquire AE signals during the loading process. Four AE sensors (The model number is NANA-30, manufactured by Physical Acoustics Corporation, manufactured in the West Windsor Township, NJ, USA) were attached to the surface of each specimen using hot-melt adhesive. The sensors were positioned 25 mm from the top and bottom ends of the specimen to form a three-dimensional spatial distribution. The primary technical parameters are listed in

Table 3. Main technical parameters of acoustic-emission probe.

Resonant Frequency (MHz)	Threshold Value (dB)	Sampling Frequency (MHz)	Peak Detection Time (s)	Hardware Trigger Delay (s)
140	40	1.0	50	200

.

3. Experimental Results and Analysis

3.1. Mechanical Properties of Coal Samples

Figure 12 shows the stress–strain curves obtained from the uniaxial compression tests on samples #1–#12 under different water immersion pressures. Compared to the dry coal samples, the compressive strength of all the water-immersed coal samples decreased significantly, with a similar reduction magnitude. However, as the water immersion pressure increased, the reduction in compressive strength decreased slightly.

Figure 13 shows that coal samples under different water immersion pressures exhibited similar relationships between stress and water content and

Table 4. Mechanical parameters of coal samples under different immersion pressures.

Samples	Compressive Strength (MPa)	Peak Strain	Elasticity Modulus (GPa)
#1	30.54	0.0198	2.01
#2	30.52	0.0179	1.98
#3	28.87	0.0207	2.12
#4	20.53	0.0219	1.19
#5	17.64	0.0189	1.32
#6	20.49	0.0229	1.43
#7	16.28	0.0242	0.67
#8	16.19	0.0237	0.72
#9	14.98	0.0268	0.63
#10	11.62	0.0285	0.56
#11	11.28	0.0304	0.43
#12	12.98	0.0276	0.76

shows the mechanical parameters of coal samples under different immersion pressures. As the immersion pressure increased, the peak strength of the coal samples decreased following a negative exponential trend. Curve fitting for all measurement groups showed a high degree of accuracy, with R² values exceeding 0.9. From the air-dried state to saturation under various immersion pressures, the average peak strength decreased from 29.98 to 11.96 MPa, representing a reduction of 57.4%.

The elastic modulus E and Poisson’s ratio μ of the rock samples were calculated using the “moving point regression” method. The characteristic stress values of the coal samples were derived using the fissure volume–strain equation. The results presented in

Table 5. Stress characteristics of coal samples.

Samples	E/GPa	μ	Stress Characteristic Values (MPa)				Stress Ratio
			σcc	σci	σcd	σf	σcc/σf	σci/σf	σcd/σf
#1	2.01	0.24	4.89	15.41	21.57	30.54	0.16	0.51	0.71
#4	1.19	0.21	3.95	10.23	14.83	20.54	0.19	0.49	0.72
#7	0.67	0.15	2.32	5.19	12.26	16.41	0.14	0.31	0.74
#10	0.56	0.13	1.87	4.13	7.96	12.98	0.14	0.32	0.61

demonstrate a pronounced negative exponential relationship between the water immersion pressure and the characteristic stress thresholds of the coal samples.

The elastic modulus E and Poisson’s ratio μ of the rock samples were calculated using the “moving point regression” method. The characteristic stress values of the coal samples were derived using the fissure volume–strain equation.

The characteristic stress thresholds of the coal samples demonstrated substantial variations at each stage during the transition from a dry to a saturated state, exhibiting a progressive decline with increasing water pressure. This observed trend is primarily due to the augmentation of internal water content within the coal samples as a result of intensified water immersion pressure. The area of the coal samples affected by the physicochemical action of water also expanded, weakening the adhesion between the coal particles and reducing the friction coefficient. This reduction in the internal structural bearing capacity of the coal changes its mechanical properties, leading to a decline in strength and making the coal samples more prone to crack propagation and penetration under lower-stress conditions.

The release of AE signals evolved over time, providing precise information on the intensity and types of individual acoustic emissions. The degree of fracture damage in the coal and rock samples was analyzed using an AE model based on wavelet transformation.

The variations in the energy and cumulative energy of the coal samples from different groups during the loading process are shown in Figure 14; the AE characteristics of the coal samples exhibited significant changes with increasing immersion pressure, and the evolution of the AE characteristics tended to follow a consistent pattern across all samples. The experimental results indicated that, during the initial loading stage, the AE energy of the coal samples fluctuated only slightly and then increased slowly, exhibiting stable behavior. This was primarily owing to the relatively small amount of energy released during compression and elastic deformation, which resulted in weak AE signals and slow energy accumulation. In the middle stage, as plastic deformation began, both the release and accumulation of energy increased; however, the magnitude remained modest. Upon reaching the maximum stress state, microcracks in the rock mass rapidly propagated and coalesced, leading to the rapid release of the previously accumulated energy and causing both the energy and cumulative energy curves to increase sharply. Throughout the loading process, the internal structures of the coal samples underwent significant changes, which markedly affected their AE behavior and energy release patterns.

Table 6. Cumulative total amount of peak stress energy.

Samples	#16	#17	#18	#19
Accumulated energy (104aj)	153.74	90.52	35.91	27.25

shows the change of the total cumulative amount of peak stress energy. Compared with the dry coal samples, the cumulative energy of the water-immersed coal samples decreased, and the rate of this reduction slowed as the immersion pressure increased. This occurred because the immersion pressure directly affected the moisture content of the coal samples, thereby influencing their strength. At higher immersion pressures, the increased moisture content rendered the coal samples more susceptible to structural slippage and crack propagation under equivalent stress conditions, thereby accelerating the development of fractures. Furthermore, the enhanced connectivity of pores and cracks within the coal samples reduced their overall strength, making them more prone to instability and fracture, thereby shortening the time available for energy accumulation.

The formation and propagation of macroscopic cracks are critical factors leading to the overall failure of coal–rock structures. Using AE technology to capture microfracture events within a material, combined with the RA-AF evaluation system, allows for the effective identification of fracture types and their evolutionary processes. Generally, lower RA and higher AF values are associated with tensile fractures, whereas higher RA and lower AF values typically indicate shear fractures.

The definitions and calculation processes for the RA and AF values are detailed in Equations (5)–(9), respectively.

$$\mathrm{R}\mathrm{A}={\displaystyle \frac{RiseTime\left(RT\right)}{Amplitude\left(Amp\right)}},$$

(6)

$$A(\mathrm{dB})=20\times \lg {\displaystyle \frac{V}{V_{ref}}} -G,$$

(7)

$$V={ V}_{ref}\times 10{\displaystyle \frac{G+A\left(\mathrm{dB}\right)}{20}},$$

(8)

$$\mathrm{AF}={\displaystyle \frac{RingCount\left(\mathrm{AE}\right)}{Duration\left(\mathrm{us}\right)}},$$

(9)

and

$$k={\displaystyle \frac{{\mathrm{AF}}_{max}}{{\mathrm{RA}}_{max}}}$$

(10)

where RT represents the rise time of the AE waveform in microseconds (μs) and Amp refers to the amplitude of the waveform. When the amplitude is provided on a logarithmic scale (dB), it must be converted into its original voltage value (V). This conversion involves the AE amplification gain (G), which represents the degree of amplification applied to the AE signal before it is recorded and was set to 40 dB in this experiment. V_ref is the reference voltage used for calibration in the AE software (PCI-2 AE System software by default, PCI-2 AE System web site is https://www.physicalacoustics.com/by-product/pci-2/, accessed on 27 November 2024), which was set to 1 μV in this experiment.

Probability density distribution curves of the RA-AF parameters under different immersion pressures were plotted using MATLAB R2022a, with different colors representing the density of data points; red indicates the highest density and blue the lowest. The white dashed line denotes the shear failure boundary, which was characterized by slope k. The maximum AF/RA ratio (k) was used as the critical value for evaluating the tensile-to-shear failure transitions of the coal samples. AE events with an AF/RA ratio greater than k were considered tensile failures, whereas those with a ratio less than k were identified as shear failures.

Figure 15 illustrates the probability density curves of the RA-AF parameters for the coal samples under different water pressure conditions. To highlight the differences in data density coloration more clearly, a yellow dashed envelope line was drawn at a data density level of 6. From the density coloration in the RA-AF plots and the relative position of the data points to the tension–shear boundary, it was observed that the RA-AF characteristic values of the dry coal samples were predominantly located above the boundary, indicating that tensile failure dominated under dry conditions. As the immersion pressure increased, the concentrated region of the data and envelope line progressively shifted toward the lower-right side of the boundary, suggesting that the immersion pressure facilitated the formation of more tensile–shear composite fractures, leading to the tensile–shear composite failure of the coal samples.

The increase in immersion time and pressure increased the moisture content of the coal samples, thereby enhancing the effective porosity, pore connectivity, and pore complexity within the coal mass. Under the influence of immersion pressure, the complex pore distribution in the coal further exacerbated the formation of internal defects. These randomly connected internal defects provided favorable conditions for the formation of shear fractures, promoting the tensile–shear composite failure mode of the coal samples.

3.2. Distribution and Evolution of Cracks in Coal Samples

Figure 16 presents the pore parameter distribution maps of samples #13, #14, and #15 under different water immersion pressures, obtained through 3D visualization of the microstructure using a ball-and-stick equivalent network model. Most pores in the coal samples had diameters less than 10 µm, and the number of pores increased exponentially as the diameter decreased, indicating that smaller diameter pores were more prevalent. Specifically, pores with diameters greater than 10 µm usually numbered no more than 20, but their quantity significantly increased with higher water immersion pressures.

Additionally, the pore volumes mainly ranged between 0 and 20 × 10³ μm³, and as water immersion pressure increased, the number of newly formed pores exceeding 20 × 10³ μm³ also significantly increased. Correspondingly, the average porosity increased from 0.4% to 2.9% with increasing water pressure.

Figure 17, Figure 18 and Figure 19 show the pore parameter distribution maps of samples #16, #17, #18, and #19 after uniaxial compression to the stress threshold under different water immersion pressures, obtained through 3D visualization of the microstructure using a ball-and-stick equivalent network model. After loading, the porosity of the coal samples increased from 7.1% to 22.5%.

As shown in Figure 17, the isolated pores exhibit relatively small changes before and after water saturation due to the difficulty of exchanging substances with the external environment [35,36,37]. After the water pressure increases from 0 MPa to 0.3 MPa post-saturation, the number of connected pores and fractures increases significantly. However, as the pressure further rises to 0.5 MPa, the growth in the number of pores and fractures continues but slows down.

As shown in Figure 18 and Figure 19, with the increase in water immersion pressure, the number of pores with diameters in the range of 0–150 µm in the loaded coal samples showed a significant increase. Additionally, the number of newly formed pores with diameters exceeding 150 µm also grew markedly. The pore volume of the coal samples primarily ranged between 0 and 10 × 10³ μm³, and after loading, the proportion of pores with volumes greater than 10 × 10³ μm³ increased. When the water pressure reached 0.3–0.5 MPa, the pore volume in the coal samples before and after loading was mainly distributed in the range of 0–25 × 10³ μm³. With the stepwise increase in water pressure, the growth of pore volume in the saturated coal samples was observed to be 7.35%, 12.51%, 17.13%, and 18.49%. This indicates that the primary small pores and fractures in the coal samples are quickly filled and deformed under lower water pressures. As the water pressure and moisture content of the coal samples increase, although the pore volume continues to rise, the growth trend gradually slows down.

As the water immersion pressure increased, the pore structures of the coal samples underwent significant changes. The untreated samples primarily exhibited millimeter-scale fissures with scattered micron-scale isolated pores in some areas. The fissure network was primarily composed of nearly horizontal end cleats that systematically divided the coal matrix into cubic structural units. Additionally, the micron-scale pore system exhibited a localized concentration of sheet-like pore groups, facilitating the diffusion of local moisture and increasing the space available for water seepage.

When the water immersion pressure was increased to 0.3 MPa, the original micron-scale cleat system was replaced by more uniformly distributed microcracks and secondary pores. This subdivision of the coal matrix into more irregular microstructural units not only enhanced the structural connectivity, leading to a relatively loose coal structure but also significantly increased the porosity and capacity for moisture exchange within the coal. At a water immersion pressure of 0.5 MPa, the internal fissure structure of the coal samples increased significantly, forming a widely distributed pore connectivity network accompanied by localized disordered microcrack structures. Compared with the 0 MPa immersion condition, the scale of the fissures showed a notable increase.

Under external loading conditions, the coal body undergoes further disturbance, resulting in the closure of internal pores and the generation, propagation, and further development of cracks [38,39,40]. Under the application of continuous stress, internal cracks in the coal body gradually expand, becoming key micro-fissures and leading to the macroscopic failure of the coal body. Under external stress, the lower part of the dry coal sample exhibited more prominent fissures, whereas the upper part, although compressed, formed new fissures. These new fissures, which were induced by the original pores, gradually expanded. With increasing external force, small fissures developed and connected to the surrounding pores, forming larger fissures. The proportion of pores with diameters greater than 100 µm increased from 28.5% to 33.6% before and after loading, and their porosity increased by 1.3%.

Under water immersion pressure, changes in the pore structure of the coal samples were significant: the porosity increased from 0.3% in dry samples to 3.2% at 0.5 MPa before loading and further increased from 7.1 % to 22.5% after loading. Additionally, the number of pores in the 0–150 µm range and new pores exceeding 150 µm increased significantly. Pore volume was mainly concentrated between 0 and 10 × 10³ μm³; However, as the pressure increased, the proportion of pores with a volume greater than 10 × 10³ μm³ in the coal samples after loading also rose. The growth in pore volume for the dry coal samples and those saturated at pressures ranging from 0 to 0.5 MPa was 7.35%, 12.51%, 17.13%, and 18.49%, respectively. This indicates that the coal samples have a high number of intrinsic pores and fractures with a dense distribution. In the early stages of water saturation, the water infiltration effect is significant (small pores become connected with medium and large pores). However, as the water pressure increased, this effect gradually weakened, though the number of pores continued to increase.

4. Conclusions

(1)

Compared with the dry coal samples, the water-immersed samples exhibited fewer AE counts and slower cumulative AE curve growth throughout the loading process until failure. The cumulative energy of the water-immersed samples decreased, and the rate of energy reduction gradually decreased with increasing immersion pressure. While dry coal samples were primarily characterized by tensile fractures, the formation of tensile–shear composite cracks under immersion conditions shifted the failure mode to a tensile–shear composite mechanism.

(2)

From the air-dried state to saturation under various immersion pressures, the average peak strength decreased from 29.98 to 11.96 MPa, representing a reduction of 57.4%. With increasing water immersion pressure, the average peak strength of the coal samples decreased from 29.98 to 11.96 MPa, a reduction of 57.4%. The peak strain increased to varying degrees.

(3)

The size and number of various types of pores increased to some extent under different water saturation pressures. As the water pressure increased, its role in promoting pore development in the coal samples gradually decreased. Most of the pores in the water-saturated samples had diameters smaller than 10 µm. The number of pores was inversely proportional to the pore diameter, with the pore volume primarily concentrated in the range of 0–20 × 10³ µm³. The average porosity of the samples increased with the increase in immersion pressure, and as the coal sample approached the fracture damage threshold, the porosity increased from 7.1% to 22.5%.