3.1 Theoretical calculation of tensile strength
When conducting dynamic experiments to measure the dynamic mechanical properties of a shale specimen, the m-SHPB system must satisfy two critical conditions: the one-dimensional stress wave assumption and the uniform distribution of stress and strain along the specimen's length. Under these conditions, and by neglecting the strain rate effect, the stress–strain relationship of the shale specimen can be obtained using the three-wave method (Wang et al.,
2020; Luo et al.,
2023):
(5)
where
,
, and
are the incident, reflection, and transmitted strain impulses,
and
are the cross-sectional area of the rod and the specimen, respectively,
,
, and
are the stress, strain, and strain rate of the specimen, respectively,
is the elastic longitudinal wave velocity of the rod, and
is the thickness of the shale specimen.
When the stress values of the two end faces are equal, the specimen is in a state of stress equilibrium, which satisfies:
(6)
According to the test specification recommended by ISRM (ISRM.Isrm,
1978), combined with the one-dimensional stress wave assumption, the dynamic loads
P
1 and
P
2 at the ends of the incident and transmission rod of the specimen are:
(7)
When the specimen is in stress equilibrium, the inertia effect can be ignored, and the loading load is calculated as follows:
(8)
The indirect tensile strength of the intact specimen is
(9)
Combined with Equation (
8), the dynamic tensile stress of the complete shale disc can be obtained as follows:
(10)
For rock specimens with a central hole, the holes will greatly affect the stress distribution inside the specimen; based on this, Hobbs (
1965) proposed a modified formula for calculating the indirect tensile strength as follows:
(11)
where
D and
d are the diameter and the central aperture of the specimen, respectively.
The existing research results (Li et al.,
2018; Li, Liu, et al.,
2024) have demonstrated that the tensile strength calculated by Equation (
11) was overestimated and did not accurately capture the tensile strength characteristics of the shale specimen. Therefore, the dynamic peak tensile strength of the SDCH was calculated using the below revised theoretical formula.
(12)
3.4 Quantitative analysis of multiple regression of tensile strength
To further quantify the differences in the influence of the impact pressure, the bedding angle, and the central aperture on the tensile strength, multivariate linear regression analysis was carried out to statistically analyze the experimental results (Table 2). As a scientific and effective analytical method, multivariate linear regression analysis (Ismail et al., 2023; Moosavi & Mohammadi, 2021) represents a complex model in regression analysis, capable of comprehensively considering the combined effects of multiple input variables on the output variables. Unlike univariate linear regression, it introduces additional factor variables to establish a more holistic system of relationships, thereby more accurately capturing the intricate influence of each factor on the output variables.
The expression for the multivariate linear regression model is as follows:
(13)
where
x is the input vector, which contains multiple features (impact pressure
x
P, bedding angle
x
B, central aperture
x
A);
f(
x) is the output variable (the tensile strength) of the model;
k
T is the feature weight; and
b is the intercept or offset of the model.
Expand Equation (
13) into the ternary regression equation required in this paper:
(14)
Parameter estimation was performed using the least squares method, and the sum of squares of the fitting error (residual)
Q was defined as:
(15)
The sum of squares of the fitting error (residual)
Q should be minimized; the sample regression equation in this case is the actual regression function of the model.
(16)
The sample regression function equation needs to satisfy:
(17)
Collating the equation yields:
(18)
The nonnormalized coefficients of the respective variables can be obtained by solving the system of equations, enabling the derivation of the MLR equations. It is particularly important to note that in multiple regression analysis, independent variables should not show high correlations with one another (Ismail et al.,
2023). To ensure the scientific rigor and validity of the MLR equations, it is essential to perform goodness-of-fit tests and significance tests on the fitting results. Additionally, the regression analysis must satisfy the assumptions of residual independence, homogeneity of variance, and normality (Cai et al.,
2023; Wang, Zhai, Shao, et al.,
2023).
1.
Goodness-of-fit test
The multiple decision coefficient R2 was defined to measure the degree of multiple regression equations fitting effect, and the specific calculation formula is as follows:
(19)
2.
Significance test
The significance test includes the linear relationship test (F-test) and the regression coefficient test (t-test), which are as follows:
(20)
(21)
3.
Residual independence
The residual independence test is based on the Durbin–Watson (DW) residual correlation test, and the specific theoretical calculation is as follows:
(22)
The key coefficients of the regression model were obtained by incorporating the experimental data from Table 2 (tensile strength) into the MLR analysis based on Equations (13-22), with the results presented in Table 3.
As shown in Table 3, the multiple decision coefficient R2 in the regression analysis was 0.907, indicating that the regression equation showed a good fitting effect. For the regression coefficient t0.975(56) = 1.67, the regression coefficients t of the impact pressure, the bedding angle, and the central aperture were 15.722, 13.724, and 7.249, respectively, so the regression equation satisfied the significance test. While the t-test evaluates individual regression coefficients, the F-test assesses the overall significance of the model (Ismail et al., 2023). In this context, the F-test was used to validate the model. At the significance level (α = 0.05), the calculated F-value of 162.69 was significantly greater than the critical F-value (F0.95[3, 56] = 2.78), further supporting the model's validity. Additionally, the correlation coefficient of Durbin–Watson residuals DW = 0.818 was between 0 and 2, indicating that the residuals showed positive autocorrelation and met the independence requirements (Basarir, 2019; Guan et al., 2023).
4.
Homogeneity and normality of variance
The normality of the regression analysis was assessed using a histogram of the normal distribution (Figure 7a). The results showed that the tensile strength data followed an approximately normal distribution, with a mean value of 20.81 and a standard deviation of 4.278 (standard normal distribution), indicating that the linear regression equation satisfied the requirement of normality (Moosavi & Mohammadi, 2021). The standardized residual plot (Figure 7b) was distributed around the 0 value, which was basically a symmetrical distribution up and down. Additionally, the distribution characteristics remained consistent across increasing predicted values, indicating that the data met the requirements for variance homogeneity and independence (Cai et al., 2023; Sari, 2020). Therefore, the regression analysis can be confidently concluded to be robust and valid based on the stringent evaluation of its underlying assumptions.
Table 3. Statistical characteristic coefficients of the ternary regression model.
| Model |
Nonnormalized coefficients |
Normalization coefficients |
t |
R2 |
F |
DW |
| b |
Standard error |
| b0 |
10.684 |
0.937 |
Null |
11.400 |
0.907 |
162.6 |
0.818 |
| bP |
35.023 |
2.228 |
0.674 |
15.722 |
| bB |
0.083 |
0.006 |
0.588 |
13.724 |
| bA |
−0.590 |
0.081 |
−0.311 |
−7.249 |
Statistical feature test for ternary regression analysis. (a) Normal distribution test and (b) variance homogeneity test.
The multiple regression equation of tensile strength under the combined influence of the impact pressure, the bedding angle, and the central aperture was as follows:
(23)
The nonstandardization coefficients of the impact pressure and the bedding angle were positive, suggesting a positive correlation between these variables and tensile strength. Conversely, an increase in the central aperture showed a negative effect on tensile strength. It is important to note that nonstandardized coefficients are influenced by factors such as variable scales and orders of magnitude, making them unsuitable for directly assessing the relative impact of independent variables on the dependent variable. In contrast, standardized regression coefficients enable a direct comparison of the effects of different variables. The standardized regression coefficients of the impact pressure, the bedding angle, and the central aperture were 0.674, 0.588, and −0.311, respectively. These values indicate that the influence of the three variables on tensile strength follows the order, from highest to lowest impact pressure, bedding angle, and central aperture. The stress fluctuations induced by impact pressure represent the most direct and significant factor, rapidly elevating local stress levels to the rock's fracture strength, thereby initiating and propagating cracks. Consequently, impact pressure exerts the greatest influence on tensile strength. The bedding angle plays a secondary role in determining tensile strength, primarily by influencing crack propagation pathways. At smaller bedding angles, cracks are more likely to propagate along the bedding planes, leading to reduced tensile strength. As the bedding angle increases, cracks may deviate from the bedding planes, altering their propagation direction and enhancing tensile strength. This variation in the bedding angle fundamentally changes the crack propagation mechanisms, thereby significantly affecting the rock's overall mechanical properties. The central aperture, on the other hand, predominantly affects tensile strength through localized stress concentrations, which diminish the rock's load-bearing capacity. Larger pores exacerbate stress concentrations in specific regions, promoting crack propagation. However, the influence of central aperture is more localized and indirect, resulting in a relatively weaker effect on tensile strength compared to the other two variables.
Under different impact pressures, with the bedding angle and the central aperture as independent variables, the key statistical characteristic coefficients for binary regression analysis are shown in Table 4. The regression analyses conducted under the three impact pressures all satisfied the conditions of normality, homogeneity of variance, and independence, confirming the validity of the statistical results.
Table 4. Statistical characteristic coefficients of the binary regression model.
| Impact pressure (MPa) |
|
Nonstandardized |
Standardized |
t |
R2 |
F |
DW |
| b |
Standard error |
| 0.2 |
b0 |
16.923 |
−0.297 |
|
57.062 |
0.971 |
283.119 |
0.988 |
| bB |
0.059 |
−0.003 |
0.895 |
21.612 |
| bA |
−0.366 |
−0.037 |
−0.412 |
−9.958 |
| 0.3 |
b0 |
22.124 |
0.670 |
|
33.005 |
0.922 |
100.987 |
0.892 |
| bB |
0.076 |
0.006 |
0.829 |
12.226 |
| bA |
−0.596 |
0.083 |
−0.485 |
−7.178 |
| 0.4 |
b0 |
24.525 |
1.369 |
|
17.919 |
0.861 |
52.533 |
0.880 |
| bB |
0.115 |
0.013 |
0.821 |
9.075 |
| bA |
−0.808 |
0.169 |
−0.431 |
−4.766 |
As shown in Figure 8, the average tensile strengths of 0.2, 0.3, and 0.4 MPa were 17.02, 21.37, and 24.03 MPa, respectively. The standard mean deviations were 2.034, 2.817, and 4.298, respectively, indicating a progressive increase in tensile strength with increasing impact pressure. Notably, the regression analyses conducted under the three impact pressure conditions all satisfied the fundamental assumptions of normality, homogeneity of variance, and independence, confirming the validity and reliability of the regression model.
Statistical feature test for binary regression analysis. (a) 0.2 MPa, (b) 0.3 MPa, and (c) 0.4 MPa.
As can be seen from Table
4 and Figure
8, the binary linear regression results were still scientific and reliable, and the linear regression equations for tensile strength were as follows:
(24)
Surface fitting diagrams with error bars at different impact pressures are shown in Figure 9. At 0.2, 0.3, and 0.4 MPa, the fitting decrease rates of the tensile strength with respect to the central aperture were 0.366, 0.596, and 0.808 MPa/mm, respectively, and the increase rates with respect to the bedding angle were 0.059, 0.076, and 0.115 MPa/(°), respectively. These results demonstrated that an increase in the impact pressure could strengthen the effect of both the bedding angle and the central aperture on the tensile strength. Under high-impact pressures, the crack propagation path and mechanisms became more intricate. The interaction between the central aperture and the bedding angle resulted in a more pronounced influence on tensile strength. Specifically, as the impact pressure increased, the stress concentration induced by the central aperture interacted with the bedding effects, thereby exacerbating crack propagation and significantly altering the mechanical response of the rock. However, the fitting error increased, indicating that the experimental data showed greater dispersion and that the control effects of the bedding and aperture were weakened. The normalization coefficients of the bedding angle were 0.895, 0.829, and 0.821, while those for the central aperture were −0.412, −0.485, and −0.431, respectively. This indicated that, across the experimental range, the influence of the bedding angle on tensile strength was consistently greater than that of the central aperture. This conclusion was also evident from Figure 9, where the inclination angle of the fitted surface in the direction of the bedding angle was obviously greater than that in the direction of the central aperture.
Surface fitting under different impact pressures. (a) 0.2 MPa, (b) 0.3 MPa, and (c) 0.4 MPa.
The prediction model of dynamic tensile strength was established according to the regression analysis, and the differences between the measured and the regressed prediction values are shown in Figure 10. All dynamic tensile strength values were predicted within the 95% prediction interval, demonstrating high accuracy. The dispersion of the predicted scatters was the smallest at 0.2 MPa impact pressure, indicating that the regression model performed better in terms of prediction accuracy at lower impact pressures.
Evaluation of the prediction accuracy of the regression model. (a) Ternary regression prediction and (b) binary regression prediction.