**4. Orthogonal Analysis**

The analysis methods of orthogonal experimental results include range analysis and variance analysis. Range analysis is a method to analyze the influence of each factor on the system value in a multi-factor system. It can not only determine the primary factor influencing the system value but also gives the optimal level combination of several factors. Variance analysis is also a method of multi-factor system analysis, which can not only estimate the error size but also represents the significant degree of influence of each factor on the system value [43]. The above two methods are used to analyze the experimental results.

### *4.1. Compressive Strength Analysis*

The results of the compressive strength range analysis are shown in Table 6. According to the range (*R*) of compressive strength caused by the change of dosage level, the factors are in order as follows: activator (29.0 MPa) > steel slag powder (9.5 MPa) > silicon powder (8.2 MPa) > metakaolin (5.4 MPa). Activator dosage has the most significant influence on the experimental results of compressive strength. It indicates that the activator is the primary influencing factor of compressive strength. The results of compressive strength variance analysis are shown in Table 7. Activator, steel slag powder, metakaolin, and silica fume have a very profound effect on the compressive strength. The influence degree of each factor on compressive strength can be further characterized according to the value of '*F'* of each factor. The greater the value of '*F*' of each factor, the more significant the influence is. Therefore, the order of the various factors according to the influence degree of compressive strength from large to small is the activator, steel slag powder, silica fume, and metakaolin. The results of range analysis and variance analysis indicate that the activator had the greatest influence on the compressive strength and is the primary factor influencing the compressive strength.

The relationship curve between the compressive strength and the dosage levels of each influencing factor is shown in Figure 5. The influence of the activator dosages on the compressive strength is shown in Figure 5a. The compressive strength decreases gradually with the increase of the activator dosage, and it is at maximum when the activator dosage is 5%. The influence of steel slag powder dosage on the compressive strength is shown in Figure 5b. The compressive strength decreases gradually with the increase of the steel slag powder dosage, and it reaches the maximum when the steel slag powder dosage is 10%. The influence of metakaolin dosage on compressive strength is shown in Figure 5c. The compressive strength first increases and then decreases with the increase of metakaolin dosage. The compressive strength reaches the maximum when the metakaolin dosage is 15%. The influence of silica fume dosage on compressive strength is shown in Figure 5d. The compressive strength first decreases and then increases with the increase of silica fume dosage. The compressive strength reaches the maximum value when the activator content is 8%. The compressive strength of steel slag cement mortar with 10% steel slag powder dosage is significantly different from that with 20%, 30%, and 40% steel slag powder dosage. The reason is that the activity of steel slag powder is low, and it is difficult to achieve a higher compressive strength even if the activation method is adopted. With the increase of the activator dosage, the content of stone powder also increases, and the negative effect caused by excessive stone powder is greater than the activation effect of activator on steel slag. When the dosage of metakaolin is 20%, the compressive strength is lower than the compressive strength corresponding to dosages of 5–15%, so the dosage should not be exceed 15%. With the increase of silica fume dosage (4–8%), the compressive strength of steel slag cement mortar increases greatly. The dosages corresponding to the maximum compressive strength are taken as the optimal dosage. Therefore, for the compressive strength of mortar, the optimal dosage combination of the four factors is activator 5%, steel slag powder 10%, metakaolin 15%, and silica fume 8%.


**Table 6.** Range analysis of compressive strength.

Note: *K*<sup>i</sup> is the sum of multiple test results at a certain level, *k*<sup>i</sup> is the mean value of multiple test results at a certain level, and *R* is the range of mean value of test results at different levels.


**Table 7.** Variance analysis of compressive strength. **Material SS df MS F Significant Degree** 

**Table 7.** Variance analysis of compressive strength.

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**Index Compressive Strength (MPa)** 

**Table 6.** Range analysis of compressive strength.

Therefore, for the compressive strength of mortar, the optimal dosage combination of the four factors is activator 5%, steel slag powder 10%, metakaolin 15%, and silica fume 8%.

*K*1 170.8 151.4 126.6 116.6 *K*2 142.9 116.6 122.9 112.7 *K*3 132.3 119.0 136.3 125.6 *K*4 54.6 113.5 114.7 145.6 *k*<sup>1</sup> **42.7 37.9** 31.7 29.2 *k*2 35.7 29.2 30.7 28.2 *k*3 33.1 29.8 **34.1** 31.4 *k*4 13.7 28.4 28.7 **36.4**  MAX 42.7 37.9 34.1 36.4 MIN 13.7 28.4 28.7 28.2 *R* 29.0 9.5 5.4 8.2 Note: *K*i is the sum of multiple test results at a certain level, *k*i is the mean value of multiple test results at a certain level, and *R* is the range of mean value of test results at different levels.

**Activator Steel Slag Powder Metakaolin Silica Fume** 

Note: *SS* is the sum of squares; *d*<sup>f</sup> is the degrees of freedom; *MS* is the mean square; *Se*<sup>1</sup> is the System error; *Se*<sup>2</sup> is the experiment error; Se is the overall error; *F* is the *MS*/*Se*; \*\* is very significant (*F* > *F*0.01 (3, 35)). tem error; *Se*2 is the experiment error; Se is the overall error; *F* is the *MS*/*Se*; \*\* is very significant (*F*  > *F*0.01 (3, 35).

**Figure 5.** Relationship between compressive strength and dosage levels of each factor: (**a**) activator; (**b**) steel slag powder; (**c**) metakaolin and (**d**) Silica fume. **Figure 5.** Relationship between compressive strength and dosage levels of each factor: (**a**) activator; (**b**) steel slag powder; (**c**) metakaolin and (**d**) Silica fume.

### *4.2. Analysis of Flexural Strength 4.2. Analysis of Flexural Strength*

strength.

The results of the flexural strength range analysis are shown in Table 8. According to the range (*R*) of flexural strength caused by the change of dosage level, the factors are in order as follows: activator (5.4 MPa)> steel slag powder (1.8 MPa) > metakaolin (1.3 MPa) > silica fume (1.2 MPa). Activator dosage has the most significant influence on the experimental results of flexural strength. It indicates that the activator is the primary influencing factor of flexural strength. The results of the variance analysis of flexural strength are shown in Table 9. Activator, steel slag powder, metakaolin, and silica fume all have a very The results of the flexural strength range analysis are shown in Table 8. According to the range (*R*) of flexural strength caused by the change of dosage level, the factors are in order as follows: activator (5.4 MPa)> steel slag powder (1.8 MPa) > metakaolin (1.3 MPa) > silica fume (1.2 MPa). Activator dosage has the most significant influence on the experimental results of flexural strength. It indicates that the activator is the primary influencing factor of flexural strength. The results of the variance analysis of flexural strength are shown in Table 9. Activator, steel slag powder, metakaolin, and silica fume all

significant influence on flexural strength. The influence degree of each factor on flexural strength can be further distinguished according to the value of '*F'* of each factor. There-

fume. The results of range analysis and variance analysis indicate the activator had the greatest influence on the flexural strength and is the primary factor affecting the flexural

The relationship curve between the flexural strength and the dosage levels of each factor is shown in Figure 6. The influence of the activator dosages on the flexural strength is shown in Figure 6a. The flexural strength first increases and then decreases with the increase of the activator dosage. The flexural strength reaches the maximum when the activator dosage is 10%. The influence of steel slag powder dosage on the flexural strength, shown in Figure 6b, indicates that the flexural strength decreases gradually with the increase of the steel slag powder dosage. The flexural strength reaches the maximum when the steel slag powder dosage level is 10%. The influence of metakaolin dosage on flexural strength is shown in Figure 6c. The flexural strength first increases and then decreases with the increase of metakaolin dosage. The flexural strength reaches the maximum when the metakaolin dosage is 15%. The influence of silica fume content on flexural strength is shown in Figure 6d. The flexural strength decreases first and then increases with the increase of silica fume dosage, while it reaches the maximum value when the activator content is 8%. Similar to the compressive strength, the steel slag cement mortar with smaller dosage (5%, 10%) of activator has a larger flexural strength, while the steel slag cement mortar with larger dosage (15%, 20%) of activator has smaller flexural strength. When the dosages of metakaolin and silica fume were 10% and 8%, respectively, the corresponding flexural strength reached the maximum, which was very close to 15% and 8% of optimal dosage in compressive strength analysis. In addition, the strength corresponding to 40% dosage of steel slag powder is only smaller than the strength corresponding to 10% dosage, and the strength corresponding to 2% dosage of silica fume is only smaller than the strength corresponding to 8% dosage, as shown in Figure 6. This indirectly explains the

have a very significant influence on flexural strength. The influence degree of each factor on flexural strength can be further distinguished according to the value of '*F'* of each factor. Therefore, the order of the different factors according to the influence degree of the flexural strength from large to small is the activator, steel slag powder, metakaolin, and silica fume. The results of range analysis and variance analysis indicate the activator had the greatest influence on the flexural strength and is the primary factor affecting the flexural strength.

The relationship curve between the flexural strength and the dosage levels of each factor is shown in Figure 6. The influence of the activator dosages on the flexural strength is shown in Figure 6a. The flexural strength first increases and then decreases with the increase of the activator dosage. The flexural strength reaches the maximum when the activator dosage is 10%. The influence of steel slag powder dosage on the flexural strength, shown in Figure 6b, indicates that the flexural strength decreases gradually with the increase of the steel slag powder dosage. The flexural strength reaches the maximum when the steel slag powder dosage level is 10%. The influence of metakaolin dosage on flexural strength is shown in Figure 6c. The flexural strength first increases and then decreases with the increase of metakaolin dosage. The flexural strength reaches the maximum when the metakaolin dosage is 15%. The influence of silica fume content on flexural strength is shown in Figure 6d. The flexural strength decreases first and then increases with the increase of silica fume dosage, while it reaches the maximum value when the activator content is 8%. Similar to the compressive strength, the steel slag cement mortar with smaller dosage (5%, 10%) of activator has a larger flexural strength, while the steel slag cement mortar with larger dosage (15%, 20%) of activator has smaller flexural strength. When the dosages of metakaolin and silica fume were 10% and 8%, respectively, the corresponding flexural strength reached the maximum, which was very close to 15% and 8% of optimal dosage in compressive strength analysis. In addition, the strength corresponding to 40% dosage of steel slag powder is only smaller than the strength corresponding to 10% dosage, and the strength corresponding to 2% dosage of silica fume is only smaller than the strength corresponding to 8% dosage, as shown in Figure 6. This indirectly explains the reason why G12 has a large flexural strength, which is the result of the combined action of many factors. The dosages corresponding to the maximum flexural strength are taken as the optimal dosages. Therefore, for the flexural strength of mortar, the optimal dosage combination of the four factors is activator 10%, steel slag powder 10%, metakaolin 10%, and silica fume 8%.


**Table 8.** Range analysis of flexural strength.

Note: *K*<sup>i</sup> is the sum of multiple test results at a certain level, *k*<sup>i</sup> is the mean value of multiple test results at a certain level, and *R* is the range of mean value of test results at different levels.


**Table 9.** Variance analysis of flexural strength. **Table 9.** Variance analysis of flexural strength.

**Table 8.** Range analysis of flexural strength.

and silica fume 8%.

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**Index Flexural Strength (MPa)** 

reason why G12 has a large flexural strength, which is the result of the combined action of many factors. The dosages corresponding to the maximum flexural strength are taken as the optimal dosages. Therefore, for the flexural strength of mortar, the optimal dosage combination of the four factors is activator 10%, steel slag powder 10%, metakaolin 10%,

*K***<sup>1</sup>** 32.2 29.1 22.8 26.4 *K***<sup>2</sup>** 32.5 22.0 27.4 23.0 *K***<sup>3</sup>** 23.2 21.8 26.6 22.4 *K***<sup>4</sup>** 11.1 26.1 22.2 27.2 *k***<sup>1</sup>** 8.0 **7.3** 5.7 6.6 *k***2 8.1** 5.5 **6.8** 5.7 *k***<sup>3</sup>** 5.8 5.4 6.7 5.6 *k***<sup>4</sup>** 2.8 6.5 5.6 **6.8 MAX** 8.1 7.3 6.8 6.8 **MIN** 2.8 5.4 5.6 5.6 *R* 5.4 1.8 1.3 1.2 Note: *K*i is the sum of multiple test results at a certain level, *k*i is the mean value of multiple test results at a certain level, and *R* is the range of mean value of test results at different levels.

**Activator Steel Slag Powder Metakaolin Silica Fume** 

Note: *SS* is the sum of squares; *d*<sup>f</sup> is the degrees of freedom; *MS* is the mean square; *Se*<sup>1</sup> is the System error; *Se*<sup>2</sup> is the experiment error; Se is the overall error; *F* is the *MS*/*Se*; \*\* is very significant (*F* > *F*0.01 (3, 35)); \* is significant (*F* > *F*0.05 (3, 35)). Note: *SS* is the sum of squares; *d*f is the degrees of freedom; *MS* is the mean square; *Se*1 is the System error; *Se*2 is the experiment error; Se is the overall error; *F* is the *MS*/*Se*; \*\* is very significant (*F*  > *F*0.01 (3, 35); \* is significant (*F* > *F*0.05 (3, 35).

**Figure 6.** Relationship between flexural strength and dosage levels of each factor: (**a**) activator; (**b**) steel slag powder; (**c**) metakaolin and (**d**) Silica fume. **Figure 6.** Relationship between flexural strength and dosage levels of each factor: (**a**) activator; (**b**) steel slag powder; (**c**) metakaolin and (**d**) Silica fume.

Through orthogonal analysis, it is determined that the activator is the primary factor affecting the strength of steel slag cement mortar, and the optimal dosages of each factor corresponding to the compressive strength and the flexure strength are obtained. It provides a basis for scientific research and engineering application. At the same time, it was found that excessive content of stone powder in the activator had a negative effect on the strength of steel slag cement mortar. Through orthogonal analysis, it is determined that the activator is the primary factor affecting the strength of steel slag cement mortar, and the optimal dosages of each factor corresponding to the compressive strength and the flexure strength are obtained. It provides a basis for scientific research and engineering application. At the same time, it was found that excessive content of stone powder in the activator had a negative effect on the strength of steel slag cement mortar.

The principle of orthogonal design method is to perform the overall evaluation of the system by uniformly sampling the level combinations of multiple factors. Therefore, the

Taking the compressive strength of the first eight groups as (0) *X*<sup>1</sup> , that is the system characteristic data sequence, and the dosage of activator, steel slag powder, metakaolin, and silica fume as the relative factors data sequence (0) *X*<sup>2</sup> , (0) *X*<sup>3</sup> , (0) *X*<sup>4</sup> , (0) *X*<sup>5</sup> , then

<sup>1</sup> *X* =( 6 ,44.2 ,40. ,29.8 ,28.4 43.4 ,37.2 ,45.6 ,44. ) 6

<sup>2</sup> *X* = ( ,10,1 ,10,10 5,5,5,5 ) 0

<sup>3</sup> *X* = ( 40,10, 0,30,40 10,20,30, ) 2

sarily take into account the optimal dosage level combination. The GM (0, *N*) model can be established to predict the system characteristic data of the optimal level combination obtained by the range analysis method. The data selection used to establish the model has a certain influence on the prediction accuracy of GM (0, *N*) model. Appropriately reducing the number of data used in the model establishment, according to the level value range of the primary factor of the system characteristic data, can improve the accuracy of the prediction model. The results of orthogonal analysis show that the activator is the main factor affecting the strength of mortar while the optimal dosages of the activator for compressive strength and flexural strength are lower 5% and 10%, respectively. Therefore, the GM (0, *N*) model was established by selecting the data of the first eight experimental groups for

(0)

(0)

**5. Prediction of Mortar Strength** 

improving the prediction accuracy.

*5.1. Prediction of Compressive Strength* 

(0)

### **5. Prediction of Mortar Strength**

The principle of orthogonal design method is to perform the overall evaluation of the system by uniformly sampling the level combinations of multiple factors. Therefore, the dosage level combinations of multiple factors adopted in the experiment may not necessarily take into account the optimal dosage level combination. The GM (0, *N*) model can be established to predict the system characteristic data of the optimal level combination obtained by the range analysis method. The data selection used to establish the model has a certain influence on the prediction accuracy of GM (0, *N*) model. Appropriately reducing the number of data used in the model establishment, according to the level value range of the primary factor of the system characteristic data, can improve the accuracy of the prediction model. The results of orthogonal analysis show that the activator is the main factor affecting the strength of mortar while the optimal dosages of the activator for compressive strength and flexural strength are lower 5% and 10%, respectively. Therefore, the GM (0, *N*) model was established by selecting the data of the first eight experimental groups for improving the prediction accuracy.

## *5.1. Prediction of Compressive Strength*

Taking the compressive strength of the first eight groups as *X* (0) 1 , that is the system characteristic data sequence, and the dosage of activator, steel slag powder, metakaolin, and silica fume as the relative factors data sequence *X* (0) 2 , *X* (0) 3 , *X* (0) 4 , *X* (0) 5 , then

$$\begin{array}{c} X\_1^{(0)} = (43.4, 37.2, 45.6, 44.6, 44.2, 40.6, 29.8, 28.4) \\ X\_2^{(0)} = (5, 5, 5, 5, 10, 10, 10, 10) \\ X\_3^{(0)} = (10, 20, 30, 40, 10, 20, 30, 40) \\ X\_4^{(0)} = (5, 10, 15, 20, 10, 5, 20, 15) \\ X\_5^{(0)} = (2, 4, 6, 8, 6, 8, 2, 4) \end{array}$$

The data sequence is superimposed at once, and the parameter column satisfying the least-square estimation is obtained through Equation (2).

$$\stackrel{\wedge}{b} = \begin{bmatrix} -0.541, -0.395, 1.361, 4.557, 41.867 \end{bmatrix}^T \tag{3}$$

Thus, the GM (0, 5) model of compressive strength is obtained:

$$\mathbf{x}\_1^{(0)}(k) = -0.541\mathbf{x}\_2^{(1)}(k) - 0.395\mathbf{x}\_3^{(1)}(k) - 1.361\mathbf{x}\_4^{(1)}(k) - 4.557\mathbf{x}\_5^{(1)}(k) + 41.867 \tag{4}$$

As shown in Table 10, the average relative simulation error of GM (0, 5) model of compressive strength is 5.9%, while the accuracy is above 94%, which is a good prediction accuracy. When the optimal dosage combination of compressive strength was substituted into Equation (4), the prediction value of compressive strength was 55.6 MPa. The optimal dosage combination of the flexural strength was substituted into Equation (4) to obtain the prediction value of compressive strength of 51.5 MPa. The results show that the compressive strength of the two mixtures reaches the level of P·O·42.5 Portland cement.


**Table 10.** Simulation error check of GM (0, 5) model for compressive strength.

## *5.2. Prediction of Flexural Strength*

Taking the flexural strength of the first eight groups as *X* (0) 1 , that is, the system characteristic data sequence, and the dosage of activator, steel slag powder, metakaolin, and silica fume as the relative factors data sequence *X* (0) 2 , *X* (0) 3 , *X* (0) 4 , *X* (0) 5 , then, the data sequence *X* (1) *i* is superimposed at once to obtain the parameter column satisfying the least-squares estimation through Equation (2).

$$\stackrel{\wedge}{b} = [0.362, -0.019, 0.158, 0.591, 7.737]^T \tag{5}$$

Thus, the GM (0, 5) prediction model of flexural strength is obtained as

$$\mathbf{x}\_1^{(0)}(k) = 0.362 \mathbf{x}\_2^{(1)}(k) - 0.019 \mathbf{x}\_3^{(1)}(k) + 0.158 \mathbf{x}\_4^{(1)}(k) + 0.591 \mathbf{x}\_5^{(1)}(k) + 7.737\tag{6}$$

As shown in Table 11, the average relative simulation error of GM (0, 5) model of flexural strength is 5.3%, while the corresponding accuracy is above 94%, which is a good prediction accuracy. When the optimal dosage combination of the flexural strength was substituted into Equation (6), the prediction value of the flexural strength is obtained as 9.7 MPa, reaching the level of P·O·42.5 Portland cement. By substituting the optimal dosage combination of compressive strength into Equation (6), the prediction value of flexural strength is 8.7 MPa, which is consistent with the strength of the reference group (9.2 MPa).

**Table 11.** Simulation error check of GM (0, 5) model for flexural strength.


### **6. Economic Benefit Analysis**

The cement industry is a material and energy-intensive industry, which not only consumes a lot of natural energy but also pollutes the environment. In practical engineering, cement as a cementitious material has a huge cost, while the cost of steel slag, metakaolin, and activator is relatively low. Activator, steel slag powder, metakaolin, and silica fume were used as cementitious materials in equal amounts instead of cement, and their economic benefits were evaluated. Through market research, the prices of activator, steel slag powder, metakaolin, silica fume, and ordinary silicate P·O·42.5 Portland cement are 170 RMB/ton, 100 RMB/ton, 400 RMB/ton, 1000 RMB/ton, and 450 RMB/ton, respectively. In practical engineering, the strength and cost requirements of cementitious materials are

different according to different working conditions. Thus, it is imperative to provide the economic benefit analysis of various dosage combinations for appropriate binder selection. Based on the analysis of the above test results, the experimental group has a total of four steel slag powder dosage levels (10%, 20%, 30%, and 40%). The results of the economic benefit analysis based on the compressive strength and flexural strength are shown in Tables 12 and 13, respectively.

**Table 12.** Economic benefit analysis on the combination with highest compressive strength in each steel slag powder dosage level.


**Table 13.** Economic benefit analysis on the combination with highest flexural strength in each steel slag powder dosage level.


The results of the economic effect analysis indicate that the cost after cement replacement can be reduced by 9.00–26.67% compared to that before the replacement when the compressive strength is used as the benchmark for analysis. When the flexural strength is analyzed, it is seen that the cost after the cement replacement can be reduced by 7.78–39.11% compared to that before the replacement. For practical engineering, it is assumed that 10,000 m<sup>3</sup> of concrete with the required strength of 42.5 MPa, the amount of cementitious material per cubic meter of concrete is 0.4 ton. Using the method proposed in this study, the cost of cement per cubic meter of concrete can save 31–156 RMB. Thus, the total cost of cement replacement can be saved by at least 310,000 RMB and up to the maximum of 1560,000 RMB, which is a significant economic impact on the project.

### **7. Conclusions**

A method of activating the activity of steel slag powder with neutral material is proposed. The validity of the proposed method is verified by experiments. Through orthogonal analysis, the optimal dosage combination of various components in the compound activator is determined. The grey prediction model is established to predict the strength of steel slag cement mortar under the optimal dosage combination of various factors. Considering the different requirements of cementitious materials in engineering, the economic benefits of several mix proportions are analyzed. The conclusions drawn from this study are appended below.

1/The experimental results show that with the change of steel slag powder dosage (10%, 20%, 30%, 40%), the compressive strength of mortar is affected. The highest strength of each dosage can reach more than 85% of the compressive strength of the control group. Similarly, the highest flexural strength can reach more than 90% of the flexural strength of the control group.

2/Through orthogonal analysis, it is ascertained that the activator is the primary factor influencing the strength of the steel slag cement mortar, and the optimal dosage

combination of the compressive strength of the mortar is obtained as activator 5%, steel slag powder 10%, metakaolin 15%, and silica fume as 8%, while the optimal dosage combination of flexural strength is determined as activator 10%, steel slag powder 10%, metakaolin 10%, and silica fume 8%.

3/GM (0, 5) prediction models for compressive strength and flexural strength were established, respectively. The compressive strength and flexural strength of mortar were predicted. The prediction results of the compressive strength and flexural strength for the optimal dosage combination of compressive strength are 55.6 MPa and 8.7 MPa, respectively. The compressive strength and flexural strength at the optimal dosage combination for flexural strength are predicted to be 51.5 MPa and 9.7 MPa, respectively, which reach the strength level of P·O·42.5 Portland cement.

4/The research conducted on economic benefit analysis for multiple dosage combinations showed that the method proposed in this study can lower environmental pollution and reduce the project cost to a greater extent on the basis of meeting project requirements.

**Author Contributions:** J.G. and X.Y. designed the experiments. Y.Z., L.L., L.Z. and J.Y. carried out the experiments. X.Y. and Y.Z. analyzed the experimental results. J.G. and Y.Z. reviewed, and edited the manuscript. J.G. received the funding. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Natural Science Foundation of China, grant number 51779095, Program for Science & Technology Innovation Talents in Universities of Henan Province, grant number 20HASTIT013, and Sichuan Univ, State Key Lab Hydraul & Mt River Engn, grant number SKHL2007. The APC was funded by Program for Science & Technology Innovation Talents in Universities of Henan Province, grant number 20HASTIT013.

**Data Availability Statement:** All the relevant data and models used in the study have been provided in the form of figures and tables in the published article.

**Acknowledgments:** This project was sponsored by National Natural Science Foundation of China (51779095), Program for Science & Technology Innovation Talents in Universities of Henan Province (20HASTIT013), Sichuan Univ, State Key Lab Hydraul & Mt River Engn (SKHL2007).

**Conflicts of Interest:** The authors declare no conflict of interest to this work.
