*Article* **Resilient Modulus—Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test**

#### **Haiping Yuan <sup>1</sup> , Weiqiang Li <sup>1</sup> , Yixian Wang 1,2,\* , Hang Lin <sup>3</sup> and Yan Liu <sup>4</sup>**


Received: 4 January 2019; Accepted: 7 March 2019; Published: 19 March 2019

**Abstract:** As one of the important parameters used in the analysis and design of subgrade, resilient modulus is directly related to the safety, economic and life time of subgrade structure. In this paper, the characteristics of resilient modulus of improved red clay at different additive content were studied through conducting laboratory repeated load tri-axial tests. The influence of stress state, moisture content, compactness, additive types, and content on resilient modulus were analyzed. In addition, the regression analysis of resilient modulus, was carried out referencing three existing prediction models. The results showed that the Andrei model can better fit the resilient modulus of red clay and have a higher determination coefficient. However, the Andrei model and other existing prediction models, reflect only the influence of stress state on resilient modulus, without considering the influence of moisture content, compactness and additive content. Therefore, based on the Andrei model, a comprehensive prediction model, which can reflect the influence of compactness, moisture content, additive content, and stress state on resilient modulus was introduced. Good agreement between the regression results and the measured ones demonstrated the integrative ability of the introduced model.

**Keywords:** red clay; additive; resilient modulus; prediction models; new comprehensive prediction model

### **1. Introduction**

With the development of the national economy, high speed and heavy loads are inevitably the developing trend of the traffic line. In this context, static design methods will not be able to meet the increasing requirements of subgrade and pavement materials for strength and deformation. Therefore, more attention should be paid to dynamic and creep characteristics [1–8]. Soils and rocks are natural subgrade and pavement materials in the flood plain. When these are stressed, they go through stages of deformation such as elastic deformation, ductile deformation and fracture [9–20]. As a special soil, red clay is widespread in China and mainly located in the humid and rainy southern region. At present, land use is becoming more and more serious. It is the development direction and inevitable trend to make full use of the local red clay to fill the subgrade. However, the stability of the clay subgrade is particularly prominent because of the adverse factors, such as rainy weather and heavy load [21–24]. In addition, red clay should not be directly used as subgrade filling, due to the high natural moisture content and poor stability. It is necessary to include additives to improve the mechanical properties of red clay, so as to meet the filling requirements. At present, there have been some research on the mechanical properties of the improved red clay with additives, but the stability of the red clay subgrade, under dynamic loading is seldom involved [25–29].

The concept of dynamic resilient modulus of subgrade soil was introduced by Seed et al. [30], who studied the relationship between the resilient (elastic and reversible) characteristics of subgrade soil and the fatigue damage of asphalt pavement. In fact, the tensile stress and cracks, caused by the accumulation of permanent strains are the major cause of the damage of the subgrade and pavement materials [31–35]. Therefore, flexible pavement and resilient modulus values can replace compaction degree(CD) and California bearing ratio (CBR) values to reflect the stress-strain relationship of pavement structures under repeated vehicle load. A resilient modulus is one of the main parameters characterizing the mechanical properties of subgrade soils and can well-reflect the stability of subgrade soil [32]. However, it is difficult to apply and popularize a resilient modulus in practical engineering because of the test operation's complexity and high cost of tests. Therefore, it is necessary to establish models for the prediction of resilient modulus. Moreover, the prediction models can provide a reliable basis for the selection of material parameters in the structure design [36–41]. For a long time, many scholars at home and abroad have deeply explored the main factors affecting the resilient modulus of subgrade soil and established many prediction models, based on different factors, such as stress state and basic physical properties of soil [42–47]. According to the different stress variables, prediction models can be divided into three categories: Single factor model related to mass stress, a composite model related to shear stress and mass stress, and a composite model related to shear stress and confining pressure [48–60]. However, most of existing prediction models only consider the influence of stress state on the resilient modulus [42], and rarely consider the influence of compactness, moisture content, and additive content. They are generally linear fitting [35–38], or combine a single physical parameter to predict the resilient modulus [43,44].

In the design of subgrade structure, the resilient modulus is different, according to the subgrade grade. In this paper, the red clay in Anhui, China was chosen to be improved by two types of additives. Through laboratory repeated load tri-axial tests, the influence of stress state, moisture content, compactness, additive type, and content on resilient modulus were analyzed. According to the research experience [61–66], based on existing prediction models, which fail to incorporate the influence of moisture content, compactness and additive content, a comprehensive prediction model which can reflect the influence of compactness, moisture content, additive content and stress state on resilient modulus was introduced. To conclude the performance of the introduced model, the predicted and the measured resilient modulus were paralleled. The parallel found the introduced model capable of capturing the characteristics of the measured resilient modulus [54]. Bycomparison and verification, it was found that the proposed model predicts the modulus of resilience of the improved clay with additives of various contents, indicating that it has some potential in engineering applications.

#### **2. Material Properties and Testing Method**

#### *2.1. Materials*

The basic properties of the red clay used in the test are shown in Table 1. In order to analyze the influence of stress state, moisture content, compactness, additive type and content on the resilient modulus of red clay, samples prepared at different moisture content (OMC minus 3%, OMC, OMC plus 3%), different compactness of P (91%, 93%, 95%), different additive types (ordinary Portland cement, lime) and content (3%, 6%, 9%) were tested.


**Table 1.** Basic properties of red clay.

The chemical and mineral compositions of the red clay, obtained from X-ray diffraction measurements, are summarized in Table 2, and Table 3, respectively. It is indicated that the mineral compositions include mainly kaolinite (i.e., Al2Si2O5(OH)4), goethite (i.e., FeO(OH)), and diaspore (i.e., SiO2). The amount of kaolinite is 60.87%. Therefore, kaolinite dominates the mineral compositions of the red clay.

**Table 2.** Chemical compositions of red clay.


#### **Table 3.** Mineral composition of red clay.


Proctor compaction tests were carried out to determine maximum dry densities and optimum moisture contents of the improved red clay samples which are presented in Table 4. Based on the compaction test results, the samples were prepared at the targeted compactness and moisture content.

**Table 4.** Proctor compaction test results on samples with different additive contents.


The samples were prepared according to China highway geotechnical test code (JTGE40-2007). The samples were prepared as follows: Drying, grinding, mixing with additive, curing, and preparation. Three parallel samples were prepared for each group with different admixtures. The samples were placed in a standard curing room with 25 ± 2 ◦C for 7 days. For each sample, the difference between the actual and the targeted moisture contents is less than 1%, and that difference is less than 2% for compactness.

### *2.2. Testing Method*

In the test, the resilient modulus values were measured by the dynamic servo system UTM400. The loading waveform is half-sine waveform and the load frequency is 1 Hz. The holding time and interval time are 0.1 s, and 0.9 s, respectively. The repeated load and resilient strain were measured by load sensor and displacement sensor respectively in the dynamic tri-axial chamber. The adopted stress loading sequence of subgrade soil was obtained from the international research results [67–70]. It is necessary to preload the samples before the test to simulate the history of subgrade stress during construction, eliminate the influence of load difference factors, such as initial loading and repeated loading and reduce the variability of test results. In addition, preloading can eliminate the possible bad contact between the bottom of the samples and the pressure plate to improve the accuracy of the

where Δ

*axial* Δε

σ

= recoverable axial strain.

σ

test [70]. A dynamic resilient modulus is defined as the ratio of repeated axial deviatoric stress to the recoverable axial strain as Figure 1. The cyclic number of loading is 100, and the average resilient deformation during the last 5 cycles, for each magnitude of repeated loading, was recorded. The dynamic resilient modulus of samples was calculated according to Equation (1). *Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 4 of 18 σΔ *axial M <sup>d</sup>* ε σ Δ Δ <sup>r</sup> = (1) where Δ *<sup>d</sup>* = deviatoric stress ( = <sup>1</sup> −

*Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 4 of 18

$$M\_{\rm r} = \frac{\Delta \sigma\_d}{\Delta \varepsilon\_{\rm axial}} \tag{1}$$

where ∆*σ<sup>d</sup>* = deviatoric stress (= *σ*<sup>1</sup> − *σ*3), *σ*<sup>1</sup> and *σ*<sup>3</sup> = major and minor principal stresses, and ∆*εaxial* = recoverable axial strain. *<sup>d</sup>* = deviatoric stress ( = <sup>1</sup> − <sup>3</sup> ), <sup>1</sup> and <sup>3</sup> = major and minor principal stresses, and *axial* Δε= recoverable axial strain.

σ

σ

σ

σ

σ

σ

σ

σ

**Figure 1.** Definition of dynamic resilient modulus (Hopkins et al., 2001 [51]). **3. Analysis of Results** 

#### **Figure 1.** Definition of dynamic resilient modulus (Hopkins et al., 2001[51]). **3. Analysis of Results** *3.1. Influence of Stress State on Resilient Modulus*

#### **3. Analysis of Results**  *3.1. Influence of Stress State on Resilient Modulus* Figure 2 shows the variation of resilient modulus with deviatoric stress and bulk stress of the

*3.1. Influence of Stress State on Resilient Modulus*  Figure 2 shows the variation of resilient modulus with deviatoric stress and bulk stress of the improved red clay. It can be seen from Figure 2 that for the improved red clay, when the confining pressure is constant, the resilient modulus decreases with an increase in deviatoric stress; when the deviatoric stress is constant, the resilient modulus increases with an increase in confining pressure. At the same time, in the repeated load triaxial test, σ <sup>2</sup> =σ <sup>3</sup> = confining pressure, 3σ <sup>3</sup> +σ <sup>d</sup> = bulk stress, octahedral shear stress is τ oct = 2σ *<sup>d</sup>* 3 . Therefore, the effects of confining pressure and deviatoric stress on the resilient modulus need to be discussed. The results of the two-factor variance analysis of resilient modulus of the improved red clay, with 3% cementor 3% lime, are Figure 2 shows the variation of resilient modulus with deviatoric stress and bulk stress of the improved red clay. It can be seen from Figure 2 that for the improved red clay, when the confining pressure is constant, the resilient modulus decreases with an increase in deviatoric stress; when the deviatoric stress is constant, the resilient modulus increases with an increase in confining pressure. At the same time, in the repeated load triaxial test, *σ*<sup>2</sup> = *σ*<sup>3</sup> = confining pressure, 3*σ*<sup>3</sup> + *σ<sup>d</sup>* = bulk stress, octahedral shear stress is *τ*oct = √ 2*σd*/3. Therefore, the effects of confining pressure and deviatoric stress on the resilient modulus need to be discussed. The results of the two-factor variance analysis of resilient modulus of the improved red clay, with 3% cementor 3% lime, are presented in Table 5. It can be seen from Table 5 that the effects of confining pressure and deviatoric stress on the resilient modulus are remarkable. improved red clay. It can be seen from Figure 2 that for the improved red clay, when the confining pressure is constant, the resilient modulus decreases with an increase in deviatoric stress; when the deviatoric stress is constant, the resilient modulus increases with an increase in confining pressure. At the same time, in the repeated load triaxial test, σ <sup>2</sup> =σ <sup>3</sup> = confining pressure, 3σ <sup>3</sup> +σ <sup>d</sup> = bulk stress, octahedral shear stress is τ oct = 2σ *<sup>d</sup>* 3 . Therefore, the effects of confining pressure and deviatoric stress on the resilient modulus need to be discussed. The results of the two-factor variance analysis of resilient modulus of the improved red clay, with 3% cementor 3% lime, are presented in Table 5. It can be seen from Table 5 that the effects of confining pressure and deviatoric stress on the resilient modulus are remarkable.

(**a**) (**b**) **Figure 2.** *Cont.*

σθ/kPa

0 30 60 90 120 σd/kPa

*M*<sup>r</sup> −σθ.

Deviatoric

*Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 5 of 18

**Figure 2.** Variation of resilient modulus with deviatoric stress and bulk stress. (**a**) Lime 3% OMC *M*<sup>r</sup> −σ *<sup>d</sup>* ; (**b**) Lime 3% OMC *M*<sup>r</sup> −σθ ; (**c**) Cement 3% OMC *M*<sup>r</sup> −σ *<sup>d</sup>* ; (**d**) Cement 3% OMC **Figure 2.** Variation of resilient modulus with deviatoric stress and bulk stress. (**a**) Lime 3% OMC *M*<sup>r</sup> − *σ<sup>d</sup>* ; (**b**) Lime 3% OMC *M*<sup>r</sup> − *σ<sup>θ</sup>* ; (**c**) Cement 3% OMC *M*<sup>r</sup> − *σ<sup>d</sup>* ; (**d**) Cement 3% OMC *M*<sup>r</sup> − *σ<sup>θ</sup>* .


**Table 5.** Two-factor variance analysis of resilient modulus.

#### stress 1853.83 3 617.942 178.36 3.86 Significant *3.2. Prediction Models of Resilient Modulus*

Confining pressure 1870.63 3 623.544 179.98 3.86 Significant Error 31.18 9 3.465 Sum 3755.64 15 *3.2. Prediction Models of Resilient Modulus*  The concept of a resilient modulus has been used to represent the non-linear stress-strain characteristics of subgrade soils. From the above analysis, it can be concluded that the resilient modulus of improved red clay has a strong dependence on the stress state. Several prediction models have been developed in the past to express the relationship between resilient modulus and stress state. The prediction models of non-linear resilient modulus mainly include the single factor The concept of a resilient modulus has been used to represent the non-linear stress-strain characteristics of subgrade soils. From the above analysis, it can be concluded that the resilient modulus of improved red clay has a strong dependence on the stress state. Several prediction models have been developed in the past to express the relationship between resilient modulus and stress state. The prediction models of non-linear resilient modulus mainly include the single factor model related to bulk stress or deviatoric stress [36], the composite model related to deviatoric stress, and mass stress [38], the composite model related to shear stress and confining pressure [40], and the composite model related to confining pressure and deviatoric stress [41]. In this paper, three prediction models frequently cited were chosen to analyze the influence of deviatoric stress and confining pressure on the resilient modulus of the improved red clay.

#### model related to bulk stress or deviatoric stress [36], the composite model related to deviatoric stress, and mass stress [38], the composite model related to shear stress and confining pressure [40], and the 3.2.1. Uzan-Witczak Model

composite model related to confining pressure and deviatoric stress [41]. In this paper, three prediction models frequently cited were chosen to analyze the influence of deviatoric stress and confining pressure on the resilient modulus of the improved red clay. Uzan-witczak [38] introduced deviatoric stress and bulk stress to characterize the influence of stress state on the resilient modulus, and established the nonlinear constitutive model, as shown in Equation (2).

$$M\_R = k\_1 \theta^{k\_2} \sigma\_d^{k\_3} \tag{2}$$

where *σ<sup>d</sup>* is the deviatoric stress and *k*1, *k*<sup>2</sup> and *k*<sup>3</sup> are regression coefficients. *θ* is the bulk stress that is given as

$$
\theta = \sigma\_1 + \sigma\_2 + \sigma\_3
$$

where *σ*<sup>1</sup> is the major principal stress; *σ*<sup>2</sup> is the intermediate principal stress, and *σ*<sup>3</sup> is the minor principal stress or confining pressure (= *σ*<sup>2</sup> for *M<sup>R</sup>* test in triaxial test)

However, this prediction model has some limitations: when *σ<sup>d</sup>* equals 0, the result of Equation (2) tends to infinity. When *σ*<sup>1</sup> and *σ*<sup>3</sup> equal 0, the result is 0·∞.

#### 3.2.2. Andrei Model

In the practical problem, the intermediate and the minor principal stresses are not equal. In order to consider the influence of both of them on the resilient modulus, Andrei model [40] introduced the octahedral shear stress to replace the deviatoric stress in the May and Witczak model [71]. This operation provides a better explanation of the stress state of the materials, and further generalize the nonlinear constitutive model, as shown in Equation (3).

$$M\_R = k\_1 P\_a \left(\frac{\theta}{P\_a}\right)^{k\_2} \left(\frac{\tau\_{\text{oct}}}{P\_a} + 1\right)^{k\_3} \tag{3}$$

where *τoct* is the octahedral shear stress, *P<sup>a</sup>* = atmospheric pressure and *k*1, *k*<sup>2</sup> and *k*<sup>3</sup> are regression coefficients.

*θ* is the mass stress that is given as

$$
\theta = \sigma\_1 + \sigma\_2 + \sigma\_3
$$

*τoct* is given as

$$\pi\_{\rm oct} = \frac{1}{3}\sqrt{(\sigma\_1 - \sigma\_3)^2 + (\sigma\_1 - \sigma\_2)^2 + (\sigma\_2 - \sigma\_3)^2}$$

where *σ*<sup>1</sup> is the major principal stress; *σ*<sup>2</sup> is the intermediate principal stress and *σ*<sup>3</sup> is the minor principal stress or confining pressure.

In this prediction model, the influence of bulk stress and octahedral shear stress on the resilient modulus was fully taken into account. Compared with Equation (2), the Andrei model is essentially consistent with the Uzan-Witczak model. The difference between them is that the term *<sup>τ</sup>oct Pa* in the Andrei model is followed by an additional constant of 1, eliminating the existing problems of the Uzan-Witczak model.

### 3.2.3. Ni Model

Based on testing results, Ni et al. [41] found that the resilient modulus was very sensitive to confining pressure, because of this, they introduced confining pressure and deviatoric stress to the prediction model to eliminate the existing problems included in the above Uzan-Witczak model. The following prediction model was given (Equation (4)):

$$M\_R = k\_1 P\_a \left(\frac{\sigma\_3}{P\_a} + 1\right)^{k\_2} \left(\frac{\sigma\_d}{P\_a} + 1\right)^{k\_3} \tag{4}$$

where *σ*<sup>3</sup> is the minor principal stress or confining pressure, *σ<sup>d</sup>* is the deviatoric stress, *P<sup>a</sup>* = atmospheric pressure, and *k*1, *k*<sup>2</sup> and *k*<sup>3</sup> are regression coefficients.

Due to the difference in the physical properties of red clay in different regions, the selected prediction models were different from each other. In this paper, the above-mentioned prediction models of the resilient modulus were selected for red clay in Anhui province.

### *3.3. Regression Analysis Results*

The fitting performance of a regression model is frequently evaluated by the determination coefficient, R<sup>2</sup> , defined as the ratio of regression sum of squares (SSR) to total sum of square (SST), that is R<sup>2</sup> = SSR/SST.

$$SSR = \sum\_{i=1}^{n} \left( y\_i - \widehat{y}\_i \right)^2 \tag{5}$$

$$SST = \sum\_{i=1}^{n} (y\_i - \overline{y}\_i)^2 \tag{6}$$

In Equations (5) and (6), *y<sup>i</sup>* is actual value, \_ *y i* is predicted value, *y<sup>i</sup>* is sample mean, SSR is regression sum of squares, SST is total sum of square.

Regression analysis of the test data was conducted for various conditions, and the results are shown in Table 6. From the regression results, a high coefficient of determination between the three prediction models, and the test results, is observed. For the Uzan-Witczak model, NI model and Andrei model, the coefficient of determination *R* <sup>2</sup> was greater than 0.80, 0.934, and 0.937, respectively. By comparing the coefficients of determination for the three prediction models, it can be concluded that the regression results, generated by the Andrei model and the NI model, are better than the regression results generated by the Uzan-Witczak model. In particular, the Andrei model can fit the test data better than the NI model does.

In the above three prediction models of the resilient modulus, k1 is used to characterize the stiffness of the subgrade soil and is proportional to the resilient modulus. The value of k1 increases with an increase in effective stress, additive content and compactness, and decreases with an increase in moisture content. k2 reflects the influence of confining pressure (bulk stress) on resilient modulus and is positively associated with confining pressure. The value of k2 as a whole decreaseswith an increase in moisture content, and increases with an increase in additive content and compactness. The value of k3 is negative, indicating that a negative correlation between the resilient modulus and the deviatoric stress (octahedral shear stress). k3 shows that the dynamic resilient modulus of subgrade soils decreases with an increase in shear stress, and its value is not obviously related to the moisture content and the compactness.

*Appl. Sci.* **2019**, *9*, 1155


**Table 6.** Parameters required in different prediction models of resilient modulus.

**Mass Stress**  **Octahedral Shear** 

Table 7 and Figure 3 show that predicted resilient modulus versus measured resilient modulus for the improved red clay prepared at 93% compactness and 6% additive (cement and lime). It can be seen that the maximum errors, between the measured and the predicted resilient modulus, by using the Andrei model, NI model and Uzan-Witczak model are, respectively, 4.533%, 3.934%, and 6.133%. In addition, the average error and variance of Andrei, NI and Uzan-Witczak models are 1.70% ± 1.14%, 2.11% ± 1.07%, and 2.32% ± 1.75%, respectively. It can be concluded that the fitting performance of Andrei model is better than that of NI model and Uzan-Witczak model. the Andrei model, NI model and Uzan-Witczak model are, respectively, 4.533%, 3.934%, and 6.133%. In addition, the average error and variance of Andrei, NI and Uzan-Witczak models are 1.70% ± 1.14%, 2.11% ± 1.07%, and 2.32% ± 1.75%, respectively. It can be concluded that the fitting performance of Andrei model is better than that of NI model and Uzan-Witczak model. **Table 7.** Test data and forecast data of different prediction models. **Resilient Modulus** 

*Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 9 of 18

for the improved red clay prepared at 93% compactness and 6% additive (cement and lime). It can be seen that the maximum errors, between the measured and the predicted resilient modulus, by using


**Table 7.** Test data and forecast data of different prediction models. **Measured Andrei Model NI Model Uzan-Witczak Model** 

**Figure 3.** Predicted resilient modulus versus measured resilient modulus by three prediction models (Andrei model, NI model and Uzan-Witczak model). (**a**) 93% compactness and 6% cement; (**b**) 93% compactness and 6% lime. **Figure 3.** Predicted resilient modulus versus measured resilient modulus by three prediction models (Andrei model, NI model and Uzan-Witczak model). (**a**) 93% compactness and 6% cement; (**b**) 93% compactness and 6% lime.

#### **4. The Dependence of Resilient Modulus on Material Properties 4. The Dependence of Resilient Modulus on Material Properties**

#### *4.1. The Influence of Moisture Content and Compactness on Resilient Modulus 4.1. The Influence of Moisture Content and Compactness on Resilient Modulus*

The improved red clay samples, prepared at 93% compactness and 6% cement (C6%), and prepared at 93% compactness and 6% lime (L6%), were selected to study the influence of different moisture content on the dynamic resilient modulus of the improved red clay. As can be seen from Figure 4, the resilient modulus of the improved red clay is greatly affected by moisture content, and its value decreases with an increase in moisture content. For the improved red clay with 6% cement, when the confining pressure is 15 kPa and the deviatoric stress is 30 kPa, the resilient modulus for The improved red clay samples, prepared at 93% compactness and 6% cement (C6%), and prepared at 93% compactness and 6% lime (L6%), were selected to study the influence of different moisture content on the dynamic resilient modulus of the improved red clay. As can be seen from Figure 4, the resilient modulus of the improved red clay is greatly affected by moisture content, and its value decreases with an increase in moisture content. For the improved red clay with 6% cement, when the confining pressure is 15 kPa and the deviatoric stress is 30 kPa, the resilient modulus for the optimum moisture content (OMC) of 21.5% (OMC minus 3%), 24.3% (OMC), 27.1% (OMC plus 3%) is 171.2 MPa, 158.0 MPa, 147.7 MPa, respectively. Compared with the moisture content of 21.5% (OMC

increases with an increase in mass stress.

minus 3%), the resilient modulus for moisture content of 24.3%, and 27.1% (OMC plus 3%) decreases by 7.71%, and 14.1%, respectively. In addition, when the moisture content is invariable, the resilient modulus of improved red clay decreases with an increase in deviatoric stress, and increases with an increase in mass stress. compactness, the resilient modulus for compactness of 93%, and 95% increases by 7.08%, and 15.05%, respectively. In addition, when the compactness is invariable, the resilient modulus of the improved red clay decreases with an increase in deviatoric stress and increases with an increase in bulk stress.

91%, 93%, and 95% is 177.89 MPa, 190.5 MPa, and 204.67 kPa, respectively. Compared with 91%

*Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 10 of 18

the optimum moisture content (OMC) of 21.5% (OMC minus 3%), 24.3% (OMC), 27.1% (OMC plus 3%) is 171.2 MPa, 158.0 MPa, 147.7 MPa, respectively. Compared with the moisture content of 21.5% (OMC minus 3%), the resilient modulus for moisture content of 24.3%, and 27.1% (OMC plus 3%) decreases by 7.71%, and 14.1%, respectively. In addition, when the moisture content is invariable, the resilient modulus of improved red clay decreases with an increase in deviatoric stress, and

The improved red clay samples, prepared at optimum moisture content and 6% cement, and prepared at OMC and 6% lime (L6%), were selected to study the influence of different compactness on the dynamic resilient modulus. As can be seen from Figure 5, when the moisture content is invariable, the resilient modulus of the improved red clay increases with an increase in compactness. For the improved red clay with 6% cement, when the confining pressure is 60 kPa and the deviatoric

**Figure 4.** *MR* − *w* relation curve at different stress states. (**a**) <sup>d</sup> *C P kPa* 6%, 93%, 30 σ = ; (**b**) <sup>d</sup> *C P kPa* 6%, 93%, 55 σ = ; (**c**) <sup>d</sup> *L OMC kPa* 6%, , 30 σ = ; (**d**) **Figure 4.** *M<sup>R</sup>* − *w* relation curve at different stress states. (**a**) *C*6%, *P*93%, *σ<sup>d</sup>* = 30 kpa; (**b**) *C*6%, *P*93%, *σ<sup>d</sup>* = 55 kpa; (**c**) *L*6%, *OMC*, *σ<sup>d</sup>* = 30 kpa; (**d**) *L*6%, *P*93%, *σ<sup>d</sup>* = 55 kpa.

<sup>d</sup> *L P kPa* 6%, 93%, 55 σ = . The improved red clay samples, prepared at optimum moisture content and 6% cement, and prepared at OMC and 6% lime (L6%), were selected to study the influence of different compactness on the dynamic resilient modulus. As can be seen from Figure 5, when the moisture content is invariable, the resilient modulus of the improved red clay increases with an increase in compactness. For the improved red clay with 6% cement, when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, the resilient modulus for the red clay samples, when prepared at a compactness of 91%, 93%, and 95% is 177.89 MPa, 190.5 MPa, and 204.67 kPa, respectively. Compared with 91% compactness, the resilient modulus for compactness of 93%, and 95% increases by 7.08%, and 15.05%, respectively. In addition, when the compactness is invariable, the resilient modulus of the improved red clay decreases with an increase in deviatoric stress and increases with an increase in bulk stress.

*Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 11 of 18

**Figure 5.** *MR* − *P* curve at different stress states. (**a**) <sup>d</sup> *C OMC kPa* 6%, , 30 σ = ; (**b**) <sup>d</sup> *C OMC kPa* 6%, , 55 σ = ; (**c**) <sup>d</sup> *L OMC kPa* 6%, , 30 σ = ; (**d**) **Figure 5.** *M<sup>R</sup>* −*P* curve at different stress states. (**a**) *C*6%, *OMC*, *σ<sup>d</sup>* = 30 kpa; (**b**) *C*6%,*OMC*, *σ<sup>d</sup>* = 55 kpa; (**c**) *L*6%, *OMC*, *σ<sup>d</sup>* = 30 kpa; (**d**) *L*6%, *OMC*, *σ<sup>d</sup>* = 55 kpa.

#### <sup>d</sup> *L OMC kPa* 6%, , 55 σ = . *4.2. The Influence of Additive Content on Resilient Modulus*

*4.2. The Influence of Additive Content on Resilient Modulus*  The improved red clay prepared at optimum moisture content and 93% compactness were selected to study the influence of different additives on the dynamic resilient modulus of the improved red clay. As can be seen from Figures 6 and 7, both cement and lime (3~9%) can significantly improve the dynamic resilient modulus of the red clay. In Figure 6, when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, the resilient modulus of red clay samples, prepared at a lime content of 3%, 6%, and 9% is 149.9 MPa, 166 MPa, and 180.6 MPa, respectively. Compared with 3% lime, the resilient modulus for lime content of 6%, 9% increases by 10.75%, and 20.4%, respectively. In Figure 7, when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, the resilient modulus of red clay with cement content of 3%, 6%, and 9% is 170.4 MPa, 190.5 MPa, and 202 MPa, respectively. Compared with 3% cement, the resilient modulus for cement content of 6%, and 9% increases by 11.8%, and 18.54%, respectively. By comparing Figure 6a with Figure 7a, it can be concluded that when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, an increase of 12% in the dynamic resilient modulus was observed for 3% cement, compared The improved red clay prepared at optimum moisture content and 93% compactness were selected to study the influence of different additives on the dynamic resilient modulus of the improved red clay. As can be seen from Figures 6 and 7, both cement and lime (3~9%) can significantly improve the dynamic resilient modulus of the red clay. In Figure 6, when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, the resilient modulus of red clay samples, prepared at a lime content of 3%, 6%, and 9% is 149.9 MPa, 166 MPa, and 180.6 MPa, respectively. Compared with 3% lime, the resilient modulus for lime content of 6%, 9% increases by 10.75%, and 20.4%, respectively. In Figure 7, when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, the resilient modulus of red clay with cement content of 3%, 6%, and 9% is 170.4 MPa, 190.5 MPa, and 202 MPa, respectively. Compared with 3% cement, the resilient modulus for cement content of 6%, and 9% increases by 11.8%, and 18.54%, respectively. By comparing Figure 6a with Figure 7a, it can be concluded that when the confining pressure is 60 kPa and the deviatoric stress is 30 kPa, an increase of 12% in the dynamic resilient modulus was observed for 3% cement, compared with that of 3% lime. By comparing other data, it can be also seen that, when the contents of cement and lime are the same, cement can significantly improve the resilient modulus of red clay.

with that of 3% lime. By comparing other data, it can be also seen that, when the contents of cement

and lime are the same, cement can significantly improve the resilient modulus of red clay.

*Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 12 of 18

180

**Figure 6.** Relationship between resilient modulus (*Mr*) and lime content for various stress states. (**a**) <sup>d</sup> *OMC P kPa* , 93%, 30 σ = ; (**b**) <sup>d</sup> *OMC P kPa* , 93%, 55 σ = . **Figure 6.** Relationship between resilient modulus (*Mr*) and lime content for various stress states. (**a**) *OMC*, *P*93%, *σ<sup>d</sup>* = 30 kpa; (**b**) *OMC*, *P*93%, *σ<sup>d</sup>* = 55 kpa. <sup>d</sup> *OMC P kPa* , 93%, 30 σ = ; (**b**) <sup>d</sup> *OMC P kPa* , 93%, 55 σ= .

**Figure 7.** Relationship between resilient modulus (*Mr*) and cement content for various stress states. = ; (**b**) <sup>d</sup> *OMC P kPa* , 93%, 55 = . **Figure 7.** Relationship between resilient modulus (*Mr*) and cement content for various stress states. (**a**) <sup>d</sup> *OMC P kPa* , , 93% 30 σ = ; (**b**) <sup>d</sup> *OMC P kPa* , 93%, 55 σ = . **Figure 7.** Relationship between resilient modulus (*Mr*) and cement content for various stress states. (**a**) *OMC*, *P*93%, *σ<sup>d</sup>* =30 kpa; (**b**) *OMC*, *P*93%, *σ<sup>d</sup>* = 55 kpa.

σ

#### (**a**) <sup>d</sup> *OMC P kPa* , , 93% 30 *4.3. New Comprehensive Prediction Model 4.3. New Comprehensive Prediction Model*

), bulk stress (

expressed as a Equation (7).

expressed as a Equation (7).

σ

*4.3. New Comprehensive Prediction Model*  From the analysis of the existing prediction models, it can be concluded that the Andrei model can better predict the resilient modulus of the red clay chosen for this test. However, the Andrei model and other existing prediction models reflect only the influence of the stress state on resilient modulus, but the influence of moisture content, compactness, and additive content were not considered. Lu et al. proposed a new methodolgy to convert the band shaped drain into an annular instead of a circle to keep the sectional area or perimeter of the band shaped drain unchanged before and after the convertion [72]. From Figure 4 through 7, it can be seen that the resilient modulus is correlated negatively with moisture content, meaning that the higher the moisture content, the smaller the resilient modulus. Additionally, the resilient modulus is correlated positively with the compactness and the additive content, which means that the greater the compactness and the additive content, the larger the resilient modulus. It can be concluded that the influence of From the analysis of the existing prediction models, it can be concluded that the Andrei model can better predict the resilient modulus of the red clay chosen for this test. However, the Andrei model and other existing prediction models reflect only the influence of the stress state on resilient modulus, but the influence of moisture content, compactness, and additive content were not considered. Lu et al. proposed a new methodolgy to convert the band shaped drain into an annular instead of a circle to keep the sectional area or perimeter of the band shaped drain unchanged before and after the convertion [72]. From Figure 4 through 7, it can be seen that the resilient modulus is correlated negatively with moisture content, meaning that the higher the moisture content, the smaller the resilient modulus. Additionally, the resilient modulus is correlated positively with the compactness and the additive content, which means that the greater the compactness and the additive content, the larger the resilient modulus. It can be concluded that the influence of From the analysis of the existing prediction models, it can be concluded that the Andrei model can better predict the resilient modulus of the red clay chosen for this test. However, the Andrei model and other existing prediction models reflect only the influence of the stress state on resilient modulus, but the influence of moisture content, compactness, and additive content were not considered. Lu et al. proposed a new methodolgy to convert the band shaped drain into an annular instead of a circle to keep the sectional area or perimeter of the band shaped drain unchanged before and after the convertion [72]. From Figure 4 through 7, it can be seen that the resilient modulus is correlated negatively with moisture content, meaning that the higher the moisture content, the smaller the resilient modulus. Additionally, the resilient modulus is correlated positively with the compactness and the additive content, which means that the greater the compactness and the additive content, the larger the resilient modulus. It can be concluded that the influence of compactness, moisture content, and additive content on the resilient modulus is remarkable.

compactness, moisture content, and additive content on the resilient modulus is remarkable. In this paper, considering the above factors affecting the resilient modulus, the relationship between moisture content (*W* ), compactness ( *P* ), additive content (*C* ), octahedral shear stress ( *oct* τ θ ), atmospheric pressure ( *Pa* ) and resilient modulus ( *MR* ) are established, which is compactness, moisture content, and additive content on the resilient modulus is remarkable. In this paper, considering the above factors affecting the resilient modulus, the relationship between moisture content (*W* ), compactness ( *P* ), additive content (*C* ), octahedral shear stress ( *oct* τ ), bulk stress (θ ), atmospheric pressure ( *Pa* ) and resilient modulus ( *MR* ) are established, which is In this paper, considering the above factors affecting the resilient modulus, the relationship between moisture content (*W*), compactness (*P*), additive content (*C*), octahedral shear stress (*τoct*), bulk stress (*θ*), atmospheric pressure (*Pa*) and resilient modulus (*MR*) are established, which is expressed as a Equation (7).

$$F(\mathcal{W}, P, \mathcal{C}, \mathfrak{x}\_{\text{oct}}, \theta, P\_a, M\_R) = 0 \tag{7}$$

,*Pa* ,*MR* = 0 (7)

,*Pa* ,*MR* = 0 (7)

*F*( ) *W*,*P*,*C*,τ *oct* ,θ

*F*( ) *W*,*P*,*C*,τ *oct* ,θ

As the resilient modulus is correlated positively with the compactness and the additive content, and negatively with the moisture content, based on dimensional analysis [73], Equation (7) can be transformed into Equation (8).

$$F(\text{P}/\text{W}, \text{C}/\text{W}, \text{\textdegree}\_{\text{oct}}/\text{P}\_{\text{a}}, \theta/\text{P}\_{\text{a}}, M\_{\text{R}}) = \mathbf{0} \tag{8}$$

On this basis, combined with the above existing prediction models, to eliminate the influence of dimension, the compactness and the additive content were normalized by the moisture content, respectively. The following prediction model was given (Equation (9)). In Equation (9), to eliminate the problem that the predicted value of resilient modulus is 0, *<sup>C</sup> <sup>W</sup>* and *<sup>τ</sup>oct Pa* add 1, respectively.

$$M\_R = k\_1 P\_a \left(\frac{P}{W}\right)^{k\_2} \left(\frac{\text{C}}{W} + 1\right)^{k\_3} \left(\frac{\theta}{P\_a}\right)^{k\_4} \left(\frac{\tau\_{oct}}{P\_a} + 1\right)^{k\_5} \tag{9}$$

where *τoct* is the octahedral shear stress, *P<sup>a</sup>* = atmospheric pressure, *k*1, *k*2, *k*3, *k*<sup>4</sup> and *k*<sup>5</sup> are regression coefficients, *θ* is the bulk stress, *P* is compactness, *C* is additive content, *W* is moisture content, and *P<sup>a</sup>* is atmospheric pressure.

Regression analysis of the testing results on the red clay samples improved with cement and/or lime was carried out by using Equation (9). Presented in Table 8 are the parameters that the proposed prediction model requires, the determination coefficient of improved red clay samples prepared at different moisture content, and the compactness and additive content. For prediction models, the greater the determination coefficient, R 2 , the better the regression effect, indicating a higher rationality and reliability of the prediction model. The determination coefficient of the proposed comprehensive prediction model for the additive of cement and lime is 0.84, and 0.86, respectively.


**Table 8.** Comprehensive prediction model regression equation for two additives.

The comparison between the predicted and the measured values are presented in Figures 8 and 9. As shown in Figures 8 and 9, the maximum error between the predicted and the testing results for different cement and lime contents is 8.67%, and 8.70%, respectively. In addition, the average error and variance of the new comprehensive prediction model for different cement and lime contents are, respectively, 3.18% ± 4.03%, and 3.06% ± 3.94%. The new comprehensive prediction model, which reflects the influence of compactness, moisture content, additive content, and stress state on resilient modulus has good regression performance.

*Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 14 of 18

*Appl. Sci.* **2019**, *9*, x FOR PEER REVIEW 14 of 18

**Figure 8.** Predicted resilient modulus versus measured resilient modulus at different optimum moisture content, compactness and cement by four prediction models (comprehensive model, Andrei model, NI model &Uzan-Witczak model). (**a**)93% compactness and 6% cement; (**b**)optimum moisture content (OMC)and 6% cement; (**c**)93% compactness and OMC. **Figure 8.** Predicted resilient modulus versus measured resilient modulus at different optimum moisture content, compactness and cement by four prediction models (comprehensive model, Andrei model, NI model &Uzan-Witczak model). (**a**) 93% compactness and 6% cement; (**b**) optimum moisture content (OMC)and 6% cement; (**c**) 93% compactness and OMC. **Figure 8.** Predicted resilient modulus versus measured resilient modulus at different optimum moisture content, compactness and cement by four prediction models (comprehensive model, Andrei model, NI model &Uzan-Witczak model). (**a**)93% compactness and 6% cement; (**b**)optimum moisture content (OMC)and 6% cement; (**c**)93% compactness and OMC.

**Figure 9.** Predicted resilient modulus versus measured resilient modulus at different optimum moisture content, compactness and lime by four prediction models (Comprehensive model, Andrei model, NI model and Uzan-Witczak model). (**a**)93% compactness and 6% lime; (**b**)OMC and 6% lime; (**c**)93% compactness and OMC. **Figure 9.** Predicted resilient modulus versus measured resilient modulus at different optimum moisture content, compactness and lime by four prediction models (Comprehensive model, Andrei model, NI model and Uzan-Witczak model). (**a**)93% compactness and 6% lime; (**b**)OMC and 6% lime; (**c**)93% compactness and OMC. **Figure 9.** Predicted resilient modulus versus measured resilient modulus at different optimum moisture content, compactness and lime by four prediction models (Comprehensive model, Andrei model, NI model and Uzan-Witczak model). (**a**) 93% compactness and 6% lime; (**b**) OMC and 6% lime; (**c**) 93%compactness and OMC.

#### **5. Conclusions 5. Conclusions 5. Conclusions**

Due to the great difference in the physical properties of red clay in different regions, it is necessary to point out that the above predicted results are only applicable to the prediction of resilient modulus and improvement of red clay in Anhui province. Due to the great difference in the physical properties of red clay in different regions, it is necessary to point out that the above predicted results are only applicable to the prediction of resilient modulus and improvement of red clay in Anhui province. Due to the great difference in the physical properties of red clay in different regions, it is necessary to point out that the above predicted results are only applicable to the prediction of resilient modulus and improvement of red clay in Anhui province.

1.The testing results showed that the dynamic resilient modulus of red clay can be obviously increased by adding cement or lime (3~9%). In addition, the dynamic resilient modulus increases with an increase in compactness and additive content, and decreases with an increase in moisture content. Testing results also showed that, when the contents of cement and lime in red clay samples were the same, the dynamic resilient modulus of red clay improved with cement was slightly higher compared to that improved with lime. 1.The testing results showed that the dynamic resilient modulus of red clay can be obviously increased by adding cement or lime (3~9%). In addition, the dynamic resilient modulus increases with an increase in compactness and additive content, and decreases with an increase in moisture content. Testing results also showed that, when the contents of cement and lime in red clay samples were the same, the dynamic resilient modulus of red clay improved with cement was slightly higher compared to that improved with lime. 1. The testing results showed that the dynamic resilient modulus of red clay can be obviously increased by adding cement or lime (3~9%). In addition, the dynamic resilient modulus increases with an increase in compactness and additive content, and decreases with an increase in moisture content. Testing results also showed that, when the contents of cement and lime in red clay samples were the same, the dynamic resilient modulus of red clay improved with cement was slightly higher compared to that improved with lime.

2.Through a variance analysis of stress state, it can be concluded that the influence of deviatoric stress, and bulk stress on dynamic resilient modulus are significant, but the influence of deviatoric stress are more obvious. The dynamic resilient modulus of red clay increases with an increase in confining pressure. In addition, it decreases with an increase in deviatoric stress when the confining pressure is invariable, and increases with an increase in bulk stress when the deviatoric stress is 2.Through a variance analysis of stress state, it can be concluded that the influence of deviatoric stress, and bulk stress on dynamic resilient modulus are significant, but the influence of deviatoric stress are more obvious. The dynamic resilient modulus of red clay increases with an increase in confining pressure. In addition, it decreases with an increase in deviatoric stress when the confining pressure is invariable, and increases with an increase in bulk stress when the deviatoric stress is 2. Through a variance analysis of stress state, it can be concluded that the influence of deviatoric stress, and bulk stress on dynamic resilient modulus are significant, but the influence of deviatoric stress are more obvious. The dynamic resilient modulus of red clay increases with an increase in confining pressure. In addition, it decreases with an increase in deviatoric stress when the confining pressure is invariable, and increases with an increase in bulk stress when the deviatoric stress is invariable.

invariable. 3.Three existing prediction models were used to predict the resilient modulus of the improved red clay. A comparison of the predicted results, using these prediction models, indicated that the fitting performance of the Andrei model was the best. The resilient modulus is not only affected by the stress state, but also by the compactness, moisture content, and additive content. Therefore, based on the Andrei models, a new comprehensive prediction model was proposed to reflect the invariable. 3.Three existing prediction models were used to predict the resilient modulus of the improved red clay. A comparison of the predicted results, using these prediction models, indicated that the fitting performance of the Andrei model was the best. The resilient modulus is not only affected by the stress state, but also by the compactness, moisture content, and additive content. Therefore, based on the Andrei models, a new comprehensive prediction model was proposed to reflect the 3. Three existing prediction models were used to predict the resilient modulus of the improved red clay. A comparison of the predicted results, using these prediction models, indicated that the fitting performance of the Andrei model was the best. The resilient modulus is not only affected by the stress state, but also by the compactness, moisture content, and additive content. Therefore, based on the Andrei models, a new comprehensive prediction model was proposed to reflect the influence of compactness, moisture content, additive content and stress state on resilient modulus.

influence of compactness, moisture content, additive content and stress state on resilient modulus.

influence of compactness, moisture content, additive content and stress state on resilient modulus.

By comparing the regression values with the measured values, it can be concluded that the new comprehensive prediction model has a good regression performance. The good performance applies to various additive contents, demonstrating a good potential of engineering application for the proposed prediction model.

**Author Contributions:** H.Y. and Y.W. conceived and designed the experiments; W.L. performed the experiments; H.L. and Y.L. analyzed the data; W.L. wrote the paper.

**Acknowledgments:** This research was funded by the National Natural Science Foundation of China (51874112;51774107), the Open Research Fund of State Key Laboratory of Geo-mechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences (Z013010), the Opening Project of State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology (No. KFJJ19-02M). All financial support is gratefully acknowledged.

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

### **References**


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