# Resilient Modulus--Physical Parameters Relationship of Improved Red Clay by Dynamic Tri-Axial Test
###### 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. red clay; additive; resilient modulus; prediction models; new comprehensive prediction model +
Footnote †: journal: Applied Sciences  
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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; 26; 27; 28; 29]. The concept of dynamic resilient modulus of subgrade soil was introduced by [PERSON] 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; 32; 33; 34; 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; 37; 38; 39; 40; 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; 43; 44; 45; 46; 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; 49; 50]. 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; 36; 37; 38], or combine a single physical parameter to predict the resilient modulus [43; 44].