# CBR Predictive Models for Granular Bases Using Physical and Structural Properties
## 1 Introduction  
The pavement is a structure formed by several soil layers designed to provide and maintain a smooth surface for several applications. It requires supporting and distributing the stresses as well as minimizing permanent deformations on it. In general terms, the structure is formed by the main pavement layer, the base, and the sub-base, which are built on a prepared subgrade surface [1,2]. In addition, the pavement structure can be constructed using reinforced concrete or simply asphalt emulsion. Among the main factors affecting the performance and quality of pavements are the mechanical and hydraulic characteristics of materials employed for each layer, climatic conditions, equipment and technology used in the site, and the skills of workmen involved in the construction. Due to these and other factors, it is complicated to provide quality control schemes in the field of engineering of pavements [3]. Hence, it is important to check and verify certain parameters used in pavements during their construction. Otherwise, the predictions made for the durability and serviceability of pavements will not be realistic, affecting the costs of maintenance and rehabilitation [4]. The design of pavements requires the knowledge of soil mechanics and specifically the behavior of compacted soils. This discipline establishes the laboratory and field tests required to evaluate the quality of compacted layers as well as the needed conditions in terms of durability and serviceability of a pavement subjected to certain loading conditions. In general, field and laboratory tests must meet the following requirements: (a) simple and standardized, (b) swift, (c) easy to interpret, and (d) use inexpensive tools easy to calibrate and use [5]. It is well-documented in the literature that granular soils are the most common materials used for the construction of bases for pavements [2]. These soils can be mixed with lime, asphalt, or other chemical products to increase its strength and reduce deformations [2]. Then, granular compacted soil layers are stiff, with large hydraulic conductivity, and show low deformations when subjected to cyclic loading [6]. When such materials are not well-compacted, or the strength of aggregates is deficient, fissuring and large permanent deformations occur on the pavement. Because of this and some other reasons, granular materials used on bases and subbases of pavements require a previous and rigorous evaluation. One of the tests employed for this purpose is the California bearing ratio (CBR) test [7]. Although pavements design has evolved in the past fifteen years, and CBR tests have been displaced by cyclic triaxial tests to define the resilient modulus of soil, these last tests are time-consuming and require specialized and expensive equipment. This is one of the reasons why CBR tests are still in use, especially in developing countries [8]. In general, the CBR test is a strength index used for the design of pavements that provides the structural capacity of the different layers of soil employed in the construction of bases, subbases, and subgrades. It can be described as a loading-deformation test that can be performed in the field or laboratory. The results of such a test are used to define the thickness of the different layers of a paved surface, depending on the loading conditions. This value depends on the compaction method and the type of soil. For the supervision of the quality of compacted layers on the field, it is normal to perform these tests on unsaturated soil samples [9; 10]. In addition, it is important to mention that the CBR test is frequently time-consuming and burdensome. Its results are affected by soil disturbance and test conditions [5]. Because of this, it is important to implement models that are both reliable and easy to use. If properly generated, the models presented in this research may substitute or complement the CBR tests. Hence, such models are based on correlations between physical and structural properties.