## 1 Introduction  
The compressibility behavior of soil is a crucial parameter in geotechnical engineering, which is mandatory to investigate during the design phase of various construction projects. This behavior dictates the change in soil volume under applied stress [1]. Particularly for housing projects, in order to forecast the settlement of building foundations, the compressibility of soil needs to be estimated as accurately as possible, because unforeseen or excessive settlement inevitably causes cracks in buildings and even threatens their stability [2; 3]. Damage to buildings and building collapses due to unanticipated settlements are widely reported in many countries [4; 5; 6; 7]. The soil compression is generally defined as the decrease in volume of the soil under pressure caused by the drainage of pore water [8]. Hence, the analyses on the compressibility of soil are basically required for fine-grained soils having low permeability. It is also noted that the process of determining the soil compressibility for fine-grained soils is relatively tricky, because these soils have low permeability and their rate of pore water drainage is a complex time-dependent phenomenon [8]. To quantify the compressibility of soil, researchers and engineers have used the compression coefficient (CC) [9; 10; 11; 12]. This parameter is practically employed in the process of estimating the settlement of soil layers in constructed areas. The quality of soil settlement estimation heavily depends on the accuracy of the CC prediction. In practice, this important property of soil is computed using the oedometer test performed in a laboratory [13], however, the process of obtaining CC via the oedometer test is recognizably time-consuming and costly [14]. More importantly, experienced and skilled technicians are required so that the test results are reliable. In this regard, mathematical models in the form of predictive equations have been developed based on experimental laboratory data as cost-effective approaches for estimating CC [15; 16; 17; 18; 19], however, the accuracy result obtained from those predictive formulas is often restricted due to their lack of generalization capability, inability to describe multivariate relationships, as well as a limited amount of collected data [20]. Therefore, predictive equations are not sufficient for deriving accurate prediction models used for CC estimation, and therefore new efficient solutions for the CC prediction should be developed to meet the current demand for practicing engineers. Recently, advanced artificial intelligence has successfully been adopted for solving complex modeling problems in various geotechnical engineering applications, i.e., predictions of soil strength [21; 22; 23], shear wave velocity [24], peak shear strength of fiber-reinforced soils [25], soil liquefaction [26; 27], pile-soil interactions [28; 29], and groutability of granular soils [30; 31]. The main merit of these artificial intelligence approaches is the high prediction capability and the ability to deal with multivariate data [32; 33]. A remarkable review of artificial intelligence in geotechnical engineering can be founded in [PERSON] [34]. Regarding the CC estimation, several artificial intelligence algorithms have been considered in various real-world projects. [PERSON] and [PERSON] [35] showed that the neural network provides a higher performance as compared with those of empirical formulas. [PERSON] et al. [17] proposed a neural network for predicting soil CC and applied it to forecast the settlement of fine-grained soils. The result showed that the predicted values were close to the experimental values. [PERSON] et al. [8] developed a multi-gene expression programming model for computing CC. The multi-gene expression programming can automatically identify predictive equations that provide an excellent fit to the employed data set, however, the physical meaning of these equations is challenging to interpret.