Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions

California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of four efficient soft computing techniques, namely multivariate adaptive regression splines with piecewise linear models (MARS-L), multivariate adaptive regression splines with piecewise cubic models (MARS-C), Gaussian process regression, and genetic programming. For this purpose, a wide range of experimental results of soaked CBR was collected from an ongoing railway project of Indian Railways. Three explicit expressions are proposed to estimate the CBR of soils in soaked conditions. Separate laboratory experiments were performed to evaluate the generalization capabilities of the developed models. Furthermore, simulated datasets were used to validate the feasibility of the best-performing model. Experimental results reveal that the proposed MARS-L model attained the most accurate prediction (R-2 = 0.9686 and RMSE = 0.0359 against separate laboratory experiments) in predicting the soaked CBR at all stages. Based on the accuracies attained, the proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects.