LAUSR

Abstract In this study, gene expression programming (GEP) and multi gene expression programming (MEP) are utilized to formulate new prediction models for determining the compaction parameters (ρdmax and wopt) of expansive soils. A total of 195 datasets with five input parameters (i.e., clay fraction CF, plastic limit wP, plasticity index IP, specific gravity Gs, maximum dry density ρdmax), and two output variables ρdmax and wopt are collected from the literature comprising 119 internationally published research articles to develop the GEP and MEP models. Simplified mathematical expressions were derived for these models to determine the ρdmax and wopt of expansive soils. The performance of the models was tested using mean absolute error (MAE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). Sensitivity and parametric analyses were also performed on the GEP and MEP models. Additionally, external validation of the models was also verified using commonly recognized statistical criteria. It is clear from the results that the GEP and MEP methods accurately characterize the compaction characteristics of expansive soils resulting in reasonable prediction performance, however, GEP model yielded relatively better performance. Also, the proposed predictive models were compared with previously available empirical models and they exhibited robust and superior performance. Moreover, the ρdmax model provided significantly improved results as compared to the wopt prediction model in the case of GEP, and vice versa in the MEP model. It is therefore recommended that the proposed GP based models can reliably be used for determining the compaction parameters of expansive soils which effectively reduces the time-consuming and laborious testing, hence attaining sustainability in the field of geo-environmental engineering.