Sensitivity-Driven Evolutionary Polynomial Regression for Tropical Subgrade Permanent Deformation

Abstract

This study developed an interpretable Evolutionary Polynomial Regression framework to predict permanent deformation of tropical soil subgrades in semi-rigid pavement structures. The database combined repeated-load triaxial test parameters reported in Brazilian studies with controlled mechanistic-empirical simulations representing traffic demand, structural thickness, subgrade Poisson’s ratio, and soil properties. Candidate equations were generated through a hybrid evolutionary search combining Genetic Algorithm and Differential Evolution, and final models were selected by jointly considering statistical performance, parsimony, and Monte Carlo sensitivity consistency. Six predictors were retained: number of axle-load repetitions, percentage passing the No. 200 sieve, optimum moisture content, clayeyness coefficient, laterization index, and equivalent pavement thickness. Model assessment used three repeated random train-test partitions. The selected equations contained three polynomial terms and achieved testing coefficient-of-determination values from 0.943 to 0.951, root mean square errors from 0.214 to 0.238 mm, and mean absolute errors from 0.163 to 0.169 mm. Sensitivity and Shapley analyses showed physically consistent trends, including increased deformation with traffic loading and laterization index, and reduced deformation with equivalent pavement thickness.