Prediction of seasonal frost heave behavior in unsaturated soil in northeastern China using interactive factor analysis with split-plot experiments and GRNN

Abstract

Few studies of frost heave mechanisms have consideredmultifactor interactions, particularly in unsaturated saline soils typical of northeastern China. We collected soil samples in western Jijin Province and assessed their potential frost heave behavior with reference to four controllable factors: soluble salt content (CSS), compactness (C), temperature (T), and water content (WC) using a two-level split-plot experiment. The resulting frost heave ratio was between -0.6% and 2.1%. Analysis of variance showed that water content, compactness, and temperature had significant effects on frost heave behavior, with water content having the strongest correlation (factor coefficient of 0.82), while content of soluble salt (CSS) had no significant effect. The interaction factors (products of single factors) CSS × WC and C × WC had significant effects on frost heave behavior. A correlation analysis using these interaction factors with experimental data drawn from previous research showed results consistent with the improved frost heave experiment as the significant effects of single factors on frost heave behavior ranked from WC > C > T and the interaction factors CSS × WC and C × WC gain had significant effects. We then established two generalized regression neural network (GRNN) models based on the single and interaction factors in order to predict frost heave behavior, showing that adding the latter to the input dataset improved the model accuracy. Thus, future research on predicting frost heave behavior in unsaturated saline soils should consider multiple interacting factor for greater accuracy.