Developing Predictive Models of Collapse Settlement and Coefficient of Stress Release of Sandy-Gravel Soil via Evolutionary Polynomial Regression

Abstract

The collapse settlement of granular soil, which brings about considerable deformations, is an important issue in geotechnical engineering. Several factors are involved in this phenomenon, which makes it difficult to predict. The present study aimed to develop a model to predict the collapse settlement and coefficient of stress release of sandy gravel soil through evolutionary polynomial regression (EPR). To achieve this, a dataset containing 180 records obtained from a large-scale direct shear test was used. In this study, five models were developed with the secant hyperbolic, tangent hyperbolic, natural logarithm, exponential, and sinusoidal inner functions. Using sand content (SC), normal stress (σn), shear stress level (SL), and relative density (Dr) values, the models can predict the collapse settlement (∆H) and coefficient of stress release (CSR). The results indicated that the models developed with the exponential functions were the best models. With these models, the values of R2 for training, testing, and all data in the prediction of collapse settlement were 0.9759, 0.9759, and 0.9757, respectively, and the values of R2 in predicting the coefficient of stress release were 0.9833, 0.9820, and 0.9833, respectively. The sensitivity analysis also revealed that the sand content (SC) and relative density (Dr) parameters had the highest and lowest degrees of importance in predicting collapse settlement. In contrast, the Dr and SC parameters showed the highest and lowest degrees of importance in predicting the coefficient of stress release. Finally, the conducted parametric study showed that the developed models were in line with the results of previous studies.