Post-cyclic behavior of Haikou marine clay and their predictions using back-propagation neural network model

Abstract

The evaluation of post-cyclic behavior of soft clay is critical for the lifetime safety and performance assessment of various infrastructures built on/in it. In this study, by employing the marine clays collected from Haikou, China, a series of both conventional shear tests and post-cyclic shear tests under undrained condition were performed using triaxial setup to investigate the post-cyclic behavior of the clay, with the investigation foci placed on the post-cyclic shear stress and secant modulus. Some important factors, namely confining pressure, loading frequency, cyclic shear strain amplitude and post-cyclic shear strain, were taken into consideration. From both the results from the tests and the parameter sensitivity analysis using random forest method, the confining pressure and cyclic shear strain amplitude were found to be evidently influential on the post-cyclic behavior of the clay, while the influence of loading frequency was relatively insignificant. In addition, an intelligent model was established based on the back-propagation neural network method to predict the post-cyclic behavior of Haikou marine clay, which was proven to be more favorable as compared to other two prediction methods developed based on multi-variate regression analysis and random forest algorithm.