Development of Analytical Models to Estimate Pile Setup in Cohesive Soils Based on FE Numerical Analyses

This paper presents the numerical simulation of pile installation and the subsequent increase in the pile capacity over time (or setup) after installation that was performed using the finite element software Abaqus. In the first part, pile installation and the following load tests were simulated numerically using the volumetric cavity expansion concept. The anisotropic modified Cam-Clay and Dracker–Prager models were adopted in the FE model to describe the behavior of the clayey and sandy soils, respectively. The proposed FE model proposed was successfully validated through simulating two full-scale instrumented driven pile case studies. In the second part, over 100 different actual properties of individual soil layers distracted from literature were used in the finite element analysis to conduct parametric study and to evaluate the effect of different soil properties on the pile setup behavior. The setup factor A was targeted here to describe the pile setup as a function of time after the end of driving. The selected soil properties in this study to evaluate the setup factor A include: soil plasticity index (PI), undrained shear strength (Su), vertical coefficient of consolidation (Cv), sensitivity ratio (Sr), and over-consolidation ratio (OCR). The predicted setup factor showed direct proportion with the PI and Sr and inverse relation with Su, Cv and OCR. These soil properties were selected as independent variables, and nonlinear multivariable regression analysis was performed using Gauss–Newton algorithm to develop appropriate regression models for A. Best models were selected among all based on level of errors of prediction, which were validated with additional nineteen different site information available in the literature. The results indicated that the developed model is able to predict the setup behavior for individual cohesive soil layers, especially for values of setup factor greater than 0.10, which is the most expectable case in nature.

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