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In the present study, the seismic performances of gravity retaining walls having both inclined back side and inclined backfill were investigated under sinusoidal acceleration excitations using series of shaking table tests on 750 mm height physical model. The effects of input peak ground acceleration (𝑃𝐺𝐴), inclination angle of backfill material (𝛼) and inclination angle of back of the gravity retaining wall (𝛽) on acceleration amplification factor (𝑅𝑀𝑆𝐴), maximum peak lateral relative (𝑆 maxpeak(𝑟𝑒𝑙) ) and maximum residual lateral displacement (𝑆 max(𝑟𝑒𝑠) ) of the wall, surface settlement (𝑆 𝑠𝑒𝑡 ) of the backfill material, inertial force (𝑃 𝐼 ) and horizontal dynamic active force (𝑃 𝑑𝑦𝑛ℎ ) were assessed. It was observed that higher values of the 𝑅𝑀𝑆𝐴 were obtained from the experimental results as compared the ones from current seismic design codes. Moreover, the six results of shaking table tests revealed that the phase difference was appeared between the inertial force and dynamic earth pressures. Pseudo-static limit equilibrium methods resulted in over conservative 𝑃 𝑑𝑦𝑛ℎ results and could not truly reflect the seismic behavior of gravity wall due to the inertial forces and phase difference not taken into consideration.

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The aim of this study is to predict the undrained shear strength (Cu) of the remolded soil samples and for this purpose, non-linear regression (NLR) analyses, fuzzy logic (FL) and artificial neural network (ANN) modelling were used to assess. Total 1306 undrained shear strength results of soil types of CH, CL, MH and ML from 230 different remolded soil test settings on 21 publications were collected while six different measurement devices were used by researchers. Although water content, plastic limit and liquid limit were used as input parameters for FL and ANN modelling, liquidity index or water content ratio were considered as input parameter for NLR analyses. In NLR analyses, 12 different regression equations were derived for prediction of Cu. Feed-Forward backpropagation and TANSIG transfer function were used for ANN modelling while Mamdani inference system was preferred with trapezoidal and triangular membership function for FL modelling. The experimental results of 914 tests for training of the ANN models, 196 for validation and 196 for testing were used. It was observed that the accuracy of the ANN and FL modellings were higher than NRL analyses. Furthermore, the simple and reliable regression equation was proposed for assessments of Cu values having higher coefficient of determination values (R2).