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The stress-strain behavior of Ottawa F65 sand is investigated through an extensive series of constant volume stress-controlled cyclic direct simple shear (CDSS) tests performed at different densities, overburden pressures, and static shear stresses prior to cyclic shearing to quantify their effects on the cyclic strength of Ottawa F65 sand. Results of the CDSS tests are used in the constitutive model calibration exercise for the Liquefaction Experiments and Analysis Project (LEAP-2022). The collected database of CDSS tests is used to develop an Artificial Neural Network (ANN) model capable of predicting Ottawa F65 liquefaction strength for a specified set of relative density, overburden pressure, static shear stress ratio, and cyclic shear stress ratio. After training, validation and testing, the ANN model is further assessed using blind prediction of the liquefaction strength in new CDSS tests for a relative density and overburden stress that are not available in the training dataset. CDSS tests under similar conditions were then carried out in the laboratory for validation of the ANN model. The comparisons of the predictions with the experimental results have demonstrated the ANN model predictive capability for liquefaction strength and its sensitivity to changes in relative density, overburden stress and cyclic stress ratio. 

The stress-strain behavior and liquefaction strength of Ottawa F65 sand was investigated through an extensive series of cyclic direct simple shear (CDSS) tests. The study quantified the effects of static shear stress on the cyclic strength of Ottawa F65 sand. The database of CDSS tests was used to develop an Artificial Neural Networks model to predict Ottawa F65 cyclic strength. 

The stress-strain behavior and liquefaction strength of Ottawa F65 sand was investigated through an extensive series of cyclic direct simple shear (CDSS) tests. The study quantified the effects of relative density on the cyclic strength of Ottawa F65 sand. The database of CDSS tests was used to develop an Artificial Neural Networks model to predict Ottawa F65 cyclic strength. 

The stress-strain behavior and liquefaction strength of Ottawa F65 sand was investigated through an extensive series of cyclic direct simple shear (CDSS) tests. The study quantified the effects of overburden stress on the cyclic strength of Ottawa F65 sand. The database of CDSS tests was used to develop an Artificial Neural Networks model to predict Ottawa F65 cyclic strength.