Stress-strain behavior of Ottawa sand in cyclic direct simple shear and modeling of cyclic strength using Artificial Neural Networks

• Published in: Soil dynamics and earthquake engineering (1984) Vol. 164; p. 107585
• Main Authors: Lbibb, Sarra, Manzari, Majid T.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2023
• Subjects: Artificial neural networks; Cyclic direct simple shear tests (CDSS); Liquefaction strength; Ottawa F65 sand; Ottawa F65 sand; Liquefaction strength; Cyclic direct simple shear tests (CDSS); Artificial neural networks
• ISSN: 0267-7261; 1879-341X
• DOI: 10.1016/j.soildyn.2022.107585

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 Networks (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. •Multiple series of constant volume cyclic direct simple shear tests on Ottawa sand.•Study of effects of relative density, overburden stress, and static shear stress.•Artificial Neural Networks (ANN) used for the prediction of liquefaction strength.•Predictive capability of ANN through blind predictions of liquefaction strength.