Evolution Prediction of Hysteresis Behavior of Sand under Cyclic Loading

Soil cyclic degradation is a serious issue that should be considered in engineering design and maintenance. The hysteretic response causes strength degradation and excessive settlement of soil structure in engineered and natural geosystems. Hysteresis is essentially the coupling deformation of elastic and plastic components during reloading and unloading processes. Conventional hysteretic models for sand in the elastoplastic framework rely highly on yield surface or potential surface evolution and fall short on complexity and inaccuracy. This study proposes a decoupling method to describe the hysteretic response of sand. In contrast to the conventional elastoplastic approach, this decoupling method can directly decouple the elastic and plastic components by determining the boundary between the elastic strain extension domain and the plastic strain extension domain for each stress cycle. In this way, the elastic and plastic stress–strain branches during cyclic loading can be separately obtained, and the corresponding elastic and plastic parameters are employed to characterize mechanical behavior. With the respective evolution of elastic and plastic strains, the hysteretic behavior of sand is reproduced by combining all the branches. Finally, this decoupling method is validated by three conventional cyclic loading tests. The predictions are consistent with the experiments, indicating that the decoupling method is generally effective in describing the hysteretic behavior under cyclic loading. This decoupling method provides new insight to obtain elastic and plastic deformation behaviors separately, without recourse to complicated plastic surface and hardening law

Contourlet textual features: Improving the diagnosis of solitary pulmonary nodules in two dimensional ct images

Materials and Methods: A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data.Results: Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93.Objective: To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. Copyright:Conclusion: Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer

T112

‘T’ series spreadsheets are the raw data of all tests. (The data in this series are overall stress and strain information). Supporting informatio

DEM

‘DEM’ spreadsheet is the simulation results. The test information of the numerical samples is included in it. Supporting informatio

T144

‘T’ series spreadsheets are the raw data of all tests. (The data in this series are overall stress and strain information). Supporting informatio

T111

‘T’ series spreadsheets are the raw data of all tests. (The data in this series are overall stress and strain information). Supporting informatio

T514

‘T’ series spreadsheets are the raw data of all tests. (The data in this series are overall stress and strain information). Supporting informatio

T134

‘T’ series spreadsheets are the raw data of all tests. (The data in this series are overall stress and strain information). Supporting informatio

T114

‘T’ series spreadsheets are the raw data of all tests. (The data in this series are overall stress and strain information). Supporting informatio

PF series

‘PS’ and ‘PF’ spreadsheets are the data of the prediction and calibration tests. (The data in this series are overall stress and strain information). Supporting informatio