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Pre-compression stress (σ pc ) has been widely used as a criterion for assessing the susceptibility of soil to compaction. Confined compression (CCT), semi-confined compression (SCCT) and plate sinkage (PST) tests are the most practical procedures to estimate σ pc . Discrepant estimates of σ pc may be obtained from the above tests mainly due to differences in boundary conditions. The aim of this study was to simulate these compaction tests using the finite element method (FEM) to compare σ pc estimated from stress-strain curves with the simulated yield stress. It was hypothesized that for a given simulated yield stress, σ pc estimated either at the point of maximum curvature or the point of Casagrande's method is not the same as simulated yield stress for different tests. FEM models were developed in ABAQUS with elastic-perfectly plastic law and Drucker-Prager yield criterion for the soil material. Yield stresses of 20, 50, 75, 100, 125, 150, 200 and 300 kPa were examined. The results showed that the stress at the point of maximum curvature was close to the simulated yield stress in CCT but smaller than that in SCCT and PST. Casagrande's method overestimated σ pc (averagely by 40%) for all the tests. A more precise estimate of σ pc was obtained at the point of maximum curvature than the Casagrande’s method in experimental CCT and PST on remolded soils. Cyclic loading-unloading CCT and PST to different stress levels showed that a safe threshold for preventing a severe plastic strain is significantly smaller (with a factor of 0.5 and 0.8 for CCT and PST, respectively) than σ pc in either of the tests. [ABSTRACT FROM AUTHOR]

Rapid and non-destructive adulteration detection is of particular importance to oil industries. This paper presents an application of visible and near-infrared (VNIR) spectroscopy for detection of adulteration levels in olive oil. Sunflower oil was used as an adulterant to olive oil, and the adulteration samples with different levels ranging from 0% to 40% were prepared and used for the experiments. The spectra were first considered in the range of 500-900 nm and then smoothened and normalized to reduce the light scattering effects. Principal component analysis (PCA) was performed on the spectra to have a primary data visualization and feature extraction. The extracted PCA scores were used to calculate the Mahalanobis distances of the adulterated samples from the pure sample. Further, the PCA scores were fed to the multi-class support vector machine (SVM) model to perform classification on the basis of different adulteration levels. The results showed that the spectral normalization highlighted different regions over the spectrum affected due to the adulteration. The PCA score biplots showed differences in the samples based on the different amounts of the adulteration. Moreover, the Mahalanobis distance provided a quantitative measure of the differences between the adulterated oil and the pure oil samples. The SVM modelling further supported the classification of the different levels of the adulteration. Consequently, the VNIR spectroscopy in combination with the SVM could support the development of the classification protocols for detection of adulteration in olive oils. [ABSTRACT FROM AUTHOR]