The next-generation constitutive correlations for simulation of cyclic stress-strain behaviour of sand

This paper presents an innovate approach to simulate the stress-strain behaviour of sands subjected to large amplitude regular cyclic loading. New prediction correlations were derived for damping ratio (D) and shear modulus (G) of sand utilizing linear genetic programming (LGP) methodology. The correlations were developed using several cyclic torsional simple shear test results. In order to formulate D and G, new equations were developed to simulate hysteresis strain–stress curves and maximum shear stress (τmax) at different loading cycles. A genetic algorithm analysis was per­formed to optimize the parameters of the proposed formulation for stress-strain relationship. A total of 746 records were extracted from the simple shear test results to develop the τmax predictive model. Sensitivity and parametric analyses were conducted to verify the results. To investigate the applicability of the models, they were employed to simulate the stress-strain curves of portions of test results that were not included in the analysis. The LGP method precisely charac­terizes the complex hysteresis behaviour of sandy soils resulting in a very good prediction performance. The proposed design equations may be used by designers as efficient tools to determine D and G, specifically when laboratory testing is not possible

Cloud detection based on high resolution stereo pairs of the geostationary meteosat images

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future