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Go to Editorial ManagerMost researches have predicted soil erosion of cohesive riverbanks using linear (excess shear stress model) and non-linear (Wilson model) models based on two soil parameters (detachment coefficient, kd, and critical shear stress, ?c) of the linear model and two soil mechanistic parameters (mechanistic detachment parameter, b0, and threshold parameter, b1) of the non-linear model. The goal of this research was to quantify the soil erodibility parameters of Tigris Riverbanks on Nu’maniyah-Kut Barrage reach using linear and non-linear models through the model parameters at three different water contents: dry side, optimum side, and wet side of water contents. Soil samples were collected from three locations south of Baghdad city on Nu’maniyah-Kut Barrage reach of Tigris Riverbanks. Six soil samples acquired from these sites were laboratory tests achieved using a miniature version of Jet Erosion Test device (“mini” JET) to determine the erodibility parameters of both linear and non-linear models. Blaisdell solution (BL) and scour depth solution (SD) were applied to determine (kd and ?c) of linear model from JETs data. Physical soil characteristics; including bulk density, particle size distribution (sand%, silt%, and clay%), average particle size (D50), and angle of repose were reported for six samples acquired from the three sites. The results showed lower value of kd of toe in compared with bank side for some specific sites as observed for both BL and SD solutions of excess shear stress model especially at wet side of water content. No general pattern of ?c related to different water content were observed. The parameters (b0 and b1) of non-linear model have the same behavior of linear model parameters (kd and ?c), but with different magnitude related to different water contents, respectively.
This study develops a decision-oriented uncertainty quantification methodology for analyzing as-built surface roughness in laser powder bed fusion/direct metal laser sintering (LPBF/DMLS) SS316L. A Taguchi L9 design was used to vary laser power (300-360 W), scan speed (800-1000 mm·s-1), and layer thickness (20-80 μm), producing nine process settings with three independently fabricated specimens per setting, resulting in 27 total specimens. Surface roughness was measured by contact stylus profilometry using arithmetic mean roughness (Ra), root mean square roughness (Rq), and maximum profile height (Rz). The mean roughness varied within narrow ranges, Ra as 5.748-5.952 μm, Rq as 6.673-6.811 μm, and Rz as 28.828-28.892 μm, while within-setting scatter remained non-negligible, particularly for Rz. Probabilistic regression models were evaluated using leave-one-setting-out validation, negative log predictive density, interval coverage, calibration diagnostics, and reliability-driven accept/reject analysis. For Ra and Rq, a low-capacity linear mean model with pooled variance achieved the best predictive density, indicating limited transportable heteroscedastic structure under setting-wise extrapolation. For Rz, a nonlinear mean model with pooled variance performed best. Unregularized two-stage variance learning produced unstable uncertainty estimates, whereas shrinkage regularization improved calibration and reduced spurious setting-dependent variance effects. The decision analysis showed that calibration strongly influences process acceptance, reliability thresholds sharply reduced the number of accepted settings, and shrinkage-stabilized uncertainty produced a conservative and consistent decision frontier. The main contribution of this work is the integration of grouped validation, probabilistic calibration, variance-shrinkage modelling, and reliability-aware decision analysis for surface roughness qualification in LPBF/DMLS SS316L.