Strength criterion for cross-anisotropic sand under general stress conditions

By incorporating the fabric effect and Lode’s angle dependence into the Mohr–Coulomb failure criterion, a strength criterion for cross-anisotropic sand under general stress conditions was proposed. The obtained criterion has only three material parameters which can be specified by conventional triaxial tests. The formula to calculate the friction angle under any loading direction and intermediate principal stress ratio condition was deduced, and the influence of the degree of the cross-anisotropy was quantified. The friction angles of sand in triaxial, true triaxial, and hollow cylinder torsional shear tests were obtained, and a parametric analysis was used to detect the varying characteristics. The friction angle becomes smaller when the major principal stress changes from perpendicular to parallel to the bedding plane. The loading direction and intermediate principal stress ratio are unrelated in true triaxial tests, and their influences on the friction angle can be well captured by the proposed criterion. In hollow cylinder torsional shear tests with the same internal and external pressures, the loading direction and intermediate principal stress ratio are related. This property results in a lower friction angle in the hollow cylinder torsional shear test than that in the true triaxial test under the same intermediate principal stress ratio condition. By comparing the calculated friction angle with the experimental results under various loading conditions (e.g., triaxial, true triaxial, and hollow cylinder torsional shear test), the proposed criterion was verified to be able to characterize the shear strength of cross-anisotropic sand under general stress conditions.     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Enhancement of removal efficiency of ammonia nitrogen in sequencing batch reactor using natural zeolite

Two sets of lab-scale sequencing batch reactors (SBR), i.e., control SBR and SBR using zeolite as carrier (zeo-SBR), were applied to assess nitrogen removal efficiency. The test results revealed that zeolite powder added in SBR could improve its performance. Due to the combination of zeolite adsorption for NH₄ ⁺–N and enhanced simultaneous nitrification and de-nitrification (SND), a higher removal ratio of ammonia nitrogen in wastewater was observed in the test reactor, and the introduction of zeolite powder was helpful to inhabit sludge bulging comparing with the control SBR, in other words, activated sludge immobilized by zeolite powder could remove NH₄ ⁺–N, COD, and PO₄ significantly in a shorter cycle time. Applied two hydraulic retention times (HRTs) showed that the nitrogen and phosphorus removal could be improved while adapting to load variations.     

Large aftershocks triggering by Coulomb failure stress following the 2001<Emphasis Type="Italic">M</Emphasis><Subscript>S</Subscript>=8.1 great Kunlun earthquake

The great Kunlun earthquake occurred on Nov. 14, 2001 in Qinghai Province, China. Five large aftershocks with magnitude larger than 5.0 occurred near the Kunlun fault after main shock. Calculations of the change in Coulomb failure stress reveal that 4 of 5 large aftershocks occurred in areas with Δσ_f>0 (10^?2–10^?1 MPa) and one aftershock occurred in an area with Δσ_f=?0.56 MPa. It is concluded that the permanent fault displacement due to the main shock is the main cause of activity of large aftershocks, but not the whole cause.     

Computing the Sensitivity Kernels for 2.5-D Seismic Waveform Inversion in Heterogeneous,Anisotropic Media

2.5-D modeling and inversion techniques are much closer to reality than the simple and traditional 2-D seismic wave modeling and inversion. The sensitivity kernels required in full waveform seismic tomographic inversion are the Fréchet derivatives of the displacement vector with respect to the independent anisotropic model parameters of the subsurface. They give the sensitivity of the seismograms to changes in the model parameters. This paper applies two methods, called ‘the perturbation method’ and ‘the matrix method’, to derive the sensitivity kernels for 2.5-D seismic waveform inversion. We show that the two methods yield the same explicit expressions for the Fréchet derivatives using a constant-block model parameterization, and are available for both the line-source (2-D) and the point-source (2.5-D) cases. The method involves two Green’s function vectors and their gradients, as well as the derivatives of the elastic modulus tensor with respect to the independent model parameters. The two Green’s function vectors are the responses of the displacement vector to the two directed unit vectors located at the source and geophone positions, respectively; they can be generally obtained by numerical methods. The gradients of the Green’s function vectors may be approximated in the same manner as the differential computations in the forward modeling. The derivatives of the elastic modulus tensor with respect to the independent model parameters can be obtained analytically, dependent on the class of medium anisotropy. Explicit expressions are given for two special cases—isotropic and tilted transversely isotropic (TTI) media. Numerical examples are given for the latter case, which involves five independent elastic moduli (or Thomsen parameters) plus one angle defining the symmetry axis.     

Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir

At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine (LSSVM) algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine (SLSSVM) model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor (SKNN) method. The new semisupervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.     

The Wadden Sea in transition - consequences of sea level rise

The impact of sea level rise (SLR) on the future morphological development of the Wadden Sea (North Sea) is investigated by means of extensive process-resolving numerical simulations. A new sediment and morphodynamic module was implemented in the well-established 3D circulation model GETM. A number of different validations are presented, ranging from an idealized 1D channel over a semi-idealized 2D Wadden Sea basin to a fully coupled realistic 40-year hindcast without morphological amplification of the Sylt-Rømøbight, a semi-enclosed subsystem of the Wadden Sea. Based on the results of the hindcast, four distinct future scenarios covering the period 2010–2100 are simulated. While these scenarios differ in the strength of SLR and wind forcing, they also account for an expected increase of tidal range over the coming century. The results of the future projections indicate a transition from a tidal-flat-dominated system toward a lagoon-like system, in which large fractions of the Sylt-Rømøbight will remain permanently covered by water. This has potentially dramatic implications for the unique ecosystem of the Wadden Sea. Although the simulations also predict an increased accumulation of sediment in the back-barrier basin, this accumulation is far too weak to compensate for the rise in mean sea level.     

Damage and recovery of the Gurbantunggut Desert vegetation following engineering activities

The fragile ecological environment of the Gurbantunggut Desert is damaged/disturbed by human activities relating to the development of oil-gas resources and the constructions of desert road and great engineering in the Jungger Basin. It was mainly represented: soil compaction, vegetation cleaning, burial of vegetation, oil polluting, and soil disturbance. With investigation and experiment, we found that when the way and intensity of engineering activities disturbing the eco-environment does not make its ecological stability disintegrated, the desert vegetation has a capacity of natural recovery. To speed and strengthen the process of vegetation recovery efficient assistant measurements, including stabilizing mobile sands promptly and sowing seeds of shrub and herb plants in good time will be needed.     

Effect of abutment modeling on the seismic response of bridge structures

Abutment behavior significantly influences the seismic response of certain bridge structures. Specifically in the case of short bridges with relatively stiff superstructures typical of highway overpasses, embankment mobilization and inelastic behavior of the soil material under high shear deformation levels dominate the response of the bridge and its column bents. This paper investigates the sensitivity of bridge seismic response with respect to three different abutment modeling approaches. The abutment modeling approaches are based on three increasing levels of complexity that attempt to capture the critical components and modes of abutment response without the need to generate continuum models of the embankment, approach, and abutment foundations. Six existing reinforced concrete bridge structures, typical of Ordinary Bridges in California, are selected for the analysis. Nonlinear models of the bridges are developed in OpenSees. Three abutment model types of increasing complexity are developed for each bridge, denoted as roller, simplified, and spring abutments. The roller model contains only single-point constraints. The spring model contains discrete representations of backfill, bearing pad, shear key, and back wall behavior. The simplified model is a compromise between the efficient roller model and the comprehensive spring model. Modal, pushover, and nonlinear dynamic time history analyses are conducted for the six bridges using the three abutment models for each bridge. Comparisons of the analysis results show major differences in mode shapes and periods, ultimate base shear strength, as well as peak displacements of the column top obtained due to dynamic excitation. The adequacy of the three abutment models used in the study to realistically represent all major resistance mechanisms and components of the abutments, including an accurate estimation of their mass, stiffness, and nonlinear hysteretic behavior, is evaluated. Recommendations for abutment modeling are made.     

The mean value concept in mono-linear regression of multi-variables and its application to trace studies in geosciences

The "mean value concept" in a mono-linear regression of multi-variables is clarified.Its applications to reconstruction of the past 90-year salinity of the sea surface water in Xisha Islands and to tracing the past 80 ka paleo-geomagnetic events from the Luochuan loess 10Be record are introduced in detail,which show the significance and the potential of the "mean value concept" in geoscience research.