Understanding slope stability is crucial for effective risk management and prevention of slides. Some deterministic approaches based on limit-equilibrium and numerical methods have been proposed for the assessment of the safety factor (SF) for a given soil slope. However, for risk analyses of slides of earth dams, a range of SFs is required due to uncertainties associated with soil strength properties as well as slope geometry. Recently, several studies have demonstrated the efficiency of artificial neural network (ANN) models in predicting the SF of natural and artificial slopes. Nevertheless, such techniques operate as black-box models, prioritizing predictive accuracy without suitable interpretability. Alternatively, multivariate polynomial regression (MVR) models offer a pragmatic interpretability strategy by combining the analysis of variance with a response surface methodology. This approach overcomes the difficulties associated with the interpretability of the black-box models, but results in limited accuracy when the relationship between independent and dependent variables is highly nonlinear. In this study, two models for a quick assessment of slope SF in earth dams are proposed considering the MVR and the ANN models. Initially, a synthetic dataset was generated considering different soil properties and slope geometries. Then, both models were evaluated and compared using unseen data. The results are also discussed from a geotechnical point of view, showing the impact of each input parameter on the assessment of the SF. Finally, the accuracy of both models was measured and compared using a real-case database. The obtained accuracy was 78% for the ANN model and 72% for the MVR one, demonstrating a great performance for both proposed models. The efficacy of the ANN model was also observed through its capacity to reduce false negatives (a stable prediction when it is not), resulting in a model more favorable to safety assessment.

This paper addresses the issue of crack expansion in adjacent buildings caused by foundation pit construction and develops a predictive model using the response surface method. Nine factors, including the distance between the foundation pit and the building, soil elastic modulus, and density, were selected as independent variables, with the crack propagation area as the dependent variable. An orthogonal test of 32 conditions was conducted, and crack propagation was analyzed using the FEM-XFEM model. Results indicate that soil elastic modulus, Poisson's ratio, and distance between the pit and building significantly impact crack propagation. A predictive model was developed through ridge regression and validated with additional test conditions. Single-factor analysis showed that elastic modulus and Poisson's ratio of the silty clay layer, elastic modulus of sandy soil, and pit distance have near-linear effects on crack propagation. In contrast, cohesion, density, and Poisson's ratio of sandy soil exhibited extremum points, with certain factors showing high sensitivity in specific ranges. This study provides theoretical guidance for mitigating crack propagation in adjacent buildings during excavation.

Although plastic has many desirable properties and numerous social benefits, it is a serious ecological problem due to massive application and difficult decomposing. Various environmental and anthropogenic impacts indicate that plastic breaks down into small particles that are ubiquitous in the environment. Microplastics (MPs) are detected in oceans and seas, freshwater, wastewater, glaciers, soils, air, sediments, precipitation, plants, animals, humans, food and drinking water worldwide. Traces of MPs have been found even in remote and sparsely populated areas, indicating far-reaching movement through environmental compartments. Inadequate waste management and wastewater treatment is considered the major source of MP pollution. MPs are persistent contaminants that can adversely affect the ecological balance of the environment and may damage the health of living organisms, including humans. This review emphasizes the current global problems of MP pollution. It covers different areas of MPs, which include basic characteristics, interactions with other pollutants, occurrence and impacts in the environment, toxic effects on living organisms, sampling, sample pre-treatment and analytical methodology for the identification and quantification of MPs in different matrices as well as potential reduction and remediation strategies and the possibilities for effective control of MPs in the environment. Various interesting and useful previously published knowledge collected in this review can serve as a valuable foundation for further MP research.

Biodegradable plastic is the preferred alternative to traditional plastic products due to its high degradability, decreased dependence on fossil sources, and decreased global pollution according to the accumulation of traditional plastic. In the current study, the optimization of biodegradable plastic synthesis was studied using biomass reinforcement materials. The reinforcement material is cellulose extracted from sawdust to prepare biodegradable plastic using the casting method. Response surface methodology using Box-Behnken Design is used to optimize the main parameters affecting the tensile strength and elongation at the break of the biodegradable plastic. These parameters are cellulose fiber addition, acetic acid addition, and the mass ratio of glycerol to starch. The maximum tensile strength and elongation were obtained at 4.45 MPa and 5.24%, respectively, using 5% cellulose fiber addition and 11.24% acetic acid addition with a 0.266 w/w glycerol to starch mass ratio. Various analyses were performed on the produced biodegradable plastic, including FTIR, SEM, and thermal stability. The biodegradability of the produced biodegradable plastic after immersing the soil for 10 days was about 90% higher than the traditional plastics. The produced biodegradable plastic has a moisture content of 4.41%, water absorption of 81.5%, water solubility of 24.6%, and alcohol solubility of 0%. According to these properties, the produced biodegradable plastic can be used in different industries as a good alternative to traditional plastics.

In order to accurately model the machine-lunar soil simulant interaction, this study combined physical and simulation experiments to calibrate the discrete element simulation parameters of the JLU-H lunar highland simulant. First, the intrinsic parameters and the true angle of repose of the JLU-H were determined through physical tests to provide data for subsequent simulation tests. A Plackett-Burman test was designed to identify and select the parameters that have a significant effect on the angle of repose. The range of values of the significant parameters was then optimized using the steepest climb test. The Box-Behnken test was then utilized to calibrate and obtain the optimal parameter combinations. Finally, a validation test of the angle of repose was conducted using the calibrated DEM parameters. The relative error between the simulation results and the test results was 1.54 %. Then further straight shear tests were conducted to verify the accuracy and validity of the DEM parameters. The results show that the calibrated parameters can provide a reference for the selection of discrete element simulation parameters for lunar soil simulant and the design and optimization of drilling and excavation machinery for lunar exploration. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This study utilizes polymers based on coal gangue and blast furnace slag to solidify engineering slurry with high silt content. Response surface methodology was employed to investigate the effects of polymer composition, alkali activator modulus, and coal gangue calcination temperature on the unconfined compressive strength of stabilized soil. Additionally, the study comprehensively characterized the thermal stability, pore structure, molecular bonds, mineral composition, and micro-morphology of the stabilized soils, and explored the mechanisms governing their strength development. The results demonstrate that the highest strength of stabilized soils is achieved with a slag to coal gangue ratio of 2.5:7.5, a water glass modulus of 1.2, and a coal gangue calcination temperature of 750 degrees C. Formation of calcium-aluminum-silicate-hydrate (C-A-S-H) and sodium-aluminum-silicate-hydrate (N-A-S-H) contributes significantly to the strength development. The presence of slag promotes early strength through C-A-S-H formation, while coal gangue facilitates N-A-S-H formation, supporting later-stage strength development by filling micropores. By applying alkali-activated calcined coal gangue-slag based cementitious materials to solidify engineering slurry, this research not only elucidates the mechanism of alkaliactivated calcined coal gangue-granulated blast furnace slag in slurry solidification but also promotes the utilization of industrial solid waste, providing new insights for environmental protection and resource recovery.

This article proposes an analytical layer element methodology for transversely isotropic (TI) laminated water-saturated subsoils to a harmonically moving load over the media surface. Biot's elastodynamic equation is decoupled by the double Fourier transform combined with the Heaviside step function of the load source. Exact 3D dynamical stiffness matrices of the soil layer element and the TI saturated half-space are established based on the derived displacement and stress components in the transform domain. Assembling the stiffness matrices of the discrete layer elements and underlying half-space yields the total 3D dynamical stiffness matrices. Considering the element boundary conditions, the analytical layer-element solutions in the wavenumber domain are obtained. The space domain result is retrieved by the inverse Fourier transformation. The results have a good consistency with the current solutions. The effects of the soft soil layer thickness, the material anisotropy, stratification characteristics, the load frequency, and the load moving velocity are analyzed.

This study investigated the combined effects of calcium carbide waste (CCW) and lateritic soil (LS) on sustainable concrete's fresh and mechanical properties as a construction material for infrastructure development. The study will explore the possibility of using easily accessible materials, such as lateritic soils and calcium carbide waste. Therefore, laterite soil was used to replace some portions of fine aggregate at 0% to 40% (interval of 10%) by weight, while CCW substituted the cement content at 0%, 5%, 10%, 15%, and 20% by weight. A response surface methodology/central composite design (RSM/CCD) tool was applied to design and develop statistical models for predicting and optimizing the properties of the sustainable concrete. The LS and CCW were input variables, and compressive strength and splitting tensile properties are response variables. The results indicated that the combined effects of CCW and LS improve workability by 18.2% compared to the control mixture. Regarding the mechanical properties, the synergic effects of CCW as a cementitious material and LS as a fine aggregate have improved the concrete's compressive and splitting tensile strengths. The contribution of LS is more pronounced than that of CCW. The established models have successfully predicted the mechanical behavior and fresh properties of sustainable concrete utilizing LS and CCW as the independent variables with high accuracy. The optimized responses can be achieved with 15% CCW and 10% lateritic soil as a substitute for fine aggregate weight. These optimization outcomes produced the most robust possible results, with a desirability of 81.3%.

Landslides pose significant impact on human life and society such as loss of livelihood, destruction of infrastructure, and damage to natural resources around the world. Due to existing complications in conditional factors of landslide, mapping and predicting landslide occurrences with high accuracy needs more attention. In light of this, we aim to develop an ensemble landslide susceptibility model named as support vector regression-grasshopper optimization algorithm (SVR-GOA). This model is validated along with other landslide susceptibility models such as artificial neural network (ANN), boosted regression tree (BRT), and elastic net models. The present study carried out over the Kalaleh Basin in Iran, in which we selected 140 landslides with 16 conditional factors to construct a geographic database of the region. The multicollinearity analysis was done on the hazard conditioning factors using variance inflation factor and tolerance indices. Similarly, significance of these factors and their association with selected locations were identified through random forest method. The state of the art of the study is implementing SVR-GOA in landslide susceptibility mapping, including this model we use other landslide models such as ANN, BRT, and elastic net for validation and development using the area under the curve (AUC), kappa, and root mean squared error values. Our results show lithology, slope degree, rainfall, topography position index, topography wetness index, surface area, and landuse/landcover were found to be the most influential conditioning factors. We have also observed that, despite accurate prediction, SVR-GOA outperforms the others by showing the highest AUC values around AUC = 0.930 and others show ANN (AUC = 0.833), BRT (AUC = 0.822) and elastic net (AUC = 0.726) respectively. This innovative approach to landslide mapping using SVR-GOA ensembles would enhance the advancement of landslide research at multiple scales. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Caffeine, a significant naturally occurring alkaloid in beverages like tea and coffee, can be degraded by bacteria. Prolonged caffeine consumption can stimulate adrenal glands, cause irregular muscle activity, cardiac arrhythmias, and withdrawal symptoms such as headaches and fatigue. Beyond its health-related concerns, the environmental impact of caffeine degradation is noteworthy. Effluents from coffee industries contain high caffeine concentrations, and the discharge of such effluents into lakes poses a risk to the portability of drinking water. This study isolated a novel bacterium from agricultural soil, identified as Bacillus sp. KS38 through 16 S rRNA gene sequencing, which can metabolize caffeine as the sole carbon and nitrogen source. The bacterium exhibited Gram-positive characteristics. Response surface methodology (RSM) optimized bacterial growth conditions. The relevant parameter for the degradation of caffeine was obtained by first screening the parameters using the Plackett-Burman design. Using central composite design (CCD) and RSM, the important parameters were determined to achieve the ideal degradation conditions. The identified the ideal degradation conditions: 0.66 g/L caffeine, 0.85 g/L glucose, pH 6.83, and 20.5 degrees C. RSM predicted a bacterial growth of 0.591, which was confirmed experimentally. This bacterium has potential applications in wastewater treatment and caffeine bioremediation.