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Uneven of filling aggregate gradation may cause transportation problems such as pipe blockage due to segregation and stratification of filling slurry. To study the influence of aggregate gradation on the conveying performance of filler slurry, aggregate gradation experiments were carried out, rheological tests on slurries with coal gangue/aeolian sand ratios (6:4, 5:5 and 4:6) showed that appropriately increasing the proportion of aeolian sand can improve particle gradation. Computational fluid dynamics (CFD) scheme was used to simulate the pipeline transportation characteristics of the slurry under the conditions of three sets each of coal gangue/aeolian sand ratios, slurry concentrations (72%,74% and 76%), and inlet velocities (1.15,1.24 and 1.33 m/s). The results show that the pipeline resistance loss is directly proportional to the slurry concentration and inlet velocity, and inversely proportional to the coal gangue/aeolian sand, which indicates that the proportion of aeolian sand should be reduced in filling project to reduce the pipeline transportation resistance.

In the past 20 years, several magnetically controlled capsule endoscopes (MCCE) have been developed for the evaluation of gastric lesions, including NaviCam (ANKON), MiroCam-Navi (Intromedic), Endocapsule MGCE (Olympus and Siemens), SMCE (JIFU), and FAMCE (Jinshan). Although limited to observing esophageal and duodenal lesions and lacking the ability of biopsy, MCCE has the advantages of comfort, safety, no anesthesia, no risk of cross-infection, and high acceptability. Several high-quality RCTs showed that the diagnostic accuracy of MCCE is comparable to the traditional gastroscopy. Due to the nonnecessity of anesthesia, MCCE may be more suitable for the elderly with obvious comorbidities as well as children. With more evidences accumulated and more innovative technologies developed, MCCE is expected to be an important tool for screening of early gastric cancer or the diagnosis of gastric diseases.

The shear strength characteristics of rock materials, specifically internal friction angle and cohesion, are critical parameters for the design of rock structures. Accurate strength prediction can significantly reduce design time and costs while minimizing material waste associated with extensive physical testing. This paper utilizes experimental data from rock samples in the Himalayas to develop a novel machine learning model that combines the improved sparrow search algorithm (ISSA) with Extreme Gradient Boosting (XGBoost), referred to as the ISSA-XGBoost model, for predicting the shear strength characteristics of rock materials. To train and validate the proposed model, a dataset comprising 199 rock measurements and six input variables was employed. The ISSA-XGBoost model was benchmarked against other models, and feature importance analysis was conducted. The results demonstrate that the ISSA-XGBoost model outperforms the alternatives in both training and test datasets, showcasing superior predictive accuracy (R² = 0.982 for cohesion and R² = 0.932 for internal friction angle). Feature importance analysis revealed that uniaxial compressive strength has the greatest influence on cohesion, followed by P-wave velocity, while density exerts the most significant impact on internal friction angle, also followed by P-wave velocity.

The aerodynamic drag on a train running in an evacuated tube varies with tube air pressure, train speed and shape, as well as blockage ratio. This paper uses numerical simulations to study the effects of different factors on the aerodynamic drag of a train running at subsonic speed in an evacuated tube. Firstly, we present the assumption of a steady state, two dimensional, incompressible viscous flow with lubricity wall conditions. Subsequently, based on the Navier-Stokes equation and the k-[straight epsilon] turbulent models, we calculate the aerodynamic drag imposed on the column train with a 3-meter diameter running under different pressure and blockage ratio conditions in an evacuated tube transportation (ETT) system. The simulation is performed with FLUENT 6.3 software package. An analyses of the simulation results suggest that the blockage ratio for ETT should be in the range of 0.25-0.7, and the tube internal diameter in the range of 2-4 m, with the feasible vacuum pressure in the range of 1-10 000 Pa for the future subsonic ETT trains.

In order to investigate the mechanical properties of basalt fiber-doped tailing sand cemented filler and the evolution of energy damage, a uniaxial compression test was carried out on the basalt fiber-doped tailing sand cemented filler specimens to analyze the energy dissipation characteristics, and the damage constitutive equations with different basalt fiber contents were established based on damage mechanics. The results show that with the increase of fiber doping and fiber length, the uniaxial compressive strength and ductility of the filling body show a trend of increasing and then decreasing; the optimal value of fiber doping is 0.6%, and the optimal value of fiber length is 9 mm; the total strain energy, elastic strain energy and dissipation energy of basalt fiber-modified tailing sand cemented filling body at peak stress show a trend of increasing and then decreasing, and the energy dissipation energy of the filling body shows a trend of increasing and then decreasing. The energy dissipation energy shows a trend of increasing and then decreasing, and the energy dissipation energy shows a trend of increasing and then decreasing. The total strain energy, elastic strain energy, and dissipation energy at the peak stress show a trend of decreasing after increasing with the fiber doping and fiber length, and the energy damage evolution process can be divided into four stages: no damage stage, stable damage development stage, accelerated damage growth stage, and damage destruction; in addition, the existing damage constitutive model of the fiber-filled body was optimized, and the damage correction factor was introduced to obtain the damage constitutive model of the filled body with different fiber contents, and finally, after the verification of experimental and theoretical models, it was found that the two stress–strain curves coincided well. Finally, after the test and theoretical model verification, it is found that the stress–strain curves of the two are in good agreement, which indicates that the established theoretical model has a certain reference value for engineering practice, and at the same time, it has certain limitations.

Traditional slope stability analysis methods are time-consuming, complex, and cannot provide fast stability estimates when facing a large amount of slope cases. In this case, artificial neural networks (ANN) provide a better alternative. Based on the ANN, the particle swarm optimization (PSO) algorithm, and the principal component analysis (PCA) method, a novel PCA-PANN model is proposed. Then, a dataset of 307 slope cases covering a wide range of slope geometries and mechanical properties of geomaterial is developed. The hybrid machine learning model trained with the dataset is applied to the factor of safety (FoS) prediction of the actual slope, and three evaluation indicators are introduced to measure the prediction performance of the model. Finally, the sensitivity analysis of input parameters is carried out, and the slope protection strategy for different sensitive factors is proposed. The results show that this new model can quickly obtain the FoS and stable state of the slope without complex calculation, only by providing the relevant characteristic parameters. The correlation coefficient of the PCA-PANN model for slope stability analysis reaches more than 0.97. The sensitivity degree of influencing factors from large to small is slope angle, cohesion, pore pressure ratio, slope height, unit weight, and friction angle.

To reduce the disasters caused by slope instability, this paper proposes a new machine learning (ML) model for slope stability prediction. This improved SVR model uses support vector machine regression (SVR) as the basic prediction tool and the grid search method with 5-fold cross-validation to optimize the hyperparameters to improve the prediction performance. Six features, namely, unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio, were taken as the input of the model, and the factor of safety was taken as the model output. Four statistical indicators, namely, the coefficient of determination (R2), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE), were introduced to assess the generalization performance of the model. Finally, the feature importance score of the features was clarified by calculating the importance of the six features and visualizing them. The results show that the model can well describe the nonlinear relationship between features and the factor of safety. The R2, MAPE, MAE, and RMSE of the testing dataset were 0.901, 7.41%, 0.082, and 0.133, respectively. Compared with other ML models, the improved SVR model had a better effect. The most sensitive feature was unit weight.

The construction industry faces urgent challenges in reducing its carbon footprint, particularly in geotechnical engineering where conventional methods often involve high-emission materials. Artificial Ground Freezing (AGF) presents a sustainable, material-saving alternative for stabilizing water-rich strata, but its efficiency relies on accurate characterization of frozen soil behavior under in situ conditions. This study advances the understanding of AGF’s sustainability by investigating the directional shear behavior of pressurized frozen saturated medium sand (Fujian ISO standard sand) at −10 °C using a novel hollow cylinder apparatus. Through systematic testing under varying mean principal stresses (p = 0.5–6 MPa) with fixed intermediate principal stress coefficient (b = 0.5) and principal stress direction (α = 30°), we demonstrate that pressurized freezing creates a fundamentally different soil–ice composite compared to conventional unpressurized freezing. Key findings reveal (1) a linear strength increase described by the failure criterion qf = 1.17p + 3.77 (R[sup.2] = 0.98) without pressure melting effects within the tested range; (2) a distinct brittle-to-ductile transition at p ≈ 4 MPa, with associated failure mode changes from localized shear bands to homogeneous plastic flow; (3) a stable peak stress ratio (q/p ≈ 1.8) for p ≥ 4 MPa. These findings enable more reliable and potentially less conservative frozen wall design, directly contributing to reduced energy consumption in AGF operations. The research provides mechanical insights and practical parameters that enhance AGF’s viability as a low-carbon ground stabilization technology, supporting the construction industry’s transition toward sustainable underground development.

This study investigates the critical issue of water‐bearing sand layer frozen walls in deep artificial ground freezing (AGF) projects subjected to complex three‐dimensional (3D) stress states. The influence of the intermediate principal stress coefficient b (0–1) on the directional shear behavior (enabled by the independent control of the major principal stress direction angle, α ) of high‐pressure frozen saturated medium sand was systematically examined under controlled conditions ( T = −10 ° C, mean principal stress p = 4 MPa, major principal stress direction angle α = 30 ° ), utilizing a self‐developed frozen soil hollow cylinder apparatus (FS‐HCA). Key findings reveal that (1) the peak generalized shear strength ( q f ) exhibits pronounced nonlinear attenuation with increasing b , accurately characterized by the quadratic function q f = −2.41 b 2 + 0.38 b + 10.31 (MPa) ( R 2 = 0.99). Strength diminishes from 10.31 MPa at b = 0 to 7.90 MPa at b = 1, representing a significant 23.38% reduction relative to the strength at b = 0. This significant strength reduction underscores the risk of overestimating frozen wall capacity in deep AGF projects if the intermediate principal stress effect is neglected. (2) Strain evolution demonstrates fundamental divergence: axial strain ( ε z ) transitions from compression ( b ≤ 0.5) to tension ( b ≥ 0.75), reaching 0.37% tensile strain at b = 1, whereas peak torsional shear strain () attenuates linearly from 17.80% to 7.86% (a 55.8% reduction) governed by . (3) Failure modes undergo critical transitions: compressive‐torsional composite failure without distinct shear bands ( b ≤ 0.25), single primary shear band formation ( b = 0.5), and development of primary–secondary conjugate shear bands ( b > 0.5). The primary shear band dip angle ( β ) correlates linearly with b ( β = 12.33 b + 32 ( R 2 = 0.98)). (4) An extended spatially mobilized plane (SMP) strength criterion, incorporating temperature‐dependent ice cementation effects via principal stress translation, is proposed. This novel criterion accurately characterizes the convex triangular failure envelope in the π ‐plane, demonstrating exceptional predictive accuracy (error < 3%). This research elucidates the mechanistic control of intermediate principal stress on macroscopic strength attenuation and furnishes a robust theoretical framework for the 3D strength design of deep frozen walls.

The accurate prediction of joint shear strength is crucial for rock mass engineering design and geological hazard assessment. However, traditional machine learning (ML) models often suffer from local optima and limited generalization ability when dealing with complex nonlinear problems, thereby compromising prediction accuracy and stability. To address these challenges, this study proposes a hybrid ML model that integrates a multilayer perceptron (MLP) with the slime mold algorithm (SMA), termed the SMA-MLP model. While MLP exhibits strong nonlinear mapping capability, SMA enhances its training process through global optimization and parameter tuning, thereby improving predictive accuracy and robustness. A dataset with five input variables was constructed to evaluate the performance of the SMA-MLP model comprehensively. The proposed model was compared with other ML models. The results indicate that SMA-MLP outperforms these models in key metrics such as the root mean squared error (RMSE) and the correlation coefficient (R2), achieving an R2 of 0.97 and an RMSE as low as 0.10 MPa on the test set. Furthermore, feature importance analysis reveals that normal stress has the most significant influence on joint shear strength. This study demonstrates the superiority of SMA-MLP in predicting joint shear strength, highlighting its potential as an efficient and accurate tool for rock mass engineering analysis and providing reliable technical support for geological hazard assessment.