bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental and numerical investigation on soil erosion under different pipeline-leaking locations
Authors: Wang Z Y , Tan Y , Long Y Y 
Published: May 2024

Abstract
In recent years, cave-in ground failures have become increasingly common worldwide. The leakage of defective pipelines buried below ground level has been identified as a significant contributor to these failures. This study utilized experimental seepage-erosion tests to characterize ground collapses resulting from pipeline leakage and examined the impacts of leakage locations on soil and water losses. Subsequently, discrete element method-finite difference method (DEM-FDM) coupling analyses were conducted to elucidate the mesoscopic mechanisms of pipeline leakage, including changes in seepage force, effective stress, and soil pressure on the pipeline during leakage. The findings include: (1) The locations of leaking spots on pipelines significantly influenced soil and water losses; (2) Leaking spots closer to the pipe bottom led to more severe ground collapses due to larger seepage forces; (3) Soil pressure exhibited decreasing trends near the leaking spot and increasing trends at other locations due to the soil arching effect. These insights provide valuable guidance for preventing pipeline leakages.
GIS-Based assessment of geological hazard susceptibility in Southeastern Tibetan region: An information entropy approach
Authors: Pei Songqi , Zhao Cheng 
Published: May 2024

Abstract
The terrain and geological conditions in southeastern Tibetan region are complex, especially under the influence of hydraulic and hydroelectric development, and susceptibility to geological hazards has increased. This study focused on three counties in Changdu City. We aimed to assess the susceptibility to geological hazards in this region using Geographic Information System (GIS) technology and information entropy models. Historical geological hazard data from the local area, remote sensing data, and GIS were utilized to develop an information entropy model for landslide geological hazard analysis. Key triggering factors for landslide geological hazards in the southeastern Tibetan region were identified, and a comprehensive dataset was compiled. Information entropy model was applied to consider various influencing factors and calculate their impact on geological hazards, Landslide risk in the area was analyzed and generate a susceptibility map for geological hazards. Our results indicate significant correlations between factors, such as steep terrain, human engineering activities, and land types with landslide occurrences. The established evaluation model can reasonably predict potential geological hazard risk in Nujiang River region and partially reflect the distribution characteristics of geological hazards in the southeastern Tibetan region. This offers a scientific foundation for relevant authorities to formulate disaster prevention and response strategies.
Influence of pre-seismic history on the cyclic response of seasonal frozen silty sand
Authors: Fu Hongjian , Ni Xueqian , Zhang Sheng 
Published: May 2024

Abstract
The implementation of the Belt and Road strategy has increased construction activities in regions with seasonally frozen soil, such as the Qinghai–Tibet plateau. Numerous infrastructure systems in the region are built on seasonally frozen soil, rendering them susceptible to high-frequency seismic loads. Previous studies have demonstrated the significant influence of pre-seismic loads on the mechanical responses of soils. However, research on the influence of pre-seismic loads on seasonally frozen soil is limited. Therefore, two groups of cyclic triaxial tests are conducted in this study to investigate the impact of pre-seismic load and freeze-thaw history on the cyclic response of silty sand. Sample reconstitution and freeze-thaw procedures are described in detail. The test results indicate that the cyclic strength of silty sand decreases with the freeze-thaw history, although the influence diminishes with an increase in the number of freeze-thaw cycles. Additionally, a small pre-seismic load enhances the silty-sand strength, regardless of the freeze-thaw history it underwent. The findings of this study offer valuable insights for engineering construction in areas prone to seasonal freezing and earthquakes.
Machine Learning Models for Predicting Shear Wave Velocity of Soils
Authors: Mousavi Z. , Bayat M. , Feng W. 
Published: May 2024

Abstract
As regards regions prone to seismic activity, shear wave velocity (Vs) is a design parameter for geotechnical systems exposed to dynamic loads. Evaluating Vs
for geomaterials involves on-site and laboratory assessments; however, its availability is often limited in projects owing to resource and time constraints. Various mathematical and empirical models have been proposed to predict Vs for cohesive or granular soils; however, a majority of these models are specific to certain soil types and loading conditions. In this study, machine learning techniques were used for Vs
prediction. These models encompass factors such as grading attributes, void ratio (e), mean effective confining pressure (σ’m), consolidation stress ratio (KC), and specimen preparation methods. To achieve this, a series of bender element tests was performed on various sand and gravel mixtures supplemented with culled data from earlier investigations. This study facilitated the development of three machine learning models aimed at predicting the Vs for granular soils: artificial neural networks (ANN), support vector regression (SVR), and gradient boosting regression (GBR), aimed at predicting Vs
for granular soils. The findings of the study demonstrated that the ANN model exhibited enhanced precision and reduced error compared with the other models.
Predicting the shear modulus and damping ratio of soils using machine learning models
Authors: Bayat M , Mousavi Z , Feng W W 
Published: May 2024

Abstract
In geotechnical engineering, the accurate estimation of fundamental soil properties, such as the shear modulus ratio (G/G
max) and damping ratio (D), is crucial to design and analyze various structures subjected to dynamic loads. This study presents a comprehensive investigation on harnessing the power of machine learning techniques to precisely predict G/G
max and D of granular soils. Using an extensive dataset gathered from cyclic triaxial and resonant column tests on diverse mixtures of sand and gravel, combined with previous research findings, a series of advanced machine learning algorithms including shallow neural networks, support vector regression, gradient boosting regression, and deep feed forward neural network (DFFNN) were developed. The proposed models elucidate various influential parameters, including the grading characteristics, void ratio, confining pressure, consolidation stress ratio, and specimen preparation techniques. The superiority of the DFFNN model in terms of accuracy and predictive performance was demonstrated through rigorous evaluation and comparison. This study contributes to a better understanding of soil behavior under dynamic conditions. It also provides a robust framework to employ machine learning in predicting G/G
max and D of granular soils, thereby enhancing the efficiency and reliability of geotechnical designs and construction practices.
Natural frequency of offshore wind turbines on monopiles in sand
Authors: Zhang Chenrong , Wang Ruilai , Wang Zhenyu 
Published: May 2024

Abstract
Offshore wind turbines are important for developing sustainable alternative energy sources. Assessing their natural frequencies is a major challenge since resonance can cause damage and even failure, especially for long-term dynamic behavior owing to environmental loads. Several model tests were conducted to understand the long-term dynamic responses of monopile-supported wind turbines buried in sand. An electrodynamic actuator was used to obtain the resonant responses of the structure and a cyclic loading device based on eccentric masses was used to apply long-term cyclic loading. The development of the natural frequency under long-term cyclic loading was investigated for two relative sand densities, three cyclic load amplitudes, and four cyclic numbers. Based on the test results, the frequency increased with increased cyclic number owing to the densification of sand. A larger amplitude results in a larger frequency for medium-dense sand, whereas the opposite tendency exists for dense sand; this is attributed to the reduced embedded depth of the monopile in dense sand accompanied by the subsidence of sand. A non-dimensional framework was used to interpret the change in the natural frequency when subjected to long-term cyclic loads. The empirical equation with the non-dimensional parameter provides an assessment of the long-term natural frequency.
Machine learning ground motion models for a critical site with long-term earthquake records
Authors: Zhan W W , Chen Q S 
Published: May 2024

Abstract
Ground motion models derived using data from widespread stations and earthquakes induce significant aleatory variability when applied to predict ground motions for a single site. This issue can be even worse when the sites of interest show complex site response characteristics and do not have enough similar sites in the ground motion model development dataset. This work provides a machine-learning-based ground motion modeling framework for sites with numerous earthquake recordings. The Onahama Port Array, a liquefiable site in Japan that has recorded more than 1200 earthquakes in the last 25 years, was selected as a case study. The gradient boosting method was employed to predict response spectra with periods from 0.01 to 10 s for earthquakes with Japan Meteorological Agency (JMA) magnitude ranging from 2.4 to 9.0. Different predictor combinations were tested, and two gradient boosting models were recommended: the basic model that requires earthquake magnitude and epicentral distance as inputs, and the optimal model that requires three additional inputs, focal depth, azimuth, and rainfall. The basic and optimal gradient boosting models have the root mean square error of 0.004 and 0.001 g, average r2 of 5-fold cross validation of 0.915 and 0.983, respectively. The results shed light on seismic hazard assessment for critical sites with long-term earthquake recordings.
Pore pressure development model of fiber-reinforced calcareous sand based on shear strain characteristics
Authors: Zhou Lin , Chen Jian-Feng , Li Xiao-Jun , Zhu Yan 
Published: May 2024

Abstract
In this study, the pore pressure buildup characteristics of fiber-reinforced calcareous sand were investigated by examining the influences of fiber contents and fiber length through a series of cyclic simple shear tests. The test results indicate that the generation mechanisms of pore pressure ratio and shear strain of fiber-reinforced calcareous sand are interrelated under cyclic loading. The fiber content and fiber length have a significant influence on the relationship of pore pressure ratio versus number of cycles. Nevertheless, the correlation of pore pressure ratio versus shear strain is independent of fiber content and fiber length. According to the unique relationship, a pore pressure development model based on shear strain was established, exhibiting excellent predictive accuracy in simulating the pore pressure generation of fiber-reinforced calcareous sand with various fiber content and fiber lengths under cyclic loading. Moreover, the proposed model is also applicable to clean calcareous sand and siliceous sand.
Effects of freeze-thaw cycles on the tensile properties of geogrids and shear behavior of geogrid-soil interface
Authors: Meng Ya , Xu Chao 
Published: May 2024

Abstract
The tensile strength of geogrids and the shear strength of the reinforced soil interface are critical technical indicators for designing reinforced soil structures. This study developed a temperature-controlled apparatus to examine the tensile properties of geogrids subjected to freeze-thaw cycles within the soil, and the effect of interface temperature and freeze-thaw cycles on the shear behavior of the geogrid-soil interface. Eight and four groups of direct shear and tensile tests were conducted on the geogrid-soil interface and geogrids, respectively, under varying conditions. The peak tensile strength of geogrids remained similar to that of non-freeze-thaw samples after experiencing freeze–thaw cycles within the soil. tensile properties of geogrids were independent of the number of freeze–thaw cycles. At constant water content (w=6%), the peak shear stress at the geogrid-soil interface decreased by 24% (7 freeze-thaw cycles) compared to the non-freeze-thaw group. The cohesion and friction angle of the geogrid-soil interface decreased with increasing freeze-thaw cycles but tended to stabilize after 4 freeze-thaw cycles. The reinforcement provided by the geogrid enhanced with decrease in the interface temperature. The shear stress was higher at interface temperature under 0 °C, whereas in a non-frozen state, the interface exhibited lower shear stress with a consistent stable value.
Hydraulic conductivity of bentonite–polymer geosynthetic clay liners subject to wet–dry cycles used in landfill cover systems
Authors: Zhao Hanrui , Stark Benjamin M , Tian Kuo 
Published: May 2024

Abstract
This study evaluated the hydraulic conductivity of bentonite–polymer (B-P) geosynthetic clay liners (GCLs) subjected to wet-dry cycles that simulated seasonal conditions on a GCL installed in a landfill cover system. Hydraulic conductivity tests were conducted on B-P GCLs and NaB GCLs at 10 kPa in accordance with the ASTM D5084 standard. Two wet–dry methods were used to investigate the hydraulic conductivity of the GCLs under different field exposure conditions. The hydraulic conductivities of the NaB and B-P GCLs gradually increased during four wet–dry cycles. However, the hydraulic conductivity of the B-P GCL (K = 2.3 × 10-12 m/s) was lower than that of the NaB GCL (K = 6.3 × 10-11 m/s) by two orders of magnitude after four permeation–drying cycles. In addition, the hydraulic conductivity of the B-P GCL was lower (K = 5.8 × 10-12 m/s) than that of the NaB GCL (K = 7.8 × 10-10 m/s) by two orders of magnitude after four saturation–drying cycles. The results suggest that B-P GCLs can enhance the effectiveness of landfill cover systems compared to Na-B GCLs in managing conditions during wet and dry seasons.
Simulation of microbial transport and petroleum hydrocarbon compound biodegradation in a fracture–matrix coupled system
Authors: Li J , Feng S J , Zheng Q T 
Published: May 2024

Abstract
In-situ bioremediation technology is considered an ideal and environment-friendly approach for the largescale treatment of petroleum-contaminated soil. Most earlier studies on bioremediation have focused on the biostimulation techniques, which can enhance the biodegradation capabilities of indigenous bacteria, rather than on bioaugmentation involving the introduction of exogenous microorganisms. Additionally, contaminated soil sites typically contain both natural and artificial fractures. These fractures serve as preferential transport pathways, complicating the transport of bacteria and related solutes. This study proposes a mathematical model to simulate the transport of exogenous bacteria and electron acceptors during the degradation of petroleum hydrocarbon pollutants in a fractured aquifer system. The model incorporates the processes such as microbial convection, diffusion, adsorption, chemotaxis, growth, death, and microbial metabolic reactions. Simulation results demonstrate that considering microbial transport significantly affects the prediction of contaminant degradation, particularly with regard to the transport of electron acceptors. The growth rate of bacteria has an evident effect on the efficiency of contaminant remediation, and microbial chemotaxis can significantly enhance the degradation efficiency. The proposed mathematical model serves as a valuable tool for assessing the effectiveness of bioaugmentation strategies and can aid in the design and optimization of remediation efforts for contaminated and fractured aquifers.
Effect of fracture distribution on extraction efficiency in low-permeability NAPL-contaminated formations
Authors: Niu Jiuge , Feng Shijin , Zheng Qiteng , Yang Chunbaixue 
Published: May 2024

Abstract
Hydraulic fracturing offers an effective solution to the challenge of remediating low-permeability contaminated formations by improving substance transport rates. In this study, COMSOL Multiphysics, a large-scale multi-physics coupled simulation software, was employed to simulate enhanced extraction and remediation of low permeability non-aqueous phase liquid (NAPL)-contaminated formations using hydraulic fractures. The results obtained indicate that fracturing and enhanced extraction can effectively improve the NAPL removal rate. The NAPL removal rate increased with increasing depth of the extraction well screens, fracture length, and fracture spacing. Through a coupling analysis of multiple factors and working conditions, the extraction well screens are suggested to be buried within the lower one-third of the low-permeability contaminated formation, and the length of a single fracture should be within 50%–80% of the extraction radius. When the distance between the two fractures was greater than 1.75 m, the enhancement effect of the fracture length was further promoted. Consequently, a reasonable configuration of fracture length, quantity, and spacing can effectively broaden the influence radius of extraction wells.
Physical and chemical properties of aluminum tailings mud modified by metal ions
Authors: Deng X , Ma B , Du B 
Published: May 2024

Abstract
Aluminum tailings mud, a type of waste generated by bauxite washing during alumina production, is harmful. Owing to the isomorphism substitution of iron-aluminum symbiotic minerals, a large number of metal ions inevitably exist in the slurry, which causes the suspension to maintain dispersion and hinders the drainage of aluminum tailings mud. This study examined the effects of the pH and commonly associated metal ions, such as K+ and Fe3+, on the sedimentation characteristics of aluminum tailings mud. Zeta potential analyzer, X-ray fluorescence spectrum analysis, and electron scanning microscopy were used to study the effects of common metal ions in the tailings slurry on the sedimentation characteristics of aluminum tailings mud. The results showed that the sedimentation effect of the tailings slurry was superior after adding Fe3+ compared with K+. The mechanism analysis shows that at a certain pH range, the addition of metal ions can compress the thickness of the electric double layer on the surface. The high-valence ions (Fe3+) will have more electric double layer compression than monovalent ions (Na+), which is more conducive to the agglomeration of aluminum tailing mud particles. This conclusion provides guidance for efficient drainage of aluminum tailings mud.
Hydro-mechanical analysis of slope stability using anisotropic constitutive model
Authors: Song Fei , Duan Guanghui , Wang Huaning , Rodriguez-Dono Alfonso , Wei Yimiao 
Published: May 2024

Abstract
This study introduces a novel and robust numerical approach to analyzing rainfall-induced landslide hazards, incorporating coupled hydro-mechanical behavior, anisotropic properties of unsaturated geomaterials, and soil–atmosphere interaction. The advanced hyperbolic Mohr-Coulomb model with anisotropy is adopted for mechanical responses, while generalized Darcy’s law is employed for hydraulic behaviors. This approach allows for anisotropic specification of hydromechanical properties like stiffness, strength, and intrinsic permeability. Atmospheric boundary conditions enable analysis of soil–atmosphere interactions, encompassing infiltration and runoff during extreme rainfall. Parametric analyses were conducted to evaluate slope safety factors under various anisotropic angles.
Numerical analysis of stress and deformation characteristics of compressed air energy storage chambers developed from a modified coal mine tunnel
Authors: Chen Yanxi , Zhao Cheng , Qian Yuan , Sun Zeyuan , Xing Jinquan , Zhang Boyi 
Published: May 2024

AbstractThe use of abandoned coal mine tunnels as underground compressed air energy storage (CAES) facilities has garnered significant attention given that it effectively repurposes unused underground space and enhances the efficiency of renewable energy utilization. However, existing coal mine tunnels, without optimization and retrofitting, may not meet the stability requirements of compressed air energy storage repositories. Sectional transformation is a retrofitting method with excellent optimization effects. In this study, numerical simulation based on Midas software was employed to create a three-dimensional model of straight-wall arched mine tunnels. The stress and displacement deformation of the tunnel sections were simulated under internal pressure of 10 MPa, and scenarios with no modification and circular-section backfilling were compared. The outcomes of the study are (1) For deeply buried soft rock mines, the existing structure without modification may not meet the stability requirements of compressed air storage facility due to low rock strength. (2) In the unmodified scenario, stress concentration effects and excessive floor tensile stress occur near the tunnel closure. (3) Backfilling straight-wall arched tunnels into circular shapes effectively reduces stress and displacement along the tunnel walls, preventing stress concentration near the tunnel closure. This study provides a retrofitting approach for repurposing abandoned mines into compressed air energy storage facilities, providing guidance for future projects facing complex geological conditions.
Numerical modelling for optimizing shoring wall performance in deep urban excavations
Authors: Yasrebi Shahab , Zolqadr Emad 
Published: May 2024

Abstract
Soil nailing and anchoring are effective methods for reinforcing the soil in situ, providing internal support that enables the earth to sustain itself during and after excavation. To ensure optimal performance, it is essential to monitor the behavior of the soil nail walls throughout the excavation and after completion, so that the SOE system design can be improved. This study presents a comprehensive survey of the design and performance of soil nail walls in deep urban excavations at depths ranging from 19.5 to 29.3 m. After construction, the lateral deformations of the soil nail walls varied significantly from 69% to 222% of the initial deformations observed immediately after completion. Numerical analyses were used in the design process to accurately predict the wall performance and determine the design characteristics to identify appropriate design parameters and predict the wall behavior. Several numerical models using different modelling approaches are examined, and the results demonstrated that post-construction deformation rates can vary between walls based on factors such as the soil nail and anchor system design and geotechnical conditions at the excavation site. A thorough understanding of these variables is crucial for optimizing the performance of SOE systems in deep urban excavations.